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Mastering Control Strategies in AI: A Comprehensive Guide

Welcome to the world of artificial intelligence (AI), where machines not only perform tasks but also make decisions independently. At the heart of this autonomous capability lies a critical concept: Control Strategies in AI. These strategies act as the guiding lights for machines.

Just as a guide directs a technician through fixing a machine, control strategies steer AI systems through problem-solving tasks. Understanding these strategies is crucial to grasp the inner workings of AI. They serve as the plans for AI operations, ensuring efficiency and effectiveness in dealing with various challenges, similar to how rules guide operations in industries like making products or communication.

Understanding Control Strategies in AI

Without control strategies, AI would lack direction and purpose. Exploring these strategies reveals the mechanisms driving AI's problem-solving prowess. From making complex decisions to navigating uncertainties, control strategies empower AI to confront diverse scenarios with confidence.

Furthermore, these strategies evolve with advancements in AI research and technology, adapting to new challenges and opportunities. By constantly refining and optimizing control strategies, AI systems become more adept at handling complex tasks and uncertainties, pushing the boundaries of what they can achieve.

Join us on a journey to dive into the significance and mechanics of control strategies in AI, and witness how they enable machines to navigate the complexities of our world with precision and intelligence.

Types of Control Strategies in AI

Uninformed Search Strategies: Navigating Without Prior Knowledge

Uninformed search strategies in AI are methods of exploring problem spaces without any knowledge or specific guidance. These strategies involve systematically examining possible solutions without considering their potential effectiveness. Examples include Breadth-First Search (BFS), where all options are explored level by level, and Depth-First Search (DFS), which explores one path deeply before backtracking.

Real-Life Example (Web Crawling) : 

Search engines like Google use uninformed search strategies to systematically explore and index web pages across the internet. They traverse links without any knowledge of page content, similar to how Breadth-First Search and Depth-First Search explore possibilities without considering their relevance.

Breadth-First Search: Systematic Exploration

Breadth-First Search involves exploring all options systematically, similar to scanning every bookshelf in a library. It examines each possibility before moving on to the next, ensuring a thorough search.

Real-life Example: Social Network Friend Suggestions

Social media platforms like Facebook or LinkedIn use Breadth-First Search to suggest potential friends or connections based on mutual friends or interests. They explore connections methodically, suggesting relevant contacts.

Depth-First Search: Delving Deep for Solutions

The depth-first search focuses on exploring one path deeply before considering other options, similar to thoroughly searching through one area before moving on. It delves deeply into possibilities before exploring alternatives.

Real-life Example: Chess Game Analysis

Chess-playing algorithms use Depth-First Search to explore potential moves and outcomes. They analyse one line of play deeply, evaluating consequences before considering other moves.

Heuristic Search Strategies: Intelligent Exploration

Heuristic search strategies involve intelligent exploration techniques that use clues or hints to guide the search process. These methods prioritize options based on perceived promise or continuously adjust based on new information, allowing for efficient problem-solving in various domains.

Real-life Example: Navigation Apps

Navigation apps like Google Maps utilize heuristic search strategies to find optimal routes between locations. For instance, Greedy Search prioritizes roads based on distance or traffic conditions, ensuring users reach their destinations quickly. Meanwhile, Hill Climbing continuously adjusts routes based on real-time information, enabling efficient navigation without encountering traffic jams or delays.

Greedy Search: Favouring the Most Promising Path

Greedy Search is a heuristic search strategy that favours the most promising path at each step of the problem-solving process. It prioritises immediate gains without considering long-term consequences and making decisions solely based on the information available at the time.

Real-life Example: Shortest Path Algorithm

In the field of transportation logistics, the Shortest Path Algorithm utilizes Greedy Search to find the most efficient route between two points. For example, delivery companies use this algorithm to determine the quickest way to deliver packages by prioritising roads with the shortest distance, disregarding potential traffic congestion or other factors.

Hill Climbing: Reaching for the Top Without Falling

Hill Climbing is a heuristic search strategy that aims to reach the peak of a problem space without falling into local optima. Similar to ascending a hill, it continuously moves towards states with higher values based on the heuristic function, making incremental improvements until reaching the optimal solution.

Real-life Example: Machine Learning Optimization

In machine learning, Hill Climbing algorithms are used to optimise models by adjusting parameters to maximize performance metrics. For instance, in training neural networks, Hill Climbing iteratively adjusts weights and biases to improve accuracy or minimize errors, gradually climbing towards the optimal configuration for the given task.

Each control strategy in AI offers a distinct approach to problem-solving, whether through systematic exploration or intelligent decision-making. These strategies find applications in various real-world scenarios, from web crawling and social networking to game playing and route planning, demonstrating their importance in modern software techniques.

Approaches to Problem-Solving in AI

Imagine you have a computer program designed to figure things out, like finding the best route to take or making decisions in a game. This program needs to solve problems, just like we do in everyday life. 

Here’s how it works:

• Identifying the Problem : First, the program needs to understand what problem it's trying to solve. This could be anything from finding the shortest path between two points on a map to deciding the best move in a game.  
• Finding Solutions : Once the problem is clear, the program needs to come up with possible solutions. It might try different strategies or approaches to see what works best.  
• Choosing the Best Solution : After generating potential solutions, the program needs to figure out which one is the most effective. It might use logic, maths, or other techniques to evaluate each option and pick the one that seems most promising.  
• Taking Action : Finally, the program puts its chosen solution into action. This could mean making a move in a game, adjusting a route, or taking some other kind of action to achieve the desired outcome.

In AI, there are different ways to approach problem-solving. Some methods involve following a set of rules or algorithms step by step, while others use more flexible strategies like heuristics (rules of thumb) or modeling frameworks (ways of representing and analyzing data).

Overall, problem-solving in AI is about finding effective ways to achieve goals and make decisions, using a combination of logic, maths, and creativity. It's a fundamental aspect of artificial intelligence that helps computers tackle a wide range of tasks and challenges.

Initiating the Search: Core AI Concepts

Initiating the search in artificial intelligence (AI) is akin to setting out on a journey to solve a problem or accomplish a task. Just like embarking on an adventure with a clear destination in mind, the AI begins its quest with a starting point and a goal to achieve. Here's how it works:

• Starting Point : The AI begins with a problem to solve or a task to accomplish. This could be anything from finding the best route on a map to diagnosing a medical condition.  
• Goal : The AI has a clear objective or goal it wants to achieve. For example, finding the shortest route or identifying the correct diagnosis.  
• Options : The AI considers different actions or steps it can take to move closer to the goal. These actions could involve exploring different paths, making decisions, or applying specific algorithms.  
• Exploring : The AI starts exploring these options systematically. It tries one action at a time and evaluates its effectiveness in reaching the goal.  
• Decision Making : Based on the results of each action, the AI makes decisions about what to try next. It might prioritize actions that seem more promising or discard those that don't lead to progress.  
• Adjusting : If the AI encounters obstacles or dead ends, it adjusts its approach. This could involve backtracking, trying different strategies, or refining its search criteria.  
• Reaching the Goal : Through a process of exploration, decision-making, and adjustment, the AI eventually reaches the goal or finds a satisfactory solution to the problem.  

Initiating the search involves navigating through the problem space, making decisions, and adapting the approach as needed to ultimately achieve the desired outcome.

Tools for Problem-Solving: Frameworks in AI

In artificial intelligence (AI), frameworks stand as essential tools for problem-solving. These frameworks provide structured methodologies to tackle challenges efficiently. Think of them as organized toolkits offering clear guidance throughout problem-solving processes. They serve as systematic approaches, guiding users step by step, much like a roadmap. Central to these frameworks are algorithms, which serve as detailed instructions for decision-making. Despite their structured nature, frameworks allow for adaptability to different scenarios, ensuring versatility in problem-solving within the AI landscape. These frameworks play a crucial role in enhancing problem-solving capabilities and driving efficiency and effectiveness in AI applications.

Essential Search Strategies for AI Success

In the domain of healthcare, hospitals are increasingly turning to artificial intelligence (AI) to enhance patient care. Consider a scenario where a hospital aims to address the challenge of high misdiagnosis rates by leveraging AI for medical diagnosis.

• Identifying the Challenge : The hospital recognizes the complexity of diagnosing medical conditions due to the vast array of symptoms and patient data. They seek to develop an AI system capable of accurately analyzing patient information to identify diseases more precisely.  
• Selecting the Right Approach : Data scientists at the hospital carefully choose an appropriate AI algorithm, such as a machine learning model, tailored to analyze patient symptoms and medical history effectively.  
• Systematic Analysis : The AI system meticulously examines patient data, including symptoms, test results, and medical records. Through this process, it adapts its methods based on the diverse range of cases encountered.  
• Continuous Improvement : With each iteration, the AI system learns and refines its diagnostic capabilities. It incorporates feedback from healthcare professionals, ensuring continuous optimization of its algorithms for better accuracy.  
• Persistence in the Face of Challenges : Despite encountering complexities like ambiguous symptoms or rare diseases, the AI system remains steadfast in its pursuit of accurate diagnoses. It perseveres, tirelessly analyzing data and seeking expert insights to deliver reliable results.

By employing these essential search strategies, the hospital's AI system advances medical diagnosis, ultimately leading to improved patient outcomes and enhanced healthcare delivery.

Selecting the Right Control Strategy

Assessing fit: factors for choosing strategies.

Selecting the right control strategy is like choosing the best approach or method to solve a problem in artificial intelligence (AI). Just as you'd pick the right tool for a job, AI systems need to use the most suitable control strategy to navigate through challenges effectively. Let's explore these all one by one:

Understanding Problem Complexity

Understanding problem complexity in AI is like figuring out how hard a problem is for artificial intelligence to solve. Just like some tasks are easier than others, some AI problems are more challenging because they need more thinking and resources to solve.

• Example : Think about sorting toys into different boxes. If there are only a few toys and they're all different colours, it's a simple problem because it's easy to sort them quickly. But if there are many toys with similar colours and shapes, it becomes more complex because it takes more time and effort to sort them correctly.  
• Importance : Why does this matter? Well, understanding how complex a problem is helps AI developers pick the right tools and methods. If a problem is simple, basic techniques might work fine. But if it's complex, they might need fancier algorithms or more computer power to solve it effectively.

Using Field Expertise to Your Advantage

Using field expertise in AI means tapping into specialised knowledge or experience related to the problem domain. As doctors rely on medical expertise to diagnose illnesses, AI systems can benefit from insights and understanding from experts in the relevant field.

• Example : Suppose you're developing an AI system to assist doctors in diagnosing diseases from medical images. By consulting radiologists and medical professionals, you can gain valuable insights into the nuances of interpreting images and identifying abnormalities, improving the accuracy and reliability of the AI system.  
• Importance : Incorporating field expertise enhances the effectiveness and relevance of AI solutions. Experts provide valuable input on data interpretation, problem-solving strategies, and real-world implications, leading to more robust and reliable AI systems.

Balancing Quality and Efficiency of Solutions

Balancing quality and efficiency in AI involves achieving the best possible outcomes while minimizing resource usage and time. Just as chefs strive to create delicious meals efficiently, AI developers aim to produce accurate and effective solutions within reasonable time and computational constraints.

• Example : Consider developing a recommendation system for an e-commerce website. While it's important to recommend products that match customer's preferences accurately (quality), it's also crucial to generate recommendations quickly to enhance user experience (efficiency).  
• Importance : Finding the right balance between quality and efficiency ensures that AI solutions meet both performance and practicality requirements. It involves optimisation algorithms, managing computational resources effectively and prioritising features that contribute most to desired outcomes.

Understanding problem complexity helps customise AI solutions to specific challenges, utilising field expertise enhances solution relevance and accuracy, and balancing quality and efficiency ensures optimal performance within resource constraints. By considering these factors, AI developers can design solutions that effectively address real-world problems while maximizing impact and efficiency.

Real-World Applications of Control Strategies in AI

Control strategies in AI aren't just theoretical concepts; they have practical applications in various fields, from everyday tasks to complex problem-solving scenarios. Let's explore some real-world examples to understand how these strategies are used:

Navigating Robots: Control Strategies in Robot Navigation

Navigating Robots involves using control strategies to guide robots through various environments or tasks. Just like a GPS helps us find the best route, control strategies help robots move efficiently and effectively.

Control Strategies : Robots use different control strategies in AI to navigate, such as:

• Rule-based navigation : Following predefined rules or instructions.
• Path planning algorithms : Finding the optimal path from a starting point to a destination.
• Reactive navigation : Reacting to immediate sensory input to avoid obstacles or follow a target.

Control strategies are essential for robots to perform tasks autonomously, whether it's delivering packages in a warehouse, exploring unknown terrain, or assisting in search and rescue missions. These strategies ensure robots navigate safely and accomplish their objectives with precision and reliability.

Winning Strategies in Games: AI's Role in Game Development

Winning Strategies in Games involves using AI to develop intelligent game-playing agents capable of competing with or surpassing human players. Just as athletes strategize to win games, AI agents use sophisticated algorithms to excel in various game scenarios.

AI Techniques : Game developers employ AI techniques such as:

• Monte Carlo Tree Search : Planning optimal moves in complex decision trees.
• Deep Reinforcement Learning : Learning optimal strategies through trial and error.
• Heuristic Search : Evaluating game states based on predefined rules or heuristics.

AI plays a vital role in game development by creating challenging opponents, designing adaptive gameplay experiences, and enhancing player engagement. Winning strategies in games not only entertain players but also drive advancements in AI research and development.

Enhancing Machine Learning: Optimization through Control Strategies

Enhancing Machine Learning involves using control strategies to optimise machine learning models and algorithms. Just as chefs refine recipes to improve taste, control strategies refine machine learning processes to maximize performance.

Control Strategies : Machine learning optimization techniques include:

• Gradient Descent : Adjusting model parameters to minimize errors or maximize performance metrics.
• Genetic Algorithms : Evolving solutions through selection, crossover, and mutation processes.
• Hyperparameter Tuning : Optimising model parameters to improve generalisation and robustness.

Control strategies play a crucial role in enhancing machine learning by fine-tuning algorithms, improving accuracy, and accelerating convergence. These strategies enable machine learning models to adapt to diverse datasets, learn complex patterns, and make accurate predictions in various applications, from image recognition to natural language processing.

Navigating robots relies on control strategies to guide them through tasks efficiently, winning strategies in games use AI to create challenging opponents and engage gameplay, and enhancing machine learning involves optimising algorithms to improve performance and accuracy. These applications demonstrate the versatility and importance of control strategies in AI across different domains, driving advancements and innovations in technology and society.

Challenges and Future Directions

As we dive into the world of artificial intelligence and control strategies in AI, it's important to understand the problems we might face and where the field is headed in the future. Let's look at some of the main challenges and what could happen next:

Overcoming Implementation Challenges

Using artificial intelligence (AI) and control methods can have difficulties. These challenges might involve technical limits, lack of resources, and problems within the organisation. To overcome them, careful planning and execution are needed. Here are some ways to handle these challenges:

• Technical Expertise : Ensure that your team possesses the necessary technical skills to develop and deploy AI systems. Providing training and professional development opportunities can help bridge any knowledge gaps.  
• Data Quality and Availability : Address issues related to data Quality and Availability by Implementing Data Management best practices. This may involve data cleaning, preprocessing, and ensuring access to relevant data sources.  
• Infrastructure and Resources : Invest in the infrastructure and resources required for AI development and deployment. This may include cloud computing services, high-performance computing clusters, and specialised hardware.  
• Regulatory Compliance : Stay abreast of regulatory requirements and ensure that your AI systems comply with relevant laws and regulations. This may involve consulting legal experts and implementing robust data privacy and security measures.  
• Change Management : Manage organisational change effectively by fostering a culture of innovation and collaboration. Communicate the benefits of AI adoption to stakeholders and involve them in the decision-making process.

By addressing these implementation challenges, organisations can unlock the full potential of AI and control strategies in AI to drive innovation and competitive advantage.

Best Practices for Resolving Issues

Resolving issues that arise during the implementation of AI and control strategies requires a systematic approach and adherence to best practices. Here are some recommended strategies for resolving issues effectively:

• Identify Root Causes : Conduct a thorough analysis to identify the underlying causes of issues or challenges. This may involve gathering input from stakeholders, conducting diagnostic tests, and leveraging data analytics tools.  
• Rank Solutions : Rank Solutions according to their ability to make a big difference and how achievable they are. Concentrate on fixing important problems that greatly affect project results or make stakeholders happy.  
• Collaborate Across Teams : Foster collaboration and communication across multidisciplinary teams involved in AI development and implementation. Encourage cross-functional teamwork to use diverse perspectives and expertise.  
• Iterative Problem-Solving : Embrace an iterative problem-solving approach that involves continuous learning and improvement. Break down complex issues into smaller, manageable tasks and iterate on solutions based on feedback and experimentation.  
• Documentation and Knowledge Sharing : Document lessons learned, best practices, and successful strategies for resolving issues. Share this knowledge with relevant stakeholders to facilitate learning and continuous improvement.

By adopting these best practices, organisations can effectively address issues and challenges encountered during the implementation of AI and control strategies, driving successful project outcomes and business results.

Future Outlook: Evolution of Control Strategies in AI

The development of control methods in AI is set to change the future of artificial intelligence and transform different industries. Here are some important trends and advancements influencing how control methods in AI will look in the future:

• Advanced Learning Techniques : Advances in machine learning and deep learning techniques will enable the development of more sophisticated control strategies. These techniques will facilitate autonomous decision-making and adaptive behaviour in AI systems.  
• Human and AI Collaboration : In the future, AI will work more closely with humans in different areas like healthcare, manufacturing, and customer service. We'll focus on improving how people and AI systems interact, making collaboration smoother.  
• Explainable AI : There will be a growing emphasis on developing explainable AI systems that can provide transparent explanations for their decisions and actions. Control strategies will prioritise interpretability and transparency to foster trust and accountability.  
• Ethical and Responsible AI : Control strategies will integrate ethical considerations and responsible AI principles into the design and implementation of AI systems. This will involve addressing biases, ensuring fairness, and mitigating unintended consequences of AI applications.  
• Edge Computing and IoT Integration : Control strategies will adapt to the increasing prevalence of edge computing and the Internet of Things (IoT). AI systems will use distributed computing architectures and real-time data processing capabilities to enable intelligent decision-making at the edge.  
• Cross-Domain Applications : Control strategies will transcend traditional boundaries and find applications across diverse domains, including healthcare, finance, agriculture, and smart cities. AI systems will offer tailored solutions to address domain-specific challenges and opportunities.

The future of control strategies in AI holds immense promise for driving innovation, enhancing productivity, and improving the quality of life. By embracing emerging trends and developments, organisations can harness the full potential of AI to address complex problems and achieve transformative outcomes.

Control strategies are fundamental for AI systems, guiding them through problem-solving tasks and enabling autonomous decision-making. They play a crucial role in ensuring AI operates efficiently and effectively, fostering seamless collaboration between machines and humans. These strategies are essential across diverse applications, whether it's guiding robots through dynamic environments or devising strategies in gaming scenarios. As AI technology advances, control strategies will continue to evolve, adapting to emerging challenges and opportunities in the field. 

For further exploration of AI tools and more blog posts, individuals can find valuable resources and information by visiting  Tooplate .

What is strategy in artificial intelligence?

A strategy in AI is a plan or method that guides AI systems in decision-making and problem-solving. It's like a roadmap for the AI, showing it what steps to take next. Strategies help AI systems tackle challenges effectively and achieve their goals efficiently.

What are control strategies used for?

Control strategies in AI are essential for guiding AI systems through problem-solving tasks. They provide a structured approach for decision-making, ensuring that the AI can navigate challenges effectively and make informed choices. Essentially, control strategies act as the guiding principles for AI systems, helping them achieve their objectives with precision.

What is the importance of control strategy in the production system in AI?

In AI production systems, control strategies are crucial for ensuring efficient and effective operation. They help optimise the performance of AI systems by guiding decision-making processes and problem-solving tasks. Without control strategies, AI systems may struggle to navigate challenges and achieve desired outcomes, impacting the overall productivity and success of AI applications in various industries.

What is the difference between control strategy and AI?

AI encompasses the broader field of creating intelligent machines, while control strategy is a specific aspect within AI that focuses on guiding decision-making processes. Essentially, AI is the entire framework or toolbox, while control strategy is one of the tools within that framework, aiding in decision-making and problem-solving tasks.

What is the role of control strategies in solving the problem?

Control strategies play a crucial role in problem-solving by providing AI systems with structured approaches to decision-making. They help the AI navigate complexities and uncertainties, ensuring efficient and effective solutions to various challenges. In essence, control strategies act as the backbone of problem-solving in AI, guiding the system towards optimal outcomes.

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Control Strategy and Requirements in Artificial Intelligence

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Production systems in artificial intelligence consist of:

1. A set of rules, “each consisting of a left side (a pattern) that determines the applicability of the rule and a right side that describes the operation to be performed if the rule is applied.”

For example:

In the Water Jug Problem , Rule (x, y) is x<4 ->(4, Y) indicates Fill the 4-gallon jug and (x, y) if y<3 -> (x, 3) indicates Fill the 3-gallon jug.

2. One or more knowledge/databases “that contain whatever information is appropriate for the particular task. Some plans of the database may be permanent, while other plans of it may pertain only to the solution of the current problem.”

3. A control strategy “that specifies the order in which the rules will be compared to the database and a way of resolving the conflicts that arise when several rules match at once.”

3. A rule applier.

Control Strategies

Given a problem definition, it is very difficult to decide which rule to choose from a set of rules which can solve the problem efficiently.

This question usually arises when we have more than one rule (and sometimes fewer than one rule) that will match with the left side of the current state. Even without a great deal of thought, it is clear that such decisions will have a crucial impact on how quickly. and even whether, a problem is finally solved.

There are mainly two requirements to choose a control strategy.

The first requirement of a good control strategy is that it causes motion .

Example: Consider the water jug problem. Suppose we implemented the simple control strategy of starting each time at the top of the list of rules and choosing the first applicable one. If we did that, we would never solve the problem. We would continue indefinitely filling the 4-gallon jug with water Control strategies that do not cause motion will never lead to a solution.

The second requirement of a good control strategy is that it be systematic.

Example: Consider the water jug problem. On each cycle, choose at random from among the applicable rules. This strategy is better than the first. It causes motion. It will lead to a solution eventually. But we are likely to arrive at the same state several times during the process and to use many more steps than are necessary. Because the control strategy is not systematic, we may explore a particular useless sequence of operators several times before we finally find a solution.

Search Strategies:

Search is the systematic examination of states to find paths from the start/root state to the goal state.

Many traditional search algorithms are used in AI applications. For complex problems, the traditional algorithms are unable to find the solution within some practical time and space limits. Consequently, many special techniques are developed; using heuristic functions. The algorithms that use heuristic functions are called heuristic algorithms. Heuristic algorithms are not really intelligent; they appear to be intelligent because they achieve better performance. Heuristic algorithms are more efficient because they take advantage of feedback from the data to direct the search path.

There are mainly two types of search strategies:

1. Uninformed search (blind search)

Also called blind, exhaustive or brute-force search uses no information about the problem to guide the search and therefore may not be very efficient.

2. Informed search (heuristic search)

Also called heuristic or intelligent search, uses information about the problem to guide the search, usually guesses the distance to a goal state, and is therefore efficient, but the search may not be always possible.

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Problem Solving in Artificial Intelligence

The reflex agent of AI directly maps states into action. Whenever these agents fail to operate in an environment where the state of mapping is too large and not easily performed by the agent, then the stated problem dissolves and sent to a problem-solving domain which breaks the large stored problem into the smaller storage area and resolves one by one. The final integrated action will be the desired outcomes.

On the basis of the problem and their working domain, different types of problem-solving agent defined and use at an atomic level without any internal state visible with a problem-solving algorithm. The problem-solving agent performs precisely by defining problems and several solutions. So we can say that problem solving is a part of artificial intelligence that encompasses a number of techniques such as a tree, B-tree, heuristic algorithms to solve a problem.  

We can also say that a problem-solving agent is a result-driven agent and always focuses on satisfying the goals.

There are basically three types of problem in artificial intelligence:

1. Ignorable: In which solution steps can be ignored.

2. Recoverable: In which solution steps can be undone.

3. Irrecoverable: Solution steps cannot be undo.

Steps problem-solving in AI: The problem of AI is directly associated with the nature of humans and their activities. So we need a number of finite steps to solve a problem which makes human easy works.

These are the following steps which require to solve a problem :

• Problem definition: Detailed specification of inputs and acceptable system solutions.
• Problem analysis: Analyse the problem thoroughly.
• Knowledge Representation: collect detailed information about the problem and define all possible techniques.
• Problem-solving: Selection of best techniques.

Components to formulate the associated problem: 

• Initial State: This state requires an initial state for the problem which starts the AI agent towards a specified goal. In this state new methods also initialize problem domain solving by a specific class.
• Action: This stage of problem formulation works with function with a specific class taken from the initial state and all possible actions done in this stage.
• Transition: This stage of problem formulation integrates the actual action done by the previous action stage and collects the final stage to forward it to their next stage.
• Goal test: This stage determines that the specified goal achieved by the integrated transition model or not, whenever the goal achieves stop the action and forward into the next stage to determines the cost to achieve the goal.  
• Path costing: This component of problem-solving numerical assigned what will be the cost to achieve the goal. It requires all hardware software and human working cost.

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Learn Creative Problem Solving Techniques to Stimulate Innovation in Your Organization

By Kate Eby | October 20, 2017 (updated August 27, 2021)

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In today’s competitive business landscape, organizations need processes in place to make strong, well-informed, and innovative decisions. Problem solving - in particular creative problem solving (CPS) - is a key skill in learning how to accurately identify problems and their causes, generate potential solutions, and evaluate all the possibilities to arrive at a strong corrective course of action. Every team in any organization, regardless of department or industry, needs to be effective, creative, and quick when solving problems. 

In this article, we’ll discuss traditional and creative problem solving, and define the steps, best practices, and common barriers associated. After that, we’ll provide helpful methods and tools to identify the cause(s) of problematic situations, so you can get to the root of the issue and start to generate solutions. Then, we offer nearly 20 creative problem solving techniques to implement at your organization, or even in your personal life. Along the way, experts weigh in on the importance of problem solving, and offer tips and tricks. 

What Is Problem Solving and Decision Making?

Problem solving is the process of working through every aspect of an issue or challenge to reach a solution. Decision making is choosing one of multiple proposed solutions  — therefore, this process also includes defining and evaluating all potential options. Decision making is often one step of the problem solving process, but the two concepts are distinct. 

Collective problem solving is problem solving that includes many different parties and bridges the knowledge of different groups. Collective problem solving is common in business problem solving because workplace decisions typically affect more than one person. 

Problem solving, especially in business, is a complicated science. Not only are business conflicts multifaceted, but they often involve different personalities, levels of authority, and group dynamics. In recent years, however, there has been a rise in psychology-driven problem solving techniques, especially for the workplace. In fact, the psychology of how people solve problems is now studied formally in academic disciplines such as psychology and cognitive science.

Joe Carella is the Assistant Dean for Executive Education at the University of Arizona . Joe has over 20 years of experience in helping executives and corporations in managing change and developing successful business strategies. His doctoral research and executive education engagements have seen him focus on corporate strategy, decision making and business performance with a variety of corporate clients including Hershey’s, Chevron, Fender Musical Instruments Corporation, Intel, DP World, Essilor, BBVA Compass Bank.

He explains some of the basic psychology behind problem solving: “When our brain is engaged in the process of solving problems, it is engaged in a series of steps where it processes and organizes the information it receives while developing new knowledge it uses in future steps. Creativity is embedded in this process by incorporating diverse inputs and/or new ways of organizing the information received.”

Laura MacLeod is a Professor of Social Group Work at City University of New York, and the creator of From The Inside Out Project® , a program that coaches managers in team leadership for a variety of workplaces. She has a background in social work and over two decades of experience as a union worker, and currently leads talks on conflict resolution, problem solving, and listening skills at conferences across the country. 

MacLeod thinks of problem solving as an integral practice of successful organizations. “Problem solving is a collaborative process — all voices are heard and connected, and resolution is reached by the group,” she says. “Problems and conflicts occur in all groups and teams in the workplace, but if leaders involve everyone in working through, they will foster cohesion, engagement, and buy in. Everybody wins.”

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What Is the First Step in Solving a Problem?

Although problem solving techniques vary procedurally, experts agree that the first step in solving a problem is defining the problem. Without a clear articulation of the problem at stake, it is impossible to analyze all the key factors and actors, generate possible solutions, and then evaluate them to pick the best option. 

Dr. Elliott Jaffa is a behavioral and management psychologist with over 25 years of problem solving training and management experience. “Start with defining the problem you want to solve,” he says, “And then define where you want to be, what you want to come away with.” He emphasizes these are the first steps in creating an actionable, clear solution. 

Bryan Mattimore is Co-Founder of Growth Engine, an 18-year old innovation agency based in Norwalk, CT. Bryan has facilitated over 1,000 ideation sessions and managed over 200 successful innovation projects leading to over $3 billion in new sales. His newest book is 21 Days to a Big Idea . When asked about the first critical component to successful problem solving, Mattimore says, “Defining the challenge correctly, or ‘solving the right problem’ … The three creative techniques we use to help our clients ‘identify the right problem to be solved’ are questioning assumptions, 20 questions, and problem redefinition. A good example of this was a new product challenge from a client to help them ‘invent a new iron. We got them to redefine the challenge as first: a) inventing new anti-wrinkle devices, and then b) inventing new garment care devices.”

What Are Problem Solving Skills?

To understand the necessary skills in problem solving, you should first understand the types of thinking often associated with strong decision making. Most problem solving techniques look for a balance between the following binaries:

• Convergent vs. Divergent Thinking: Convergent thinking is bringing together disparate information or ideas to determine a single best answer or solution. This thinking style values logic, speed, and accuracy, and leaves no chance for ambiguity. Divergent thinking is focused on generating new ideas to identify and evaluate multiple possible solutions, often uniting ideas in unexpected combinations. Divergent thinking is characterized by creativity, complexity, curiosity, flexibility, originality, and risk-taking.
• Pragmatics vs. Semantics: Pragmatics refer to the logic of the problem at hand, and semantics is how you interpret the problem to solve it. Both are important to yield the best possible solution.
• Mathematical vs. Personal Problem Solving: Mathematical problem solving involves logic (usually leading to a single correct answer), and is useful for problems that involve numbers or require an objective, clear-cut solution. However, many workplace problems also require personal problem solving, which includes interpersonal, collaborative, and emotional intuition and skills. 

The following basic methods are fundamental problem solving concepts. Implement them to help balance the above thinking models.

• Reproductive Thinking: Reproductive thinking uses past experience to solve a problem. However, be careful not to rely too heavily on past solutions, and to evaluate current problems individually, with their own factors and parameters. 
• Idea Generation: The process of generating many possible courses of action to identify a solution. This is most commonly a team exercise because putting everyone’s ideas on the table will yield the greatest number of potential solutions. 

However, many of the most critical problem solving skills are “soft” skills: personal and interpersonal understanding, intuitiveness, and strong listening. 

Mattimore expands on this idea: “The seven key skills to be an effective creative problem solver that I detail in my book Idea Stormers: How to Lead and Inspire Creative Breakthroughs are: 1) curiosity 2) openness 3) a willingness to embrace ambiguity 4) the ability to identify and transfer principles across categories and disciplines 5) the desire to search for integrity in ideas, 6) the ability to trust and exercise “knowingness” and 7) the ability to envision new worlds (think Dr. Seuss, Star Wars, Hunger Games, Harry Potter, etc.).”

“As an individual contributor to problem solving it is important to exercise our curiosity, questioning, and visioning abilities,” advises Carella. “As a facilitator it is essential to allow for diverse ideas to emerge, be able to synthesize and ‘translate’ other people’s thinking, and build an extensive network of available resources.”

MacLeod says the following interpersonal skills are necessary to effectively facilitate group problem solving: “The abilities to invite participation (hear all voices, encourage silent members), not take sides, manage dynamics between the monopolizer, the scapegoat, and the bully, and deal with conflict (not avoiding it or shutting down).” 

Furthermore, Jaffa explains that the skills of a strong problem solver aren’t measurable. The best way to become a creative problem solver, he says, is to do regular creative exercises that keep you sharp and force you to think outside the box. Carella echoes this sentiment: “Neuroscience tells us that creativity comes from creating novel neural paths. Allow a few minutes each day to exercise your brain with novel techniques and brain ‘tricks’ – read something new, drive to work via a different route, count backwards, smell a new fragrance, etc.”

What Is Creative Problem Solving? History, Evolution, and Core Principles

Creative problem solving (CPS) is a method of problem solving in which you approach a problem or challenge in an imaginative, innovative way. The goal of CPS is to come up with innovative solutions, make a decision, and take action quickly. Sidney Parnes and Alex Osborn are credited with developing the creative problem solving process in the 1950s. The concept was further studied and developed at SUNY Buffalo State and the Creative Education Foundation. 

The core principles of CPS include the following:

• Balance divergent and convergent thinking
• Ask problems as questions
• Defer or suspend judgement
• Focus on “Yes, and…” rather than “No, but…”

According to Carella, “Creative problem solving is the mental process used for generating innovative and imaginative ideas as a solution to a problem or a challenge. Creative problem solving techniques can be pursued by individuals or groups.”

When asked to define CPS, Jaffa explains that it is, by nature, difficult to create boundaries for. “Creative problem solving is not cut and dry,” he says, “If you ask 100 different people the definition of creative problem solving, you’ll get 100 different responses - it’s a non-entity.”

Business presents a unique need for creative problem solving. Especially in today’s competitive landscape, organizations need to iterate quickly, innovate with intention, and constantly be at the cutting-edge of creativity and new ideas to succeed. Developing CPS skills among your workforce not only enables you to make faster, stronger in-the-moment decisions, but also inspires a culture of collaborative work and knowledge sharing. When people work together to generate multiple novel ideas and evaluate solutions, they are also more likely to arrive at an effective decision, which will improve business processes and reduce waste over time. In fact, CPS is so important that some companies now list creative problem solving skills as a job criteria.

MacLeod reiterates the vitality of creative problem solving in the workplace. “Problem solving is crucial for all groups and teams,” she says. “Leaders need to know how to guide the process, hear all voices and involve all members - it’s not easy.”

“This mental process [of CPS] is especially helpful in work environments where individuals and teams continuously struggle with new problems and challenges posed by their continuously changing environment,” adds Carella. 

Problem Solving Best Practices

By nature, creative problem solving does not have a clear-cut set of do’s and don’ts. Rather, creating a culture of strong creative problem solvers requires flexibility, adaptation, and interpersonal skills. However, there are a several best practices that you should incorporate:

• Use a Systematic Approach: Regardless of the technique you use, choose a systematic method that satisfies your workplace conditions and constraints (time, resources, budget, etc.). Although you want to preserve creativity and openness to new ideas, maintaining a structured approach to the process will help you stay organized and focused. 
• View Problems as Opportunities: Rather than focusing on the negatives or giving up when you encounter barriers, treat problems as opportunities to enact positive change on the situation. In fact, some experts even recommend defining problems as opportunities, to remain proactive and positive.
• Change Perspective: Remember that there are multiple ways to solve any problem. If you feel stuck, changing perspective can help generate fresh ideas. A perspective change might entail seeking advice of a mentor or expert, understanding the context of a situation, or taking a break and returning to the problem later. “A sterile or familiar environment can stifle new thinking and new perspectives,” says Carella. “Make sure you get out to draw inspiration from spaces and people out of your usual reach.”
• Break Down Silos: To invite the greatest possible number of perspectives to any problem, encourage teams to work cross-departmentally. This not only combines diverse expertise, but also creates a more trusting and collaborative environment, which is essential to effective CPS. According to Carella, “Big challenges are always best tackled by a group of people rather than left to a single individual. Make sure you create a space where the team can concentrate and convene.”
• Employ Strong Leadership or a Facilitator: Some companies choose to hire an external facilitator that teaches problem solving techniques, best practices, and practicums to stimulate creative problem solving. But, internal managers and staff can also oversee these activities. Regardless of whether the facilitator is internal or external, choose a strong leader who will value others’ ideas and make space for creative solutions.  Mattimore has specific advice regarding the role of a facilitator: “When facilitating, get the group to name a promising idea (it will crystalize the idea and make it more memorable), and facilitate deeper rather than broader. Push for not only ideas, but how an idea might specifically work, some of its possible benefits, who and when would be interested in an idea, etc. This fleshing-out process with a group will generate fewer ideas, but at the end of the day will yield more useful concepts that might be profitably pursued.” Additionally, Carella says that “Executives and managers don’t necessarily have to be creative problem solvers, but need to make sure that their teams are equipped with the right tools and resources to make this happen. Also they need to be able to foster an environment where failing fast is accepted and celebrated.”
• Evaluate Your Current Processes: This practice can help you unlock bottlenecks, and also identify gaps in your data and information management, both of which are common roots of business problems.

MacLeod offers the following additional advice, “Always get the facts. Don’t jump too quickly to a solution – working through [problems] takes time and patience.”

Mattimore also stresses that how you introduce creative problem solving is important. “Do not start by introducing a new company-wide innovation process,” he says. “Instead, encourage smaller teams to pursue specific creative projects, and then build a process from the ground up by emulating these smaller teams’ successful approaches. We say: ‘You don’t innovate by changing the culture, you change the culture by innovating.’”

Barriers to Effective Problem Solving

Learning how to effectively solve problems is difficult and takes time and continual adaptation. There are several common barriers to successful CPS, including:

• Confirmation Bias: The tendency to only search for or interpret information that confirms a person’s existing ideas. People misinterpret or disregard data that doesn’t align with their beliefs.
• Mental Set: People’s inclination to solve problems using the same tactics they have used to solve problems in the past. While this can sometimes be a useful strategy (see Analogical Thinking in a later section), it often limits inventiveness and creativity.
• Functional Fixedness: This is another form of narrow thinking, where people become “stuck” thinking in a certain way and are unable to be flexible or change perspective.
• Unnecessary Constraints: When people are overwhelmed with a problem, they can invent and impose additional limits on solution avenues. To avoid doing this, maintain a structured, level-headed approach to evaluating causes, effects, and potential solutions.
• Groupthink: Be wary of the tendency for group members to agree with each other — this might be out of conflict avoidance, path of least resistance, or fear of speaking up. While this agreeableness might make meetings run smoothly, it can actually stunt creativity and idea generation, therefore limiting the success of your chosen solution.
• Irrelevant Information: The tendency to pile on multiple problems and factors that may not even be related to the challenge at hand. This can cloud the team’s ability to find direct, targeted solutions.
• Paradigm Blindness: This is found in people who are unwilling to adapt or change their worldview, outlook on a particular problem, or typical way of processing information. This can erode the effectiveness of problem solving techniques because they are not aware of the narrowness of their thinking, and therefore cannot think or act outside of their comfort zone.

According to Jaffa, the primary barrier of effective problem solving is rigidity. “The most common things people say are, ‘We’ve never done it before,’ or ‘We’ve always done it this way.’” While these feelings are natural, Jaffa explains that this rigid thinking actually precludes teams from identifying creative, inventive solutions that result in the greatest benefit.

“The biggest barrier to creative problem solving is a lack of awareness – and commitment to – training employees in state-of-the-art creative problem-solving techniques,” Mattimore explains. “We teach our clients how to use ideation techniques (as many as two-dozen different creative thinking techniques) to help them generate more and better ideas. Ideation techniques use specific and customized stimuli, or ‘thought triggers’ to inspire new thinking and new ideas.” 

MacLeod adds that ineffective or rushed leadership is another common culprit. “We're always in a rush to fix quickly,” she says. “Sometimes leaders just solve problems themselves, making unilateral decisions to save time. But the investment is well worth it — leaders will have less on their plates if they can teach and eventually trust the team to resolve. Teams feel empowered and engagement and investment increases.”

Strategies for Problem Cause Identification

As discussed, most experts agree that the first and most crucial step in problem solving is defining the problem. Once you’ve done this, however, it may not be appropriate to move straight to the solution phase. Rather, it is often helpful to identify the cause(s) of the problem: This will better inform your solution planning and execution, and help ensure that you don’t fall victim to the same challenges in the future. 

Below are some of the most common strategies for identifying the cause of a problem:

• Root Cause Analysis: This method helps identify the most critical cause of a problem. A factor is considered a root cause if removing it prevents the problem from recurring. Performing a root cause analysis is a 12 step process that includes: define the problem, gather data on the factors contributing to the problem, group the factors based on shared characteristics, and create a cause-and-effect timeline to determine the root cause. After that, you identify and evaluate corrective actions to eliminate the root cause.

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Problem Solving Techniques and Strategies

In this section, we’ll explain several traditional and creative problem solving methods that you can use to identify challenges, create actionable goals, and resolve problems as they arise. Although there is often procedural and objective crossover among techniques, they are grouped by theme so you can identify which method works best for your organization.

Divergent Creative Problem Solving Techniques

Brainstorming: One of the most common methods of divergent thinking, brainstorming works best in an open group setting where everyone is encouraged to share their creative ideas. The goal is to generate as many ideas as possible – you analyze, critique, and evaluate the ideas only after the brainstorming session is complete. To learn more specific brainstorming techniques, read this article . 

Mind Mapping: This is a visual thinking tool where you graphically depict concepts and their relation to one another. You can use mind mapping to structure the information you have, analyze and synthesize it, and generate solutions and new ideas from there. The goal of a mind map is to simplify complicated problems so you can more clearly identify solutions.

Appreciative Inquiry (AI): The basic assumption of AI is that “an organization is a mystery to be embraced.” Using this principle, AI takes a positive, inquisitive approach to identifying the problem, analyzing the causes, and presenting possible solutions. The five principles of AI emphasize dialogue, deliberate language and outlook, and social bonding. 

Lateral Thinking: This is an indirect problem solving approach centered on the momentum of idea generation. As opposed to critical thinking, where people value ideas based on their truth and the absence of errors, lateral thinking values the “movement value” of new ideas: This means that you reward team members for producing a large volume of new ideas rapidly. With this approach, you’ll generate many new ideas before approving or rejecting any.

Problem Solving Techniques to Change Perspective

Constructive Controversy: This is a structured approach to group decision making to preserve critical thinking and disagreement while maintaining order. After defining the problem and presenting multiple courses of action, the group divides into small advocacy teams who research, analyze, and refute a particular option. Once each advocacy team has presented its best-case scenario, the group has a discussion (advocacy teams still defend their presented idea). Arguing and playing devil’s advocate is encouraged to reach an understanding of the pros and cons of each option. Next, advocacy teams abandon their cause and evaluate the options openly until they reach a consensus. All team members formally commit to the decision, regardless of whether they advocated for it at the beginning. You can learn more about the goals and steps in constructive controversy here . 

Carella is a fan of this approach. “Create constructive controversy by having two teams argue the pros and cons of a certain idea,” he says. “It forces unconscious biases to surface and gives space for new ideas to formulate.”

Abstraction: In this method, you apply the problem to a fictional model of the current situation. Mapping an issue to an abstract situation can shed extraneous or irrelevant factors, and reveal places where you are overlooking obvious solutions or becoming bogged down by circumstances. 

Analogical Thinking: Also called analogical reasoning , this method relies on an analogy: using information from one problem to solve another problem (these separate problems are called domains). It can be difficult for teams to create analogies among unrelated problems, but it is a strong technique to help you identify repeated issues, zoom out and change perspective, and prevent the problems from occurring in the future. .

CATWOE: This framework ensures that you evaluate the perspectives of those whom your decision will impact. The factors and questions to consider include (which combine to make the acronym CATWOE):

• Customers: Who is on the receiving end of your decisions? What problem do they currently have, and how will they react to your proposed solution?
• Actors: Who is acting to bring your solution to fruition? How will they respond and be affected by your decision?
• Transformation Process: What processes will you employ to transform your current situation and meet your goals? What are the inputs and outputs?
• World View: What is the larger context of your proposed solution? What is the larger, big-picture problem you are addressing?
• Owner: Who actually owns the process? How might they influence your proposed solution (positively or negatively), and how can you influence them to help you?
• Environmental Constraints: What are the limits (environmental, resource- and budget-wise, ethical, legal, etc.) on your ideas? How will you revise or work around these constraints?

Complex Problem Solving

Soft Systems Methodology (SSM): For extremely complex problems, SSM can help you identify how factors interact, and determine the best course of action. SSM was borne out of organizational process modeling and general systems theory, which hold that everything is part of a greater, interconnected system: This idea works well for “hard” problems (where logic and a single correct answer are prioritized), and less so for “soft” problems (i.e., human problems where factors such as personality, emotions, and hierarchy come into play). Therefore, SSM defines a seven step process for problem solving: 

• Begin with the problem or problematic situation 
• Express the problem or situation and build a rich picture of the themes of the problem 
• Identify the root causes of the problem (most commonly with CATWOE)
• Build conceptual models of human activity surrounding the problem or situation
• Compare models with real-world happenings
• Identify changes to the situation that are both feasible and desirable
• Take action to implement changes and improve the problematic situation

SSM can be used for any complex soft problem, and is also a useful tool in change management . 

Failure Mode and Effects Analysis (FMEA): This method helps teams anticipate potential problems and take steps to mitigate them. Use FMEA when you are designing (redesigning) a complex function, process, product, or service. First, identify the failure modes, which are the possible ways that a project could fail. Then, perform an effects analysis to understand the consequences of each of the potential downfalls. This exercise is useful for internalizing the severity of each potential failure and its effects so you can make adjustments or safeties in your plan. 

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Problem Solving Based on Data or Logic (Heuristic Methods)

TRIZ: A Russian-developed problem solving technique that values logic, analysis, and forecasting over intuition or soft reasoning. TRIZ (translated to “theory of inventive problem solving” or TIPS in English) is a systematic approach to defining and identifying an inventive solution to difficult problems. The method offers several strategies for arriving at an inventive solution, including a contradictions matrix to assess trade-offs among solutions, a Su-Field analysis which uses formulas to describe a system by its structure, and ARIZ (algorithm of inventive problem solving) which uses algorithms to find inventive solutions. 

Inductive Reasoning: A logical method that uses evidence to conclude that a certain answer is probable (this is opposed to deductive reasoning, where the answer is assumed to be true). Inductive reasoning uses a limited number of observations to make useful, logical conclusions (for example, the Scientific Method is an extreme example of inductive reasoning). However, this method doesn’t always map well to human problems in the workplace — in these instances, managers should employ intuitive inductive reasoning , which allows for more automatic, implicit conclusions so that work can progress. This, of course, retains the principle that these intuitive conclusions are not necessarily the one and only correct answer. 

Process-Oriented Problem Solving Methods

Plan Do Check Act (PDCA): This is an iterative management technique used to ensure continual improvement of products or processes. First, teams plan (establish objectives to meet desired end results), then do (implement the plan, new processes, or produce the output), then check (compare expected with actual results), and finally act (define how the organization will act in the future, based on the performance and knowledge gained in the previous three steps). 

Means-End Analysis (MEA): The MEA strategy is to reduce the difference between the current (problematic) state and the goal state. To do so, teams compile information on the multiple factors that contribute to the disparity between the current and goal states. Then they try to change or eliminate the factors one by one, beginning with the factor responsible for the greatest difference in current and goal state. By systematically tackling the multiple factors that cause disparity between the problem and desired outcome, teams can better focus energy and control each step of the process. 

Hurson’s Productive Thinking Model: This technique was developed by Tim Hurson, and is detailed in his 2007 book Think Better: An Innovator’s Guide to Productive Thinking . The model outlines six steps that are meant to give structure while maintaining creativity and critical thinking: 1) Ask “What is going on?” 2) Ask “What is success?” 3) Ask “What is the question?” 4) Generate answers 5) Forge the solution 6) Align resources. 

Control Influence Accept (CIA): The basic premise of CIA is that how you respond to problems determines how successful you will be in overcoming them. Therefore, this model is both a problem solving technique and stress-management tool that ensures you aren’t responding to problems in a reactive and unproductive way. The steps in CIA include:

• Control: Identify the aspects of the problem that are within your control.
• Influence: Identify the aspects of the problem that you cannot control, but that you can influence.
• Accept: Identify the aspects of the problem that you can neither control nor influence, and react based on this composite information. 

GROW Model: This is a straightforward problem solving method for goal setting that clearly defines your goals and current situation, and then asks you to define the potential solutions and be realistic about your chosen course of action. The steps break down as follows:

• Goal: What do you want?
• Reality: Where are you now?
• Options: What could you do?
• Will: What will you do?

OODA Loop: This acronym stands for observe, orient, decide, and act. This approach is a decision-making cycle that values agility and flexibility over raw human force. It is framed as a loop because of the understanding that any team will continually encounter problems or opponents to success and have to overcome them.

There are also many un-named creative problem solving techniques that follow a sequenced series of steps. While the exact steps vary slightly, they all follow a similar trajectory and aim to accomplish similar goals of problem, cause, and goal identification, idea generation, and active solution implementation.

MacLeod offers her own problem solving procedure, which echoes the above steps:

“1. Recognize the Problem: State what you see. Sometimes the problem is covert. 2. Identify: Get the facts — What exactly happened? What is the issue? 3. and 4. Explore and Connect: Dig deeper and encourage group members to relate their similar experiences. Now you're getting more into the feelings and background [of the situation], not just the facts.  5. Possible Solutions: Consider and brainstorm ideas for resolution. 6. Implement: Choose a solution and try it out — this could be role play and/or a discussion of how the solution would be put in place.  7. Evaluate: Revisit to see if the solution was successful or not.”

Many of these problem solving techniques can be used in concert with one another, or multiple can be appropriate for any given problem. It’s less about facilitating a perfect CPS session, and more about encouraging team members to continually think outside the box and push beyond personal boundaries that inhibit their innovative thinking. So, try out several methods, find those that resonate best with your team, and continue adopting new techniques and adapting your processes along the way. 

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Overview of the Problem-Solving Mental Process

• Identify the Problem
• Define the Problem
• Form a Strategy
• Organize Information
• Allocate Resources
• Monitor Progress
• Evaluate the Results

Frequently Asked Questions

Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.

The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

• Asking questions about the problem
• Breaking the problem down into smaller pieces
• Looking at the problem from different perspectives
• Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

• Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
• Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell​

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

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You can become a better problem solving by:

• Practicing brainstorming and coming up with multiple potential solutions to problems
• Being open-minded and considering all possible options before making a decision
• Breaking down problems into smaller, more manageable pieces
• Asking for help when needed
• Researching different problem-solving techniques and trying out new ones
• Learning from mistakes and using them as opportunities to grow

It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.

Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.

If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.

Davidson JE, Sternberg RJ, editors.  The Psychology of Problem Solving .  Cambridge University Press; 2003. doi:10.1017/CBO9780511615771

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

4 Main problem-solving strategies

In Psychology, you get to read about a ton of therapies. It’s mind-boggling how different theorists have looked at human nature differently and have come up with different, often somewhat contradictory, theoretical approaches.

Yet, you can’t deny the kernel of truth that’s there in all of them. All therapies, despite being different, have one thing in common- they all aim to solve people’s problems. They all aim to equip people with problem-solving strategies to help them deal with their life problems.

Problem-solving is really at the core of everything we do. Throughout our lives, we’re constantly trying to solve one problem or another. When we can’t, all sorts of psychological problems take hold. Getting good at solving problems is a fundamental life skill.

Problem-solving stages

What problem-solving does is take you from an initial state (A) where a problem exists to a final or goal state (B), where the problem no longer exists.

To move from A to B, you need to perform some actions called operators. Engaging in the right operators moves you from A to B. So, the stages of problem-solving are:

• Initial state

The problem itself can either be well-defined or ill-defined. A well-defined problem is one where you can clearly see where you are (A), where you want to go (B), and what you need to do to get there (engaging the right operators).

For example, feeling hungry and wanting to eat can be seen as a problem, albeit a simple one for many. Your initial state is hunger (A) and your final state is satisfaction or no hunger (B). Going to the kitchen and finding something to eat is using the right operator.

In contrast, ill-defined or complex problems are those where one or more of the three problem solving stages aren’t clear. For example, if your goal is to bring about world peace, what is it exactly that you want to do?

It’s been rightly said that a problem well-defined is a problem half-solved. Whenever you face an ill-defined problem, the first thing you need to do is get clear about all the three stages.

Often, people will have a decent idea of where they are (A) and where they want to be (B). What they usually get stuck on is finding the right operators.

Initial theory in problem-solving

When people first attempt to solve a problem, i.e. when they first engage their operators, they often have an initial theory of solving the problem. As I mentioned in my article on overcoming challenges for complex problems, this initial theory is often wrong.

But, at the time, it’s usually the result of the best information the individual can gather about the problem. When this initial theory fails, the problem-solver gets more data, and he refines the theory. Eventually, he finds an actual theory i.e. a theory that works. This finally allows him to engage the right operators to move from A to B.

Problem-solving strategies

These are operators that a problem solver tries to move from A to B. There are several problem-solving strategies but the main ones are:

• Trial and error

1. Algorithms

When you follow a step-by-step procedure to solve a problem or reach a goal, you’re using an algorithm. If you follow the steps exactly, you’re guaranteed to find the solution. The drawback of this strategy is that it can get cumbersome and time-consuming for large problems.

Say I hand you a 200-page book and ask you to read out to me what’s written on page 100. If you start from page 1 and keep turning the pages, you’ll eventually reach page 100. There’s no question about it. But the process is time-consuming. So instead you use what’s called a heuristic.

2. Heuristics

Heuristics are rules of thumb that people use to simplify problems. They’re often based on memories from past experiences. They cut down the number of steps needed to solve a problem, but they don’t always guarantee a solution. Heuristics save us time and effort if they work.

You know that page 100 lies in the middle of the book. Instead of starting from page one, you try to open the book in the middle. Of course, you may not hit page 100, but you can get really close with just a couple of tries.

If you open page 90, for instance, you can then algorithmically move from 90 to 100. Thus, you can use a combination of heuristics and algorithms to solve the problem. In real life, we often solve problems like this.

When police are looking for suspects in an investigation, they try to narrow down the problem similarly. Knowing the suspect is 6 feet tall isn’t enough, as there could be thousands of people out there with that height.

Knowing the suspect is 6 feet tall, male, wears glasses, and has blond hair narrows down the problem significantly.

3. Trial and error

When you have an initial theory to solve a problem, you try it out. If you fail, you refine or change your theory and try again. This is the trial-and-error process of solving problems. Behavioral and cognitive trial and error often go hand in hand, but for many problems, we start with behavioural trial and error until we’re forced to think.

Say you’re in a maze, trying to find your way out. You try one route without giving it much thought and you find it leads to nowhere. Then you try another route and fail again. This is behavioural trial and error because you aren’t putting any thought into your trials. You’re just throwing things at the wall to see what sticks.

This isn’t an ideal strategy but can be useful in situations where it’s impossible to get any information about the problem without doing some trials.

Then, when you have enough information about the problem, you shuffle that information in your mind to find a solution. This is cognitive trial and error or analytical thinking. Behavioral trial and error can take a lot of time, so using cognitive trial and error as much as possible is advisable. You got to sharpen your axe before you cut the tree.

When solving complex problems, people get frustrated after having tried several operators that didn’t work. They abandon their problem and go on with their routine activities. Suddenly, they get a flash of insight that makes them confident they can now solve the problem.

I’ve done an entire article on the underlying mechanics of insight . Long story short, when you take a step back from your problem, it helps you see things in a new light. You make use of associations that were previously unavailable to you.

You get more puzzle pieces to work with and this increases the odds of you finding a path from A to B, i.e. finding operators that work.

Pilot problem-solving

No matter what problem-solving strategy you employ, it’s all about finding out what works. Your actual theory tells you what operators will take you from A to B. Complex problems don’t reveal their actual theories easily solely because they are complex.

Therefore, the first step to solving a complex problem is getting as clear as you can about what you’re trying to accomplish- collecting as much information as you can about the problem.

This gives you enough raw materials to formulate an initial theory. We want our initial theory to be as close to an actual theory as possible. This saves time and resources.

Solving a complex problem can mean investing a lot of resources. Therefore, it is recommended you verify your initial theory if you can. I call this pilot problem-solving.

Before businesses invest in making a product, they sometimes distribute free versions to a small sample of potential customers to ensure their target audience will be receptive to the product.

Before making a series of TV episodes, TV show producers often release pilot episodes to figure out whether the show can take off.

Before conducting a large study, researchers do a pilot study to survey a small sample of the population to determine if the study is worth carrying out.

The same ‘testing the waters’ approach needs to be applied to solving any complex problem you might be facing. Is your problem worth investing a lot of resources in? In management, we’re constantly taught about Return On Investment (ROI). The ROI should justify the investment.

If the answer is yes, go ahead and formulate your initial theory based on extensive research. Find a way to verify your initial theory. You need this reassurance that you’re going in the right direction, especially for complex problems that take a long time to solve.

Getting your causal thinking right

Problem solving boils down to getting your causal thinking right. Finding solutions is all about finding out what works, i.e. finding operators that take you from A to B. To succeed, you need to be confident in your initial theory (If I do X and Y, they’ll lead me to B). You need to be sure that doing X and Y will lead you to B- doing X and Y will cause B.

All obstacles to problem-solving or goal-accomplishing are rooted in faulty causal thinking leading to not engaging the right operators. When your causal thinking is on point, you’ll have no problem engaging the right operators.

As you can imagine, for complex problems, getting our causal thinking right isn’t easy. That’s why we need to formulate an initial theory and refine it over time.

I like to think of problem-solving as the ability to project the present into the past or into the future. When you’re solving problems, you’re basically looking at your present situation and asking yourself two questions:

“What caused this?” (Projecting present into the past)

“What will this cause?” (Projecting present into the future)

The first question is more relevant to problem-solving and the second to goal-accomplishing.

If you find yourself in a mess , you need to answer the “What caused this?” question correctly. For the operators you’re currently engaging to reach your goal, ask yourself, “What will this cause?” If you think they cannot cause B, it’s time to refine your initial theory.

Hi, I’m Hanan Parvez (MA Psychology). I’ve published over 500 articles and authored one book. My work has been featured in Forbes , Business Insider , Reader’s Digest , and Entrepreneur .

The Art of Effective Problem Solving: A Step-by-Step Guide

Author: Daniel Croft

Daniel Croft is an experienced continuous improvement manager with a Lean Six Sigma Black Belt and a Bachelor's degree in Business Management. With more than ten years of experience applying his skills across various industries, Daniel specializes in optimizing processes and improving efficiency. His approach combines practical experience with a deep understanding of business fundamentals to drive meaningful change.

Whether we realise it or not, problem solving skills are an important part of our daily lives. From resolving a minor annoyance at home to tackling complex business challenges at work, our ability to solve problems has a significant impact on our success and happiness. However, not everyone is naturally gifted at problem-solving, and even those who are can always improve their skills. In this blog post, we will go over the art of effective problem-solving step by step.

You will learn how to define a problem, gather information, assess alternatives, and implement a solution, all while honing your critical thinking and creative problem-solving skills. Whether you’re a seasoned problem solver or just getting started, this guide will arm you with the knowledge and tools you need to face any challenge with confidence. So let’s get started!

Problem Solving Methodologies

Individuals and organisations can use a variety of problem-solving methodologies to address complex challenges. 8D and A3 problem solving techniques are two popular methodologies in the Lean Six Sigma framework.

Methodology of 8D (Eight Discipline) Problem Solving:

The 8D problem solving methodology is a systematic, team-based approach to problem solving. It is a method that guides a team through eight distinct steps to solve a problem in a systematic and comprehensive manner.

The 8D process consists of the following steps:

• Form a team: Assemble a group of people who have the necessary expertise to work on the problem.
• Define the issue: Clearly identify and define the problem, including the root cause and the customer impact.
• Create a temporary containment plan: Put in place a plan to lessen the impact of the problem until a permanent solution can be found.
• Identify the root cause: To identify the underlying causes of the problem, use root cause analysis techniques such as Fishbone diagrams and Pareto charts.
• Create and test long-term corrective actions: Create and test a long-term solution to eliminate the root cause of the problem.
• Implement and validate the permanent solution: Implement and validate the permanent solution’s effectiveness.
• Prevent recurrence: Put in place measures to keep the problem from recurring.
• Recognize and reward the team: Recognize and reward the team for its efforts.

Download the 8D Problem Solving Template

A3 Problem Solving Method:

The A3 problem solving technique is a visual, team-based problem-solving approach that is frequently used in Lean Six Sigma projects. The A3 report is a one-page document that clearly and concisely outlines the problem, root cause analysis, and proposed solution.

The A3 problem-solving procedure consists of the following steps:

• Determine the issue: Define the issue clearly, including its impact on the customer.
• Perform root cause analysis: Identify the underlying causes of the problem using root cause analysis techniques.
• Create and implement a solution: Create and implement a solution that addresses the problem’s root cause.
• Monitor and improve the solution: Keep an eye on the solution’s effectiveness and make any necessary changes.

Subsequently, in the Lean Six Sigma framework, the 8D and A3 problem solving methodologies are two popular approaches to problem solving. Both methodologies provide a structured, team-based problem-solving approach that guides individuals through a comprehensive and systematic process of identifying, analysing, and resolving problems in an effective and efficient manner.

Step 1 – Define the Problem

The definition of the problem is the first step in effective problem solving. This may appear to be a simple task, but it is actually quite difficult. This is because problems are frequently complex and multi-layered, making it easy to confuse symptoms with the underlying cause. To avoid this pitfall, it is critical to thoroughly understand the problem.

To begin, ask yourself some clarifying questions:

• What exactly is the issue?
• What are the problem’s symptoms or consequences?
• Who or what is impacted by the issue?
• When and where does the issue arise?

Answering these questions will assist you in determining the scope of the problem. However, simply describing the problem is not always sufficient; you must also identify the root cause. The root cause is the underlying cause of the problem and is usually the key to resolving it permanently.

Try asking “why” questions to find the root cause:

• What causes the problem?
• Why does it continue?
• Why does it have the effects that it does?

By repeatedly asking “ why ,” you’ll eventually get to the bottom of the problem. This is an important step in the problem-solving process because it ensures that you’re dealing with the root cause rather than just the symptoms.

Once you have a firm grasp on the issue, it is time to divide it into smaller, more manageable chunks. This makes tackling the problem easier and reduces the risk of becoming overwhelmed. For example, if you’re attempting to solve a complex business problem, you might divide it into smaller components like market research, product development, and sales strategies.

To summarise step 1, defining the problem is an important first step in effective problem-solving. You will be able to identify the root cause and break it down into manageable parts if you take the time to thoroughly understand the problem. This will prepare you for the next step in the problem-solving process, which is gathering information and brainstorming ideas.

Step 2 – Gather Information and Brainstorm Ideas

Gathering information and brainstorming ideas is the next step in effective problem solving. This entails researching the problem and relevant information, collaborating with others, and coming up with a variety of potential solutions. This increases your chances of finding the best solution to the problem.

Begin by researching the problem and relevant information. This could include reading articles, conducting surveys, or consulting with experts. The goal is to collect as much information as possible in order to better understand the problem and possible solutions.

Next, work with others to gather a variety of perspectives. Brainstorming with others can be an excellent way to come up with new and creative ideas. Encourage everyone to share their thoughts and ideas when working in a group, and make an effort to actively listen to what others have to say. Be open to new and unconventional ideas and resist the urge to dismiss them too quickly.

Finally, use brainstorming to generate a wide range of potential solutions. This is the place where you can let your imagination run wild. At this stage, don’t worry about the feasibility or practicality of the solutions; instead, focus on generating as many ideas as possible. Write down everything that comes to mind, no matter how ridiculous or unusual it may appear. This can be done individually or in groups.

Once you’ve compiled a list of potential solutions, it’s time to assess them and select the best one. This is the next step in the problem-solving process, which we’ll go over in greater detail in the following section.

Step 3 – Evaluate Options and Choose the Best Solution

Once you’ve compiled a list of potential solutions, it’s time to assess them and select the best one. This is the third step in effective problem solving, and it entails weighing the advantages and disadvantages of each solution, considering their feasibility and practicability, and selecting the solution that is most likely to solve the problem effectively.

To begin, weigh the advantages and disadvantages of each solution. This will assist you in determining the potential outcomes of each solution and deciding which is the best option. For example, a quick and easy solution may not be the most effective in the long run, whereas a more complex and time-consuming solution may be more effective in solving the problem in the long run.

Consider each solution’s feasibility and practicability. Consider the following:

• Can the solution be implemented within the available resources, time, and budget?
• What are the possible barriers to implementing the solution?
• Is the solution feasible in today’s political, economic, and social environment?

You’ll be able to tell which solutions are likely to succeed and which aren’t by assessing their feasibility and practicability.

Finally, choose the solution that is most likely to effectively solve the problem. This solution should be based on the criteria you’ve established, such as the advantages and disadvantages of each solution, their feasibility and practicability, and your overall goals.

It is critical to remember that there is no one-size-fits-all solution to problems. What is effective for one person or situation may not be effective for another. This is why it is critical to consider a wide range of solutions and evaluate each one based on its ability to effectively solve the problem.

Step 4 – Implement and Monitor the Solution

When you’ve decided on the best solution, it’s time to put it into action. The fourth and final step in effective problem solving is to put the solution into action, monitor its progress, and make any necessary adjustments.

To begin, implement the solution. This may entail delegating tasks, developing a strategy, and allocating resources. Ascertain that everyone involved understands their role and responsibilities in the solution’s implementation.

Next, keep an eye on the solution’s progress. This may entail scheduling regular check-ins, tracking metrics, and soliciting feedback from others. You will be able to identify any potential roadblocks and make any necessary adjustments in a timely manner if you monitor the progress of the solution.

Finally, make any necessary modifications to the solution. This could entail changing the solution, altering the plan of action, or delegating different tasks. Be willing to make changes if they will improve the solution or help it solve the problem more effectively.

It’s important to remember that problem solving is an iterative process, and there may be times when you need to start from scratch. This is especially true if the initial solution does not effectively solve the problem. In these situations, it’s critical to be adaptable and flexible and to keep trying new solutions until you find the one that works best.

To summarise, effective problem solving is a critical skill that can assist individuals and organisations in overcoming challenges and achieving their objectives. Effective problem solving consists of four key steps: defining the problem, generating potential solutions, evaluating alternatives and selecting the best solution, and implementing the solution.

You can increase your chances of success in problem solving by following these steps and considering factors such as the pros and cons of each solution, their feasibility and practicability, and making any necessary adjustments. Furthermore, keep in mind that problem solving is an iterative process, and there may be times when you need to go back to the beginning and restart. Maintain your adaptability and try new solutions until you find the one that works best for you.

• Novick, L.R. and Bassok, M., 2005.  Problem Solving . Cambridge University Press.

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Daniel Croft

Daniel Croft is a seasoned continuous improvement manager with a Black Belt in Lean Six Sigma. With over 10 years of real-world application experience across diverse sectors, Daniel has a passion for optimizing processes and fostering a culture of efficiency. He's not just a practitioner but also an avid learner, constantly seeking to expand his knowledge. Outside of his professional life, Daniel has a keen Investing, statistics and knowledge-sharing, which led him to create the website www.learnleansigma.com, a platform dedicated to Lean Six Sigma and process improvement insights.

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36 Problem-solving techniques, methods and tools

When it comes to solving problems, getting ideas is the easy part. 

But businesses often forget the other four stages of the problem-solving process that will allow them to find the best solution.

Instead of jumping straight to idea generation, your problem-solving framework should look like this:

• Identify the problem
• Reveal why it has occurred
• Brainstorm ideas
• Select the best solution

See how idea generation doesn’t appear until stage 3?!

In this extensive resource, we provide techniques, methodologies and tools to guide you through every stage of the problem-solving process.

Once you’ve finished reading, you’ll possess an extensive problem-solving arsenal that will enable you to overcome your biggest workplace challenges.

11 Problem-solving techniques for clarity and confidence

Before we dive into more comprehensive methodologies for solving problems, there are a few basic techniques you should know. 

The following techniques will set you up for a successful problem-solving session with your team, allowing you to take on your biggest challenges with clarity and confidence. ‍

1. Take a moment, take a breath

When a problem or challenge arises, it’s normal to act too quickly or rely on solutions that have worked well in the past. This is known as entrenched thinking.

But acting impulsively, without prior consideration or planning, can cause you to misunderstand the issue and overlook possible solutions to the problem.

Therefore, the first thing you should always do when you encounter a problem is: breathe in and out.

Take a step back and make a clear plan of action before you act. This will help you to take rational steps towards solving a problem. ‍

2. Ask questions to understand the full extent of the issue

Another common mistake people make when attempting to solve a problem is taking action before fully understanding the problem.

Before committing to a theory, ask enough questions to unearth the true root of the issue. 

Later in this article, we cover The 5 Why’s problem-solving methodology which you can use to easily identify the root of your problem. Give this a go at your next meeting and see how your initial understanding of a problem can often be wrong. ‍

3. Consider alternative perspectives

A common problem-solving issue is that of myopia—a narrow-minded view or perception of the problem. Myopia can occur when you’re too involved with the problem or your team isn’t diverse enough.

To give yourself the best chance of resolving a problem, gain insight from a wide range of sources. Collaborate with key stakeholders, customers and on-the-ground employees to learn how the problem affects them and whether they have found workarounds or solutions.

To paint the broadest picture, don’t limit your problem-solving team to a specific archetype. Try to include everyone, from the chief executive to the office janitor.

If you’re working with a small team, try the Flip It! problem-solving methodology to view the issue from a fresh angle. ‍

4. Make your office space conducive to problem-solving

The environment in which your host your brainstorming sessions should maximise creativity . When your team members trust each other and feel relaxed, they’re more likely to come up with innovative ideas and solutions to a problem.

Here are a few ways to get your employees’ creative juices flowing:

• Play team-building games that maximise trust and build interpersonal relationships
• Improve your team’s problem-solving skills with games that encourage critical thinking
• Redesign the office with comfortable furniture and collaborative spaces
• Boost job satisfaction by creating a positive work-life balance
• Improve collaborative skills and learn to resolve conflicts

World Café is a problem-solving method that creates a casual environment conducive to creative thinking. 

Keep reading to learn more about how World Café can help your team solve complex organisational problems. ‍

5. Use problem-solving methodologies to guide the process

Because problem-solving is a creative process, it can be hard to keep it on track. As more ideas get banded around, conflicts can arise that derail the session.

That’s why problem-solving methodologies are so helpful. They offer you proven problem-solving frameworks to guide your group sessions and keep them on track.

The Six Thinking Hats problem-solving method is a popular technique that guides the process and helps your team analyse a problem from all angles.

We’re going to take a look at our favourite problem-solving methodologies in the next section of this article, XY Tried and tested problem-solving methodologies. ‍

6. Use analogies to solve complex problems

Sometimes, solving a different problem can help you uncover solutions to another problem! 

By stripping back a complex issue and framing it as a simplified analogy , you approach a problem from a different angle, enabling you to come up with alternative ideas.

After solving practice problems, your team might be more aptly equipped to solve real-world issues.

However, coming up with an analogy that reflects your issue can be difficult, so don’t worry if this technique doesn’t work for you.

The Speed Boat diagram is a visual tool that helps your employees view existing challenges as anchors holding back a boat which represents your end goals. By assigning a “weight” to each anchor, your team can prioritise which issues to tackle first. ‍

7. Establish clear constraints

Constraints make a big problem more approachable. 

Before you tackle a problem, establish clear boundaries and codes of conduct for the session. This allows your team to focus on the current issue without becoming distracted or veering off on a tangent.

In an article published in the Harvard Business Review, authors Oguz A. Acar, Murat Tarakci, and Daan van Knippenberg wrote, “Constraints … provide focus and a creative challenge that motivates people to search for and connect information from different sources to generate novel ideas for new products, services, or business processes.” (Why Constraints Are Good for Innovation, 2019)

Lightning Decision Jam is a prime example of how constraints can assist the creative process. Here, your team are given strict time constraints and isn’t permitted to discuss ideas until the end. ‍

8. Dislodge preconceived ideas

Humans are creatures of habit. 

We defer to strategies that have produced positive results in the past. This is typically beneficial because recalling our previous successes means we don’t need to constantly re-learn similar tasks.

But when it comes to problem-solving, this way of thinking can trip us up. We become fixated on a solution that worked in the past, but when this fails we’re dismayed and left wondering what to do next.

To resolve problems effectively, your employees need to escape the precincts of their imaginations. This helps to eliminate functional fixedness—the belief that an item serves only its predefined function.

Alternative Application is an icebreaker game that encourages employees to think outside the box by coming up with different uses for everyday objects. Try this at your next meeting or team-building event and watch your team tap into their creativity. ‍

9. Level the playing field

Having a diverse group of employees at your brainstorming sessions is a good idea, but there’s one problem: the extroverted members of your team will be more vocal than the introverts.

To ensure you’re gaining insight from every member of your team, you need to give your quieter employees equal opportunities to contribute by eliminating personality biases.

Read more: What icebreaker games and questions work best for introverts?

The obvious solution, then, is to “silence” the louder participants (it’s not as sinister as it sounds, promise)—all you have to do is ban your team from debating suggestions during the ideation process. 

The Lightning Decision Jam methodology gives your employees equal opportunities to contribute because much of the problem-solving process is carried out in silence. ‍

10. Take a break from the problem

Have you ever noticed how the best ideas seem to come when you’re not actively working on a problem? You may have spent hours slumped over your desk hashing out a solution, only for the “eureka!” moment to come when you’re walking your dog or taking a shower.

In James Webb Young’s book, A Technique for Producing Ideas , phase three of the process is “stepping away from the problem.” Young proclaims that after putting in the hard work, the information needs to ferment in the mind before any plausible ideas come to you.

So next time you’re in a meeting with your team trying to solve a problem, don’t panic if you don’t uncover groundbreaking ideas there and then. Allow everybody to mull over what they’ve learned, then reconvene at a later date.

The Creativity Dice methodology is a quick-fire brainstorming game that allows your team to incubate ideas while concentrating on another. ‍

11. Limit feedback sessions

The way your team delivers feedback at the end of a successful brainstorming session is critical. Left unsupervised, excessive feedback can undo all of your hard work.

Therefore, it’s wise to put a cap on the amount of feedback your team can provide. One great way of doing this is by using the One Breath Feedback technique.

By limiting your employees to one breath, they’re taught to be concise with their final comments. 

16 Tried and tested problem-solving methodologies

Problem-solving methodologies keep your brainstorming session on track and encourage your team to consider all angles of the issue.

Countless methods have wiggled their way into the world of business, each one with a unique strategy and end goal.

Here are 12 of our favourite problem-solving methodologies that will help you find the best-fit solution to your troubles. ‍

12. Six Thinking Hats

Six Thinking Hats is a methodical problem-solving framework that helps your group consider all possible problems, causes, solutions and repercussions by assigning a different coloured hat to each stage of the problem-solving process.

The roles of each hat are as follows:

• Blue Hat (Control): This hat controls the session and dictates the order in which the hats will be worn. When wearing the Blue Hat, your group will observe possible solutions, draw conclusions and define a plan of action.
• Green Hat (Idea Generation): The Green Hat signifies creativity. At this stage of the methodology, your team will focus their efforts on generating ideas, imagining solutions and considering alternatives.
• Red Hat (Intuition and Feelings): It’s time for your employees to communicate their feelings. Here, your team listen to their guts and convey their emotional impulses without justification. 
• Yellow Hat (Benefits and Values): What are the merits of each idea that has been put forward thus far? What positive impacts could they have?
• Black or Grey Hat (Caution): What are the potential risks or shortcomings of each idea? What negative impacts could result from implicating each idea?
• White Hat (Information and Data): While wearing The White Hat, your team must determine what information is needed and from where it can be obtained.

For Six Thinking Hats to work effectively, ensure your team acts within the confines of each role. 

While wearing The Yellow Hat, for example, your team should only discuss the positives . Any negative implications should be left for the Black or Grey hat.

Note: Feel free to alter the hat colours to align with your cultural context. ‍

13. Lightning Decision Jam (LDJ)

Lightning Decision Jam is a nine-stage problem-solving process designed to uncover a variety of perspectives while keeping the session on track.

The process starts by defining a general topic like the internal design process, interdepartmental communication, the sales funnel, etc.

Then, armed with pens and post-it notes, your team will work through the nine stages in the following order:

• Write problems (7 minutes)
• Present problems (4 minutes/person)
• Select problems (6 minutes)
• Reframe the problems (6 minutes)
• Offer solutions (7 minutes)
• Vote on solutions (10 minutes)
• Prioritise solutions (30 seconds)
• Decide what to execute (10 minutes)
• Create task lists (5 minutes)

The philosophy behind LDJ is that of constraint. By limiting discussion, employees can focus on compiling ideas and coming to democratic decisions that benefit the company without being distracted or going off on a tangent. ‍

14. The 5 Why’s

Root Cause Analysis (RCA) is the process of unearthing a problem and finding the underlying cause. To help you through this process, you can use The 5 Why’s methodology.

The idea is to ask why you’re experiencing a problem, reframe the problem based on the answer, and then ask “ why?” again. If you do this five times , you should come pretty close to the root of your original challenge.

While this might not be a comprehensive end-to-end methodology, it certainly helps you to pin down your core challenges. ‍

15. World Café

If you’ve had enough of uninspiring corporate boardrooms, World Café is the solution. 

This problem-solving strategy facilitates casual conversations around given topics, enabling players to speak more openly about their grievances without the pressure of a large group.

Here’s how to do it:

• Create a cosy cafe-style setting (try to have at least five or six chairs per table).
• As a group, decide on a core problem and mark this as the session topic.
• Divide your group into smaller teams by arranging five or six players at a table.
• Assign each group a question that pertains to the session topic, or decide on one question for all groups to discuss at once.
• Give the groups about 20 minutes to casually talk over each question.
• Repeat this with about three or four different questions, making sure to write down key insights from each group.
• Share the insights with the whole group.

World Café is a useful way of uncovering hidden causes and pitfalls by having multiple simultaneous conversations about a given topic. ‍

16. Discovery and Action Dialogue (DAD)

Discovery and Actions Dialogues are a collaborative method for employees to share and adopt personal behaviours in response to a problem. 

This crowdsourcing approach provides insight into how a problem affects individuals throughout your company and whether some are better equipped than others.

A DAD session is guided by a facilitator who asks seven open-ended questions in succession. Each person is given equal time to participate while a recorder takes down notes and valuable insights. 

This is a particularly effective method for uncovering preexisting ideas, behaviours and solutions from the people who face problems daily. ‍

17. Design Sprint 2.0

The Design Sprint 2.0 model by Jake Knapp helps your team to focus on finding, developing measuring a solution within four days . Because theorising is all well and good, but sometimes you can learn more by getting an idea off the ground and observing how it plays out in the real world.

Here’s the basic problem-solving framework:

• Day 1: Map out or sketch possible solutions
• Day 2: Choose the best solutions and storyboard your strategy going forward
• Day 3: Create a living, breathing prototype
• Day 4: Test and record how it performs in the real world

This technique is great for testing the viability of new products or expanding and fixing the features of an existing product. ‍

18. Open Space Technology

Open Space Technology is a method for large groups to create a problem-solving agenda around a central theme. It works best when your group is comprised of subject-matter experts and experienced individuals with a sufficient stake in the problem.

Open Space Technology works like this:

• Establish a core theme for your team to centralise their efforts.
• Ask the participants to consider their approach and write it on a post-it note.
• Everybody writes a time and place for discussion on their note and sticks it to the wall.
• The group is then invited to join the sessions that most interest them.
• Everybody joins and contributes to their chosen sessions
• Any significant insights and outcomes are recorded and presented to the group.

This methodology grants autonomy to your team and encourages them to take ownership of the problem-solving process. ‍

19. Round-Robin Brainstorming Technique

While not an end-to-end problem-solving methodology, the Round-Robin Brainstorming Technique is an effective way of squeezing every last ounce of creativity from your ideation sessions.

Here’s how it works:

• Decide on a problem that needs to be solved
• Sitting in a circle, give each employee a chance to offer an idea
• Have somebody write down each idea as they come up
• Participants can pass if they don’t have anything to contribute
• The brainstorming session ends once everybody has passed

Once you’ve compiled a long list of ideas, it’s up to you how you move forward. You could, for example, borrow techniques from other methodologies, such as the “vote on solutions” phase of the Lightning Decision Jam. ‍

20. Failure Modes and Effects Analysis (FMEA)

Failure Modes and Effects Analysis is a method for preventing and mitigating problems within your business processes.

This technique starts by examining the process in question and asking, “What could go wrong?” From here, your team starts to brainstorm a list of potential failures.

Then, going through the list one by one, ask your participants, “Why would this failure happen?” 

Once you’ve answered this question for each list item, ask yourselves, “What would the consequences be of this failure?”

This proactive method focuses on prevention rather than treatment. Instead of waiting for a problem to occur and reacting, you’re actively searching for future shortcomings. ‍

21. Flip It!

The Flip It! Methodology teaches your team to view their concerns in a different light and frame them instead as catalysts for positive change.

The game works like this:

• Select a topic your employees are likely to be concerned about, like market demand for your product or friction between departments.
• Give each participant a pile of sticky notes and ask them to write down all their fears about the topic.
• Take the fears and stick them to an area of the wall marked “fears.”
• Then, encourage your team to look at these fears and ask them to reframe them as “hope” by writing new statements on different sticky notes.
• Take these “hope” statements and stick them to an area of the wall marked “hope.”
• Discuss the statements, then ask them to vote on the areas they feel they can start to take action on. They can do this by drawing a dot on the corner of the sticky note.
• Move the notes with the most votes to a new area of the wall marked “traction.”
• Discuss the most popular statements as a group and brainstorm actionable items related to each.
• Write down the actions that need to be made and discuss them again as a group.

This brainstorming approach teaches your employees the danger of engrained thinking and helps them to reframe their fears as opportunities. ‍

22. The Creativity Dice

The Creativity Dice teaches your team to incubate ideas as they focus on different aspects of a problem. As we mentioned earlier in the article, giving ideas time to mature can be a highly effective problem-solving strategy. Here’s how the game works:

Choose a topic to focus on, It can be as specific or open-ended as you like. Write this down as a word or sentence. Roll the die, start a timer of three minutes and start writing down ideas within the confines of what that number resembles. The roles of each number are as follows:

• Specification: Write down goals you want to achieve.
• Investigation: Write down existing factual information you know about the topic.
• Ideation: Write down creative or practical ideas related to the topic.
• Incubation: Do something else unrelated to the problem.
• Iteration: Look at what you’ve already written and come up with related ideas (roll again if you didn’t write anything yet). ‍
• Integration: Look at everything you have written and try to create something cohesive from your ideas like a potential new product or actionable next step.

Once you’ve finished the activity, review your findings and decide what you want to take with you. ‍

23. SWOT Analysis

The SWOT Analysis is a long-standing method for analysing the current state of your business and considering how this affects the desired end state.

The basic idea is this:

• Before the meeting, come up with a “Desired end state” and draw a picture that represents this on a flipchart or whiteboard.
• Divide a large piece of paper into quadrants marked “Strengths”, “Weaknesses”, “Opportunities” and “Threats.”
• Starting with “Strengths”, work through the quadrants, coming up with ideas that relate to the desired end state.
• Ask your team to vote for the statements or ideas of each category that they feel are most relevant to the desired end state.
• As a group, discuss the implications that these statements have on the desired end state. Spark debate by asking thought-provoking and open-ended questions.

The SWOT Analysis is an intuitive method for understanding which parts of your business could be affecting your long-term goals. ‍

24. The Journalistic Six

When learning to cover every aspect of a story, journalists are taught to ask themselves six essential questions:  

Now, this approach has been adopted by organisations to help understand every angle of a problem. All you need is a clear focus question, then you can start working through the six questions with your team until you have a 360-degree view of what has, can and needs to be done. ‍

25. Gamestorming

Gamestorming is a one-stop creative-thinking framework that uses various games to help your team come up with innovative ideas.

Originally published as a book 10 years ago, Gamestorming contained a selection of creative games used by Silicon Valley’s top-performing businesses to develop groundbreaking products and services.

This collection of resources, plucked from the minds of founders and CEOs like Jeff Bezos and Steve Jobs, allows you to tap into the potentially genius ideas lying dormant in the minds of your employees. ‍

26. Four-Step Sketch

The Four-Step Sketch is a visual brainstorming that provides an alternative to traditional discussion-based ideation techniques .

This methodology requires prior discussion to clarify the purpose of the activity. Imagine you’re on a startup retreat , for example, and your team is taking part in a design sprint or hackathon.

Once you’ve brainstormed a list of ideas with your team, participants can look at the suggestions and take down any relevant notes. They then take these notes and turn them into rough sketches that resemble the idea.

Then, as a warm-up, give each participant eight minutes to produce eight alternative sketches (eight minutes per sketch) of the idea. These ideas are not to be shared with the group.

Finally, participants create new sketches based on their favourite ideas and share them with the group. The group can then vote on the ideas they think offer the best solution. ‍

27. 15% Solutions

15% Solutions is a problem-solving strategy for motivating and inspiring your employees. By encouraging your team to gain small victories, you pave the way for bigger changes.

First, ask your participants to think about things they can personally do within the confines of their role.

Then, arrange your team into small groups of three to four and give them time to share their ideas and consult with each other.

This simple problem-solving process removes negativity and powerlessness and teaches your team to take responsibility for change. 

9 Problem-solving tools for gathering and selecting ideas

Problem-solving tools support your meeting with easy-to-use graphs, visualisations and techniques.  

By implementing a problem-solving tool, you break the cycle of mundane verbal discussion, enabling you to maintain engagement throughout the session. ‍

28. Fishbone Diagram

The Fishbone Diagram (otherwise known as the Ishikawa Diagram or Cause and Effect Diagram), is a tool for identifying the leading causes of a problem. You can then consolidate these causes into a comprehensive “Problem Statement.”

The term “Fishbone Diagram” is derived from the diagram’s structure. The problem itself forms the tail, possible causes radiate from the sides to form the fish skeleton while the final “Problem Statement” appears as the “head” of the fish.

Example: A fast-food chain is investigating the declining quality of their food. As the team brainstorms potential causes, they come up with reasons like “poorly trained personnel”, “lack of quality control”, and “incorrect quantity of spices.” Together with other causes, the group summarises that these problems lead to “bad burgers.” They write this as the Problem Statement and set about eliminating the main contributing factors. ‍

29. The Problem Tree

A Problem Tree is a useful tool for assessing the importance or relevance of challenges concerning the core topic. If you’re launching a new product, for example, gather your team and brainstorm the current issues, roadblocks and bottlenecks that are hindering the process.

Then, work together to decide which of these are most pressing. Place the most relevant issues closer to the core topic and less relevant issues farther away. ‍

30. SQUID Diagram

The Squid Diagram is an easy-to-use tool that charts the progress of ideas and business developments as they unfold. Your SQUID Diagram can remain on a wall for your team to add to over time.

• Write down a core theme on a sticky note such as “customer service” or “Innovation”—this will be the “head” of your SQUID.
• Hand two sets of different coloured sticky notes to your participants and choose one colour to represent “questions” and the other to represent “answers.”
• Ask your team to write down questions pertaining to the success of the main topic. In the case of “Innovation,” your team might write things like “How can we improve collaboration between key stakeholders?”
• Then, using the other coloured sticky notes, ask your team to write down possible answers to these questions. In the example above, this might be “Invest in open innovation software.”
• Over time, you’ll develop a spawling SQUID Diagram that reflects the creative problem-solving process. ‍

31. The Speed Boat

The Speed Boat Diagram is a visual metaphor used to help your team identify and solve problems in the way of your goals.

Here’s how it works: 

• Draw a picture of a boat and name it after the core objective.
• With your team, brainstorm things that are slowing progress and draw each one as an anchor beneath the boat.
• Discuss possible solutions to each problem on the diagram.

This is an easy-to-use tool that sparks creative solutions. If you like, your team can assign a “weight” to each anchor which determines the impact each problem has on the end goal. ‍

32. The LEGO Challenge

LEGO is an excellent creative-thinking and problem-solving tool used regularly by event facilitators to help teams overcome challenges. 

In our article 5 and 10-minute Team-Building Activities , we introduce Sneak a Peek —a collaborative team-building game that develops communication and leadership skills. ‍

33. The Three W’s: What? So What? Now What?

Teams aren’t always aligned when it comes to their understanding of a problem. While the problem remains the same for everyone, they might have differing opinions as to how it occurred at the implications it had.

Asking “ What? So What? Now What?” Helps you to understand different perspectives around a problem.

It goes like this:

• Alone or in small groups, ask your employees to consider and write What happened. This should take between five and 10 minutes.
• Then ask So What? What occurred because of this? Why was what happened important? What might happen if this issue is left unresolved?
• Finally, ask your team Now What? What might be a solution to the problem? What actions do you need to take to avoid this happening again?

This approach helps your team understand how problems affect individuals in different ways and uncovers a variety of ways to overcome them. ‍

34. Now-How-Wow Matrix

Gathering ideas is easy—but selecting the best ones? That’s a different story. 

If you’ve got a bunch of ideas, try the Now-How-Wow Matrix to help you identify which ones you should implement now and which ones should wait until later.

Simply draw a two-axis graph with “implementation difficulty” on the Y axis and “idea originality” on the X axis. Divide this graph into quadrants and write “Now!” in the bottom left panel, “Wow!” in the bottom right panel, and “How?” in the top right panel. You can leave the top left panel blank.

Then, take your ideas and plot them on the graph depending on their implementation difficulty and level of originality.

By the end, you’ll have a clearer picture of which ideas to ignore, which ones to implement now, and which ones to add to the pipeline for the future. ‍

35. Impact-Effort Matrix

The Impact-Effort Matrix is a variation of the Now-How-Wow Matrix where the Y axis is marked “Impact” and the X axis is marked “Effort.”

Then, divide the graph into quadrants and plot your ideas. 

• Top left section = Excellent, implement immediately
• Top right section = Risky, but worth a try
• Bottom left section = Low risk, but potentially ineffective
• Bottom right section = Bad idea, ignore

The Impact-Effort Matrix is a simple way for your team to weigh the benefits of an idea against the amount of investment required. ‍

36. Dot Voting

Once you’ve gathered a substantial list of ideas from your employees, you need to sort the good from the bad. 

Dot voting is a simple tool used by problem-solving facilitators as a fast and effective way for large groups to vote on their favourite ideas . You’ll have seen this method used in problem-solving methods like Flip It! and Lightning Decision Jam .

• Participants write their ideas on sticky notes and stick them to the wall or a flipchart.
• When asked, participants draw a small dot on the corner of the idea they like the most.
• Participants can be given as many votes as necessary.
• When voting ends, arrange the notes from “most popular” to “least popular.”

This provides an easy-to-use visual representation of the best and worst ideas put forward by your team.

Give your problems the attention they deserve at an offsite retreat

While working from home or at the office, your team is often too caught up in daily tasks to take on complex problems. 

By escaping the office and uniting at an offsite location, you can craft a purposeful agenda of team-building activities and problem-solving sessions. This special time away from the office can prove invaluable when it comes to keeping your business on track.

If you have problems that need fixing (who doesn’t?), reach out to Surf Office and let us put together a fully-customised offsite retreat for you.

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How to improve your problem solving skills and build effective problem solving strategies

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A step-by-step guide to planning a workshop, 54 great online tools for workshops and meetings, how to create an unforgettable training session in 8 simple steps.

• 18 Free Facilitation Resources We Think You’ll Love

Effective problem solving is all about using the right process and following a plan tailored to the issue at hand. Recognizing your team or organization has an issue isn’t enough to come up with effective problem solving strategies. 

To truly understand a problem and develop appropriate solutions, you will want to follow a solid process, follow the necessary problem solving steps, and bring all of your problem solving skills to the table.   We’ll forst look at what problem solving strategies you can employ with your team when looking for a way to approach the process. We’ll then discuss the problem solving skills you need to be more effective at solving problems, complete with an activity from the SessionLab library you can use to develop that skill in your team.

Let’s get to it! 

Problem solving strategies

What skills do i need to be an effective problem solver, how can i improve my problem solving skills.

Problem solving strategies are methods of approaching and facilitating the process of problem-solving with a set of techniques , actions, and processes. Different strategies are more effective if you are trying to solve broad problems such as achieving higher growth versus more focused problems like, how do we improve our customer onboarding process?

Broadly, the problem solving steps outlined above should be included in any problem solving strategy though choosing where to focus your time and what approaches should be taken is where they begin to differ. You might find that some strategies ask for the problem identification to be done prior to the session or that everything happens in the course of a one day workshop.

The key similarity is that all good problem solving strategies are structured and designed. Four hours of open discussion is never going to be as productive as a four-hour workshop designed to lead a group through a problem solving process.

Good problem solving strategies are tailored to the team, organization and problem you will be attempting to solve. Here are some example problem solving strategies you can learn from or use to get started.

Use a workshop to lead a team through a group process

Often, the first step to solving problems or organizational challenges is bringing a group together effectively. Most teams have the tools, knowledge, and expertise necessary to solve their challenges – they just need some guidance in how to use leverage those skills and a structure and format that allows people to focus their energies.

Facilitated workshops are one of the most effective ways of solving problems of any scale. By designing and planning your workshop carefully, you can tailor the approach and scope to best fit the needs of your team and organization. 

Problem solving workshop

• Creating a bespoke, tailored process
• Tackling problems of any size
• Building in-house workshop ability and encouraging their use

Workshops are an effective strategy for solving problems. By using tried and test facilitation techniques and methods, you can design and deliver a workshop that is perfectly suited to the unique variables of your organization. You may only have the capacity for a half-day workshop and so need a problem solving process to match. 

By using our session planner tool and importing methods from our library of 700+ facilitation techniques, you can create the right problem solving workshop for your team. It might be that you want to encourage creative thinking or look at things from a new angle to unblock your groups approach to problem solving. By tailoring your workshop design to the purpose, you can help ensure great results.

One of the main benefits of a workshop is the structured approach to problem solving. Not only does this mean that the workshop itself will be successful, but many of the methods and techniques will help your team improve their working processes outside of the workshop. 

We believe that workshops are one of the best tools you can use to improve the way your team works together. Start with a problem solving workshop and then see what team building, culture or design workshops can do for your organization!

Run a design sprint

Great for: 

• aligning large, multi-discipline teams
• quickly designing and testing solutions
• tackling large, complex organizational challenges and breaking them down into smaller tasks

By using design thinking principles and methods, a design sprint is a great way of identifying, prioritizing and prototyping solutions to long term challenges that can help solve major organizational problems with quick action and measurable results.

Some familiarity with design thinking is useful, though not integral, and this strategy can really help a team align if there is some discussion around which problems should be approached first. 

The stage-based structure of the design sprint is also very useful for teams new to design thinking.  The inspiration phase, where you look to competitors that have solved your problem, and the rapid prototyping and testing phases are great for introducing new concepts that will benefit a team in all their future work. 

It can be common for teams to look inward for solutions and so looking to the market for solutions you can iterate on can be very productive. Instilling an agile prototyping and testing mindset can also be great when helping teams move forwards – generating and testing solutions quickly can help save time in the long run and is also pretty exciting!

Break problems down into smaller issues

Organizational challenges and problems are often complicated and large scale in nature. Sometimes, trying to resolve such an issue in one swoop is simply unachievable or overwhelming. Try breaking down such problems into smaller issues that you can work on step by step. You may not be able to solve the problem of churning customers off the bat, but you can work with your team to identify smaller effort but high impact elements and work on those first.

This problem solving strategy can help a team generate momentum, prioritize and get some easy wins. It’s also a great strategy to employ with teams who are just beginning to learn how to approach the problem solving process. If you want some insight into a way to employ this strategy, we recommend looking at our design sprint template below!

Use guiding frameworks or try new methodologies

Some problems are best solved by introducing a major shift in perspective or by using new methodologies that encourage your team to think differently.

Props and tools such as Methodkit , which uses a card-based toolkit for facilitation, or Lego Serious Play can be great ways to engage your team and find an inclusive, democratic problem solving strategy. Remember that play and creativity are great tools for achieving change and whatever the challenge, engaging your participants can be very effective where other strategies may have failed.

LEGO Serious Play

• Improving core problem solving skills
• Thinking outside of the box
• Encouraging creative solutions

LEGO Serious Play is a problem solving methodology designed to get participants thinking differently by using 3D models and kinesthetic learning styles. By physically building LEGO models based on questions and exercises, participants are encouraged to think outside of the box and create their own responses. 

Collaborate LEGO Serious Play exercises are also used to encourage communication and build problem solving skills in a group. By using this problem solving process, you can often help different kinds of learners and personality types contribute and unblock organizational problems with creative thinking. 

Problem solving strategies like LEGO Serious Play are super effective at helping a team solve more skills-based problems such as communication between teams or a lack of creative thinking. Some problems are not suited to LEGO Serious Play and require a different problem solving strategy.

Card Decks and Method Kits

• New facilitators or non-facilitators 
• Approaching difficult subjects with a simple, creative framework
• Engaging those with varied learning styles

Card decks and method kids are great tools for those new to facilitation or for whom facilitation is not the primary role. Card decks such as the emotional culture deck can be used for complete workshops and in many cases, can be used right out of the box. Methodkit has a variety of kits designed for scenarios ranging from personal development through to personas and global challenges so you can find the right deck for your particular needs.

Having an easy to use framework that encourages creativity or a new approach can take some of the friction or planning difficulties out of the workshop process and energize a team in any setting. Simplicity is the key with these methods. By ensuring everyone on your team can get involved and engage with the process as quickly as possible can really contribute to the success of your problem solving strategy.

Source external advice

Looking to peers, experts and external facilitators can be a great way of approaching the problem solving process. Your team may not have the necessary expertise, insights of experience to tackle some issues, or you might simply benefit from a fresh perspective. Some problems may require bringing together an entire team, and coaching managers or team members individually might be the right approach. Remember that not all problems are best resolved in the same manner.

If you’re a solo entrepreneur, peer groups, coaches and mentors can also be invaluable at not only solving specific business problems, but in providing a support network for resolving future challenges. One great approach is to join a Mastermind Group and link up with like-minded individuals and all grow together. Remember that however you approach the sourcing of external advice, do so thoughtfully, respectfully and honestly. Reciprocate where you can and prepare to be surprised by just how kind and helpful your peers can be!

Mastermind Group

• Solo entrepreneurs or small teams with low capacity
• Peer learning and gaining outside expertise
• Getting multiple external points of view quickly

Problem solving in large organizations with lots of skilled team members is one thing, but how about if you work for yourself or in a very small team without the capacity to get the most from a design sprint or LEGO Serious Play session? 

A mastermind group – sometimes known as a peer advisory board – is where a group of people come together to support one another in their own goals, challenges, and businesses. Each participant comes to the group with their own purpose and the other members of the group will help them create solutions, brainstorm ideas, and support one another. 

Mastermind groups are very effective in creating an energized, supportive atmosphere that can deliver meaningful results. Learning from peers from outside of your organization or industry can really help unlock new ways of thinking and drive growth. Access to the experience and skills of your peers can be invaluable in helping fill the gaps in your own ability, particularly in young companies.

A mastermind group is a great solution for solo entrepreneurs, small teams, or for organizations that feel that external expertise or fresh perspectives will be beneficial for them. It is worth noting that Mastermind groups are often only as good as the participants and what they can bring to the group. Participants need to be committed, engaged and understand how to work in this context. 

Coaching and mentoring

• Focused learning and development
• Filling skills gaps
• Working on a range of challenges over time

Receiving advice from a business coach or building a mentor/mentee relationship can be an effective way of resolving certain challenges. The one-to-one format of most coaching and mentor relationships can really help solve the challenges those individuals are having and benefit the organization as a result.

A great mentor can be invaluable when it comes to spotting potential problems before they arise and coming to understand a mentee very well has a host of other business benefits. You might run an internal mentorship program to help develop your team’s problem solving skills and strategies or as part of a large learning and development program. External coaches can also be an important part of your problem solving strategy, filling skills gaps for your management team or helping with specific business issues. 

Now we’ve explored the problem solving process and the steps you will want to go through in order to have an effective session, let’s look at the skills you and your team need to be more effective problem solvers.

Problem solving skills are highly sought after, whatever industry or team you work in. Organizations are keen to employ people who are able to approach problems thoughtfully and find strong, realistic solutions. Whether you are a facilitator , a team leader or a developer, being an effective problem solver is a skill you’ll want to develop.

Problem solving skills form a whole suite of techniques and approaches that an individual uses to not only identify problems but to discuss them productively before then developing appropriate solutions.

Here are some of the most important problem solving skills everyone from executives to junior staff members should learn. We’ve also included an activity or exercise from the SessionLab library that can help you and your team develop that skill. 

If you’re running a workshop or training session to try and improve problem solving skills in your team, try using these methods to supercharge your process!

Active listening

Active listening is one of the most important skills anyone who works with people can possess. In short, active listening is a technique used to not only better understand what is being said by an individual, but also to be more aware of the underlying message the speaker is trying to convey. When it comes to problem solving, active listening is integral for understanding the position of every participant and to clarify the challenges, ideas and solutions they bring to the table.

Some active listening skills include:

• Paying complete attention to the speaker.
• Removing distractions.
• Avoid interruption.
• Taking the time to fully understand before preparing a rebuttal.
• Responding respectfully and appropriately.
• Demonstrate attentiveness and positivity with an open posture, making eye contact with the speaker, smiling and nodding if appropriate. Show that you are listening and encourage them to continue.
• Be aware of and respectful of feelings. Judge the situation and respond appropriately. You can disagree without being disrespectful.   
• Observe body language. 
• Paraphrase what was said in your own words, either mentally or verbally.
• Remain neutral. 
• Reflect and take a moment before responding.
• Ask deeper questions based on what is said and clarify points where necessary.   

Active Listening   #hyperisland   #skills   #active listening   #remote-friendly   This activity supports participants to reflect on a question and generate their own solutions using simple principles of active listening and peer coaching. It’s an excellent introduction to active listening but can also be used with groups that are already familiar with it. Participants work in groups of three and take turns being: “the subject”, the listener, and the observer.

Analytical skills

All problem solving models require strong analytical skills, particularly during the beginning of the process and when it comes to analyzing how solutions have performed.

Analytical skills are primarily focused on performing an effective analysis by collecting, studying and parsing data related to a problem or opportunity. 

It often involves spotting patterns, being able to see things from different perspectives and using observable facts and data to make suggestions or produce insight. 

Analytical skills are also important at every stage of the problem solving process and by having these skills, you can ensure that any ideas or solutions you create or backed up analytically and have been sufficiently thought out.

Nine Whys   #innovation   #issue analysis   #liberating structures   With breathtaking simplicity, you can rapidly clarify for individuals and a group what is essentially important in their work. You can quickly reveal when a compelling purpose is missing in a gathering and avoid moving forward without clarity. When a group discovers an unambiguous shared purpose, more freedom and more responsibility are unleashed. You have laid the foundation for spreading and scaling innovations with fidelity.

Collaboration

Trying to solve problems on your own is difficult. Being able to collaborate effectively, with a free exchange of ideas, to delegate and be a productive member of a team is hugely important to all problem solving strategies.

Remember that whatever your role, collaboration is integral, and in a problem solving process, you are all working together to find the best solution for everyone. 

Marshmallow challenge with debriefing   #teamwork   #team   #leadership   #collaboration   In eighteen minutes, teams must build the tallest free-standing structure out of 20 sticks of spaghetti, one yard of tape, one yard of string, and one marshmallow. The marshmallow needs to be on top. The Marshmallow Challenge was developed by Tom Wujec, who has done the activity with hundreds of groups around the world. Visit the Marshmallow Challenge website for more information. This version has an extra debriefing question added with sample questions focusing on roles within the team.

Communication  

Being an effective communicator means being empathetic, clear and succinct, asking the right questions, and demonstrating active listening skills throughout any discussion or meeting. 

In a problem solving setting, you need to communicate well in order to progress through each stage of the process effectively. As a team leader, it may also fall to you to facilitate communication between parties who may not see eye to eye. Effective communication also means helping others to express themselves and be heard in a group.

Bus Trip   #feedback   #communication   #appreciation   #closing   #thiagi   #team   This is one of my favourite feedback games. I use Bus Trip at the end of a training session or a meeting, and I use it all the time. The game creates a massive amount of energy with lots of smiles, laughs, and sometimes even a teardrop or two.

Creative problem solving skills can be some of the best tools in your arsenal. Thinking creatively, being able to generate lots of ideas and come up with out of the box solutions is useful at every step of the process. 

The kinds of problems you will likely discuss in a problem solving workshop are often difficult to solve, and by approaching things in a fresh, creative manner, you can often create more innovative solutions.

Having practical creative skills is also a boon when it comes to problem solving. If you can help create quality design sketches and prototypes in record time, it can help bring a team to alignment more quickly or provide a base for further iteration.

The paper clip method   #sharing   #creativity   #warm up   #idea generation   #brainstorming   The power of brainstorming. A training for project leaders, creativity training, and to catalyse getting new solutions.

Critical thinking

Critical thinking is one of the fundamental problem solving skills you’ll want to develop when working on developing solutions. Critical thinking is the ability to analyze, rationalize and evaluate while being aware of personal bias, outlying factors and remaining open-minded.

Defining and analyzing problems without deploying critical thinking skills can mean you and your team go down the wrong path. Developing solutions to complex issues requires critical thinking too – ensuring your team considers all possibilities and rationally evaluating them. 

Agreement-Certainty Matrix   #issue analysis   #liberating structures   #problem solving   You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic .  A problem is simple when it can be solved reliably with practices that are easy to duplicate.  It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably.  A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail.  Chaotic is when the context is too turbulent to identify a path forward.  A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.”  The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.

Data analysis 

Though it shares lots of space with general analytical skills, data analysis skills are something you want to cultivate in their own right in order to be an effective problem solver.

Being good at data analysis doesn’t just mean being able to find insights from data, but also selecting the appropriate data for a given issue, interpreting it effectively and knowing how to model and present that data. Depending on the problem at hand, it might also include a working knowledge of specific data analysis tools and procedures. 

Having a solid grasp of data analysis techniques is useful if you’re leading a problem solving workshop but if you’re not an expert, don’t worry. Bring people into the group who has this skill set and help your team be more effective as a result.

Decision making

All problems need a solution and all solutions require that someone make the decision to implement them. Without strong decision making skills, teams can become bogged down in discussion and less effective as a result. 

Making decisions is a key part of the problem solving process. It’s important to remember that decision making is not restricted to the leadership team. Every staff member makes decisions every day and developing these skills ensures that your team is able to solve problems at any scale. Remember that making decisions does not mean leaping to the first solution but weighing up the options and coming to an informed, well thought out solution to any given problem that works for the whole team.

Lightning Decision Jam (LDJ)   #action   #decision making   #problem solving   #issue analysis   #innovation   #design   #remote-friendly   The problem with anything that requires creative thinking is that it’s easy to get lost—lose focus and fall into the trap of having useless, open-ended, unstructured discussions. Here’s the most effective solution I’ve found: Replace all open, unstructured discussion with a clear process. What to use this exercise for: Anything which requires a group of people to make decisions, solve problems or discuss challenges. It’s always good to frame an LDJ session with a broad topic, here are some examples: The conversion flow of our checkout Our internal design process How we organise events Keeping up with our competition Improving sales flow

Dependability

Most complex organizational problems require multiple people to be involved in delivering the solution. Ensuring that the team and organization can depend on you to take the necessary actions and communicate where necessary is key to ensuring problems are solved effectively.

Being dependable also means working to deadlines and to brief. It is often a matter of creating trust in a team so that everyone can depend on one another to complete the agreed actions in the agreed time frame so that the team can move forward together. Being undependable can create problems of friction and can limit the effectiveness of your solutions so be sure to bear this in mind throughout a project. 

Team Purpose & Culture   #team   #hyperisland   #culture   #remote-friendly   This is an essential process designed to help teams define their purpose (why they exist) and their culture (how they work together to achieve that purpose). Defining these two things will help any team to be more focused and aligned. With support of tangible examples from other companies, the team members work as individuals and a group to codify the way they work together. The goal is a visual manifestation of both the purpose and culture that can be put up in the team’s work space.

Emotional intelligence

Emotional intelligence is an important skill for any successful team member, whether communicating internally or with clients or users. In the problem solving process, emotional intelligence means being attuned to how people are feeling and thinking, communicating effectively and being self-aware of what you bring to a room. 

There are often differences of opinion when working through problem solving processes, and it can be easy to let things become impassioned or combative. Developing your emotional intelligence means being empathetic to your colleagues and managing your own emotions throughout the problem and solution process. Be kind, be thoughtful and put your points across care and attention. 

Being emotionally intelligent is a skill for life and by deploying it at work, you can not only work efficiently but empathetically. Check out the emotional culture workshop template for more!

Facilitation

As we’ve clarified in our facilitation skills post, facilitation is the art of leading people through processes towards agreed-upon objectives in a manner that encourages participation, ownership, and creativity by all those involved. While facilitation is a set of interrelated skills in itself, the broad definition of facilitation can be invaluable when it comes to problem solving. Leading a team through a problem solving process is made more effective if you improve and utilize facilitation skills – whether you’re a manager, team leader or external stakeholder.

The Six Thinking Hats   #creative thinking   #meeting facilitation   #problem solving   #issue resolution   #idea generation   #conflict resolution   The Six Thinking Hats are used by individuals and groups to separate out conflicting styles of thinking. They enable and encourage a group of people to think constructively together in exploring and implementing change, rather than using argument to fight over who is right and who is wrong.

Flexibility 

Being flexible is a vital skill when it comes to problem solving. This does not mean immediately bowing to pressure or changing your opinion quickly: instead, being flexible is all about seeing things from new perspectives, receiving new information and factoring it into your thought process.

Flexibility is also important when it comes to rolling out solutions. It might be that other organizational projects have greater priority or require the same resources as your chosen solution. Being flexible means understanding needs and challenges across the team and being open to shifting or arranging your own schedule as necessary. Again, this does not mean immediately making way for other projects. It’s about articulating your own needs, understanding the needs of others and being able to come to a meaningful compromise.

The Creativity Dice   #creativity   #problem solving   #thiagi   #issue analysis   Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.

Working in any group can lead to unconscious elements of groupthink or situations in which you may not wish to be entirely honest. Disagreeing with the opinions of the executive team or wishing to save the feelings of a coworker can be tricky to navigate, but being honest is absolutely vital when to comes to developing effective solutions and ensuring your voice is heard. 

Remember that being honest does not mean being brutally candid. You can deliver your honest feedback and opinions thoughtfully and without creating friction by using other skills such as emotional intelligence. 

Explore your Values   #hyperisland   #skills   #values   #remote-friendly   Your Values is an exercise for participants to explore what their most important values are. It’s done in an intuitive and rapid way to encourage participants to follow their intuitive feeling rather than over-thinking and finding the “correct” values. It is a good exercise to use to initiate reflection and dialogue around personal values.

Initiative 

The problem solving process is multi-faceted and requires different approaches at certain points of the process. Taking initiative to bring problems to the attention of the team, collect data or lead the solution creating process is always valuable. You might even roadtest your own small scale solutions or brainstorm before a session. Taking initiative is particularly effective if you have good deal of knowledge in that area or have ownership of a particular project and want to get things kickstarted.

That said, be sure to remember to honor the process and work in service of the team. If you are asked to own one part of the problem solving process and you don’t complete that task because your initiative leads you to work on something else, that’s not an effective method of solving business challenges.

15% Solutions   #action   #liberating structures   #remote-friendly   You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference.  15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change.  With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.

Impartiality

A particularly useful problem solving skill for product owners or managers is the ability to remain impartial throughout much of the process. In practice, this means treating all points of view and ideas brought forward in a meeting equally and ensuring that your own areas of interest or ownership are not favored over others. 

There may be a stage in the process where a decision maker has to weigh the cost and ROI of possible solutions against the company roadmap though even then, ensuring that the decision made is based on merit and not personal opinion. 

Empathy map   #frame insights   #create   #design   #issue analysis   An empathy map is a tool to help a design team to empathize with the people they are designing for. You can make an empathy map for a group of people or for a persona. To be used after doing personas when more insights are needed.

Being a good leader means getting a team aligned, energized and focused around a common goal. In the problem solving process, strong leadership helps ensure that the process is efficient, that any conflicts are resolved and that a team is managed in the direction of success.

It’s common for managers or executives to assume this role in a problem solving workshop, though it’s important that the leader maintains impartiality and does not bulldoze the group in a particular direction. Remember that good leadership means working in service of the purpose and team and ensuring the workshop is a safe space for employees of any level to contribute. Take a look at our leadership games and activities post for more exercises and methods to help improve leadership in your organization.

Leadership Pizza   #leadership   #team   #remote-friendly   This leadership development activity offers a self-assessment framework for people to first identify what skills, attributes and attitudes they find important for effective leadership, and then assess their own development and initiate goal setting.

In the context of problem solving, mediation is important in keeping a team engaged, happy and free of conflict. When leading or facilitating a problem solving workshop, you are likely to run into differences of opinion. Depending on the nature of the problem, certain issues may be brought up that are emotive in nature. 

Being an effective mediator means helping those people on either side of such a divide are heard, listen to one another and encouraged to find common ground and a resolution. Mediating skills are useful for leaders and managers in many situations and the problem solving process is no different.

Conflict Responses   #hyperisland   #team   #issue resolution   A workshop for a team to reflect on past conflicts, and use them to generate guidelines for effective conflict handling. The workshop uses the Thomas-Killman model of conflict responses to frame a reflective discussion. Use it to open up a discussion around conflict with a team.

Planning 

Solving organizational problems is much more effective when following a process or problem solving model. Planning skills are vital in order to structure, deliver and follow-through on a problem solving workshop and ensure your solutions are intelligently deployed.

Planning skills include the ability to organize tasks and a team, plan and design the process and take into account any potential challenges. Taking the time to plan carefully can save time and frustration later in the process and is valuable for ensuring a team is positioned for success.

3 Action Steps   #hyperisland   #action   #remote-friendly   This is a small-scale strategic planning session that helps groups and individuals to take action toward a desired change. It is often used at the end of a workshop or programme. The group discusses and agrees on a vision, then creates some action steps that will lead them towards that vision. The scope of the challenge is also defined, through discussion of the helpful and harmful factors influencing the group.

Prioritization

As organisations grow, the scale and variation of problems they face multiplies. Your team or is likely to face numerous challenges in different areas and so having the skills to analyze and prioritize becomes very important, particularly for those in leadership roles.

A thorough problem solving process is likely to deliver multiple solutions and you may have several different problems you wish to solve simultaneously. Prioritization is the ability to measure the importance, value, and effectiveness of those possible solutions and choose which to enact and in what order. The process of prioritization is integral in ensuring the biggest challenges are addressed with the most impactful solutions.

Impact and Effort Matrix   #gamestorming   #decision making   #action   #remote-friendly   In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.

Project management

Some problem solving skills are utilized in a workshop or ideation phases, while others come in useful when it comes to decision making. Overseeing an entire problem solving process and ensuring its success requires strong project management skills. 

While project management incorporates many of the other skills listed here, it is important to note the distinction of considering all of the factors of a project and managing them successfully. Being able to negotiate with stakeholders, manage tasks, time and people, consider costs and ROI, and tie everything together is massively helpful when going through the problem solving process. 

Record keeping

Working out meaningful solutions to organizational challenges is only one part of the process.  Thoughtfully documenting and keeping records of each problem solving step for future consultation is important in ensuring efficiency and meaningful change. 

For example, some problems may be lower priority than others but can be revisited in the future. If the team has ideated on solutions and found some are not up to the task, record those so you can rule them out and avoiding repeating work. Keeping records of the process also helps you improve and refine your problem solving model next time around!

Personal Kanban   #gamestorming   #action   #agile   #project planning   Personal Kanban is a tool for organizing your work to be more efficient and productive. It is based on agile methods and principles.

Research skills

Conducting research to support both the identification of problems and the development of appropriate solutions is important for an effective process. Knowing where to go to collect research, how to conduct research efficiently, and identifying pieces of research are relevant are all things a good researcher can do well. 

In larger groups, not everyone has to demonstrate this ability in order for a problem solving workshop to be effective. That said, having people with research skills involved in the process, particularly if they have existing area knowledge, can help ensure the solutions that are developed with data that supports their intention. Remember that being able to deliver the results of research efficiently and in a way the team can easily understand is also important. The best data in the world is only as effective as how it is delivered and interpreted.

Customer experience map   #ideation   #concepts   #research   #design   #issue analysis   #remote-friendly   Customer experience mapping is a method of documenting and visualizing the experience a customer has as they use the product or service. It also maps out their responses to their experiences. To be used when there is a solution (even in a conceptual stage) that can be analyzed.

Risk management

Managing risk is an often overlooked part of the problem solving process. Solutions are often developed with the intention of reducing exposure to risk or solving issues that create risk but sometimes, great solutions are more experimental in nature and as such, deploying them needs to be carefully considered. 

Managing risk means acknowledging that there may be risks associated with more out of the box solutions or trying new things, but that this must be measured against the possible benefits and other organizational factors. 

Be informed, get the right data and stakeholders in the room and you can appropriately factor risk into your decision making process. 

Decisions, Decisions…   #communication   #decision making   #thiagi   #action   #issue analysis   When it comes to decision-making, why are some of us more prone to take risks while others are risk-averse? One explanation might be the way the decision and options were presented.  This exercise, based on Kahneman and Tversky’s classic study , illustrates how the framing effect influences our judgement and our ability to make decisions . The participants are divided into two groups. Both groups are presented with the same problem and two alternative programs for solving them. The two programs both have the same consequences but are presented differently. The debriefing discussion examines how the framing of the program impacted the participant’s decision.

Team-building 

No single person is as good at problem solving as a team. Building an effective team and helping them come together around a common purpose is one of the most important problem solving skills, doubly so for leaders. By bringing a team together and helping them work efficiently, you pave the way for team ownership of a problem and the development of effective solutions. 

In a problem solving workshop, it can be tempting to jump right into the deep end, though taking the time to break the ice, energize the team and align them with a game or exercise will pay off over the course of the day.

Remember that you will likely go through the problem solving process multiple times over an organization’s lifespan and building a strong team culture will make future problem solving more effective. It’s also great to work with people you know, trust and have fun with. Working on team building in and out of the problem solving process is a hallmark of successful teams that can work together to solve business problems.

9 Dimensions Team Building Activity   #ice breaker   #teambuilding   #team   #remote-friendly   9 Dimensions is a powerful activity designed to build relationships and trust among team members. There are 2 variations of this icebreaker. The first version is for teams who want to get to know each other better. The second version is for teams who want to explore how they are working together as a team.

Time management 

The problem solving process is designed to lead a team from identifying a problem through to delivering a solution and evaluating its effectiveness. Without effective time management skills or timeboxing of tasks, it can be easy for a team to get bogged down or be inefficient.

By using a problem solving model and carefully designing your workshop, you can allocate time efficiently and trust that the process will deliver the results you need in a good timeframe.

Time management also comes into play when it comes to rolling out solutions, particularly those that are experimental in nature. Having a clear timeframe for implementing and evaluating solutions is vital for ensuring their success and being able to pivot if necessary.

Improving your skills at problem solving is often a career-long pursuit though there are methods you can use to make the learning process more efficient and to supercharge your problem solving skillset.

Remember that the skills you need to be a great problem solver have a large overlap with those skills you need to be effective in any role. Investing time and effort to develop your active listening or critical thinking skills is valuable in any context. Here are 7 ways to improve your problem solving skills.

Share best practices

Remember that your team is an excellent source of skills, wisdom, and techniques and that you should all take advantage of one another where possible. Best practices that one team has for solving problems, conducting research or making decisions should be shared across the organization. If you have in-house staff that have done active listening training or are data analysis pros, have them lead a training session. 

Your team is one of your best resources. Create space and internal processes for the sharing of skills so that you can all grow together. 

Ask for help and attend training

Once you’ve figured out you have a skills gap, the next step is to take action to fill that skills gap. That might be by asking your superior for training or coaching, or liaising with team members with that skill set. You might even attend specialized training for certain skills – active listening or critical thinking, for example, are business-critical skills that are regularly offered as part of a training scheme.

Whatever method you choose, remember that taking action of some description is necessary for growth. Whether that means practicing, getting help, attending training or doing some background reading, taking active steps to improve your skills is the way to go.

Learn a process 

Problem solving can be complicated, particularly when attempting to solve large problems for the first time. Using a problem solving process helps give structure to your problem solving efforts and focus on creating outcomes, rather than worrying about the format. 

Tools such as the seven-step problem solving process above are effective because not only do they feature steps that will help a team solve problems, they also develop skills along the way. Each step asks for people to engage with the process using different skills and in doing so, helps the team learn and grow together. Group processes of varying complexity and purpose can also be found in the SessionLab library of facilitation techniques . Using a tried and tested process and really help ease the learning curve for both those leading such a process, as well as those undergoing the purpose.

Effective teams make decisions about where they should and shouldn’t expend additional effort. By using a problem solving process, you can focus on the things that matter, rather than stumbling towards a solution haphazardly. 

Create a feedback loop

Some skills gaps are more obvious than others. It’s possible that your perception of your active listening skills differs from those of your colleagues. 

It’s valuable to create a system where team members can provide feedback in an ordered and friendly manner so they can all learn from one another. Only by identifying areas of improvement can you then work to improve them. 

Remember that feedback systems require oversight and consideration so that they don’t turn into a place to complain about colleagues. Design the system intelligently so that you encourage the creation of learning opportunities, rather than encouraging people to list their pet peeves.

While practice might not make perfect, it does make the problem solving process easier. If you are having trouble with critical thinking, don’t shy away from doing it. Get involved where you can and stretch those muscles as regularly as possible. 

Problem solving skills come more naturally to some than to others and that’s okay. Take opportunities to get involved and see where you can practice your skills in situations outside of a workshop context. Try collaborating in other circumstances at work or conduct data analysis on your own projects. You can often develop those skills you need for problem solving simply by doing them. Get involved!

Use expert exercises and methods

Learn from the best. Our library of 700+ facilitation techniques is full of activities and methods that help develop the skills you need to be an effective problem solver. Check out our templates to see how to approach problem solving and other organizational challenges in a structured and intelligent manner.

There is no single approach to improving problem solving skills, but by using the techniques employed by others you can learn from their example and develop processes that have seen proven results. 

Try new ways of thinking and change your mindset

Using tried and tested exercises that you know well can help deliver results, but you do run the risk of missing out on the learning opportunities offered by new approaches. As with the problem solving process, changing your mindset can remove blockages and be used to develop your problem solving skills.

Most teams have members with mixed skill sets and specialties. Mix people from different teams and share skills and different points of view. Teach your customer support team how to use design thinking methods or help your developers with conflict resolution techniques. Try switching perspectives with facilitation techniques like Flip It! or by using new problem solving methodologies or models. Give design thinking, liberating structures or lego serious play a try if you want to try a new approach. You will find that framing problems in new ways and using existing skills in new contexts can be hugely useful for personal development and improving your skillset. It’s also a lot of fun to try new things. Give it a go!

Encountering business challenges and needing to find appropriate solutions is not unique to your organization. Lots of very smart people have developed methods, theories and approaches to help develop problem solving skills and create effective solutions. Learn from them!

Books like The Art of Thinking Clearly , Think Smarter, or Thinking Fast, Thinking Slow are great places to start, though it’s also worth looking at blogs related to organizations facing similar problems to yours, or browsing for success stories. Seeing how Dropbox massively increased growth and working backward can help you see the skills or approach you might be lacking to solve that same problem. Learning from others by reading their stories or approaches can be time-consuming but ultimately rewarding.

A tired, distracted mind is not in the best position to learn new skills. It can be tempted to burn the candle at both ends and develop problem solving skills outside of work. Absolutely use your time effectively and take opportunities for self-improvement, though remember that rest is hugely important and that without letting your brain rest, you cannot be at your most effective. 

Creating distance between yourself and the problem you might be facing can also be useful. By letting an idea sit, you can find that a better one presents itself or you can develop it further. Take regular breaks when working and create a space for downtime. Remember that working smarter is preferable to working harder and that self-care is important for any effective learning or improvement process.

Want to design better group processes?

Over to you

Now we’ve explored some of the key problem solving skills and the problem solving steps necessary for an effective process, you’re ready to begin developing more effective solutions and leading problem solving workshops.

Need more inspiration? Check out our post on problem solving activities you can use when guiding a group towards a great solution in your next workshop or meeting. Have questions? Did you have a great problem solving technique you use with your team? Get in touch in the comments below. We’d love to chat!

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10 Problem-solving strategies to turn challenges on their head

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What is an example of problem-solving?

What are the 5 steps to problem-solving, 10 effective problem-solving strategies, what skills do efficient problem solvers have, how to improve your problem-solving skills.

Problems come in all shapes and sizes — from workplace conflict to budget cuts.

Creative problem-solving is one of the most in-demand skills in all roles and industries. It can boost an organization’s human capital and give it a competitive edge. 

Problem-solving strategies are ways of approaching problems that can help you look beyond the obvious answers and find the best solution to your problem . 

Let’s take a look at a five-step problem-solving process and how to combine it with proven problem-solving strategies. This will give you the tools and skills to solve even your most complex problems.

Good problem-solving is an essential part of the decision-making process . To see what a problem-solving process might look like in real life, let’s take a common problem for SaaS brands — decreasing customer churn rates.

To solve this problem, the company must first identify it. In this case, the problem is that the churn rate is too high. 

Next, they need to identify the root causes of the problem. This could be anything from their customer service experience to their email marketing campaigns. If there are several problems, they will need a separate problem-solving process for each one. 

Let’s say the problem is with email marketing — they’re not nurturing existing customers. Now that they’ve identified the problem, they can start using problem-solving strategies to look for solutions. 

This might look like coming up with special offers, discounts, or bonuses for existing customers. They need to find ways to remind them to use their products and services while providing added value. This will encourage customers to keep paying their monthly subscriptions.

They might also want to add incentives, such as access to a premium service at no extra cost after 12 months of membership. They could publish blog posts that help their customers solve common problems and share them as an email newsletter.

The company should set targets and a time frame in which to achieve them. This will allow leaders to measure progress and identify which actions yield the best results.

Perhaps you’ve got a problem you need to tackle. Or maybe you want to be prepared the next time one arises. Either way, it’s a good idea to get familiar with the five steps of problem-solving. 

Use this step-by-step problem-solving method with the strategies in the following section to find possible solutions to your problem.

1. Identify the problem

The first step is to know which problem you need to solve. Then, you need to find the root cause of the problem. 

The best course of action is to gather as much data as possible, speak to the people involved, and separate facts from opinions. 

Once this is done, formulate a statement that describes the problem. Use rational persuasion to make sure your team agrees .

2. Break the problem down 

Identifying the problem allows you to see which steps need to be taken to solve it. 

First, break the problem down into achievable blocks. Then, use strategic planning to set a time frame in which to solve the problem and establish a timeline for the completion of each stage.

3. Generate potential solutions

At this stage, the aim isn’t to evaluate possible solutions but to generate as many ideas as possible. 

Encourage your team to use creative thinking and be patient — the best solution may not be the first or most obvious one.

Use one or more of the different strategies in the following section to help come up with solutions — the more creative, the better.

4. Evaluate the possible solutions

Once you’ve generated potential solutions, narrow them down to a shortlist. Then, evaluate the options on your shortlist. 

There are usually many factors to consider. So when evaluating a solution, ask yourself the following questions:

• Will my team be on board with the proposition?
• Does the solution align with organizational goals ?
• Is the solution likely to achieve the desired outcomes?
• Is the solution realistic and possible with current resources and constraints?
• Will the solution solve the problem without causing additional unintended problems?

5. Implement and monitor the solutions

Once you’ve identified your solution and got buy-in from your team, it’s time to implement it. 

But the work doesn’t stop there. You need to monitor your solution to see whether it actually solves your problem. 

Request regular feedback from the team members involved and have a monitoring and evaluation plan in place to measure progress.

If the solution doesn’t achieve your desired results, start this step-by-step process again.

There are many different ways to approach problem-solving. Each is suitable for different types of problems. 

The most appropriate problem-solving techniques will depend on your specific problem. You may need to experiment with several strategies before you find a workable solution.

Here are 10 effective problem-solving strategies for you to try:

• Use a solution that worked before
• Brainstorming
• Work backward
• Use the Kipling method
• Draw the problem
• Use trial and error
• Sleep on it
• Get advice from your peers
• Use the Pareto principle
• Add successful solutions to your toolkit

Let’s break each of these down.

1. Use a solution that worked before

It might seem obvious, but if you’ve faced similar problems in the past, look back to what worked then. See if any of the solutions could apply to your current situation and, if so, replicate them.

2. Brainstorming

The more people you enlist to help solve the problem, the more potential solutions you can come up with.

Use different brainstorming techniques to workshop potential solutions with your team. They’ll likely bring something you haven’t thought of to the table.

3. Work backward

Working backward is a way to reverse engineer your problem. Imagine your problem has been solved, and make that the starting point.

Then, retrace your steps back to where you are now. This can help you see which course of action may be most effective.

4. Use the Kipling method

This is a method that poses six questions based on Rudyard Kipling’s poem, “ I Keep Six Honest Serving Men .” 

• What is the problem?
• Why is the problem important?
• When did the problem arise, and when does it need to be solved?
• How did the problem happen?
• Where is the problem occurring?
• Who does the problem affect?

Answering these questions can help you identify possible solutions.

5. Draw the problem

Sometimes it can be difficult to visualize all the components and moving parts of a problem and its solution. Drawing a diagram can help.

This technique is particularly helpful for solving process-related problems. For example, a product development team might want to decrease the time they take to fix bugs and create new iterations. Drawing the processes involved can help you see where improvements can be made.

6. Use trial-and-error

A trial-and-error approach can be useful when you have several possible solutions and want to test them to see which one works best.

7. Sleep on it

Finding the best solution to a problem is a process. Remember to take breaks and get enough rest . Sometimes, a walk around the block can bring inspiration, but you should sleep on it if possible.

A good night’s sleep helps us find creative solutions to problems. This is because when you sleep, your brain sorts through the day’s events and stores them as memories. This enables you to process your ideas at a subconscious level. 

If possible, give yourself a few days to develop and analyze possible solutions. You may find you have greater clarity after sleeping on it. Your mind will also be fresh, so you’ll be able to make better decisions.

8. Get advice from your peers

Getting input from a group of people can help you find solutions you may not have thought of on your own. 

For solo entrepreneurs or freelancers, this might look like hiring a coach or mentor or joining a mastermind group. 

For leaders , it might be consulting other members of the leadership team or working with a business coach .

It’s important to recognize you might not have all the skills, experience, or knowledge necessary to find a solution alone. 

9. Use the Pareto principle

The Pareto principle — also known as the 80/20 rule — can help you identify possible root causes and potential solutions for your problems.

Although it’s not a mathematical law, it’s a principle found throughout many aspects of business and life. For example, 20% of the sales reps in a company might close 80% of the sales. 

You may be able to narrow down the causes of your problem by applying the Pareto principle. This can also help you identify the most appropriate solutions.

10. Add successful solutions to your toolkit

Every situation is different, and the same solutions might not always work. But by keeping a record of successful problem-solving strategies, you can build up a solutions toolkit. 

These solutions may be applicable to future problems. Even if not, they may save you some of the time and work needed to come up with a new solution.

Improving problem-solving skills is essential for professional development — both yours and your team’s. Here are some of the key skills of effective problem solvers:

• Critical thinking and analytical skills
• Communication skills , including active listening
• Decision-making
• Planning and prioritization
• Emotional intelligence , including empathy and emotional regulation
• Time management
• Data analysis
• Research skills
• Project management

And they see problems as opportunities. Everyone is born with problem-solving skills. But accessing these abilities depends on how we view problems. Effective problem-solvers see problems as opportunities to learn and improve.

Ready to work on your problem-solving abilities? Get started with these seven tips.

1. Build your problem-solving skills

One of the best ways to improve your problem-solving skills is to learn from experts. Consider enrolling in organizational training , shadowing a mentor , or working with a coach .

2. Practice

Practice using your new problem-solving skills by applying them to smaller problems you might encounter in your daily life. 

Alternatively, imagine problematic scenarios that might arise at work and use problem-solving strategies to find hypothetical solutions.

3. Don’t try to find a solution right away

Often, the first solution you think of to solve a problem isn’t the most appropriate or effective.

Instead of thinking on the spot, give yourself time and use one or more of the problem-solving strategies above to activate your creative thinking. 

4. Ask for feedback

Receiving feedback is always important for learning and growth. Your perception of your problem-solving skills may be different from that of your colleagues. They can provide insights that help you improve. 

5. Learn new approaches and methodologies

There are entire books written about problem-solving methodologies if you want to take a deep dive into the subject. 

We recommend starting with “ Fixed — How to Perfect the Fine Art of Problem Solving ” by Amy E. Herman. 

6. Experiment

Tried-and-tested problem-solving techniques can be useful. However, they don’t teach you how to innovate and develop your own problem-solving approaches. 

Sometimes, an unconventional approach can lead to the development of a brilliant new idea or strategy. So don’t be afraid to suggest your most “out there” ideas.

7. Analyze the success of your competitors

Do you have competitors who have already solved the problem you’re facing? Look at what they did, and work backward to solve your own problem. 

For example, Netflix started in the 1990s as a DVD mail-rental company. Its main competitor at the time was Blockbuster. 

But when streaming became the norm in the early 2000s, both companies faced a crisis. Netflix innovated, unveiling its streaming service in 2007. 

If Blockbuster had followed Netflix’s example, it might have survived. Instead, it declared bankruptcy in 2010.

Use problem-solving strategies to uplevel your business

When facing a problem, it’s worth taking the time to find the right solution. 

Otherwise, we risk either running away from our problems or headlong into solutions. When we do this, we might miss out on other, better options.

Use the problem-solving strategies outlined above to find innovative solutions to your business’ most perplexing problems.

If you’re ready to take problem-solving to the next level, request a demo with BetterUp . Our expert coaches specialize in helping teams develop and implement strategies that work.

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Maximize your time and productivity with strategies from our expert coaches.

Elizabeth Perry, ACC

Elizabeth Perry is a Coach Community Manager at BetterUp. She uses strategic engagement strategies to cultivate a learning community across a global network of Coaches through in-person and virtual experiences, technology-enabled platforms, and strategic coaching industry partnerships. With over 3 years of coaching experience and a certification in transformative leadership and life coaching from Sofia University, Elizabeth leverages transpersonal psychology expertise to help coaches and clients gain awareness of their behavioral and thought patterns, discover their purpose and passions, and elevate their potential. She is a lifelong student of psychology, personal growth, and human potential as well as an ICF-certified ACC transpersonal life and leadership Coach.

8 creative solutions to your most challenging problems

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Control Strategies used in Artificial Intelligence to reach the solution

Control Strategy in Artificial Intelligence scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space. It helps us to decide which rule has to apply next without getting stuck at any point. These rules decide the way we approach the problem and how quickly it is solved and even whether a problem is finally solved.

Control Strategy helps to find the solution when there is more than one rule or fewer rules for finding the solution at each point in problem space. A good Control strategy has two main characteristics:

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Control Strategy should cause Motion

Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.

Control strategy should be Systematic

Though the strategy applied should create the motion but if do not follow some systematic strategy than we are likely to reach the same state number of times before reaching the solution which increases the number of steps.  Taking care of only first strategy we may go through particular useless sequences of operators several times.  Control Strategy should be systematic implies a need for global motion (over the course of several steps) as well as for local motion (over the course of single step).

Breadth-First Search: It searches along the breadth and follows first-in-first-out queue data structure approach.  It will start to scan node A first and then B-C-D-E-F.

Depth-First Search:  It searches along the depth and follows the stack approach. The sequence for scanning nodes will be A-B-D-E-C-F, it scans all the sub-nodes of parent nodes and then moves to another node.

Widely used Control Strategies are Breadth-First Search, Depth-First Search, Generate and Test, Hill-Climbing, Best-first search, Problem Reduction and many more.

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• General Problem-solving Techniques

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Problem-solving and diagnostic strategies in control system analysis.

• Learn Fundamental Principles, not Procedures
• Active Reading and Outlining Documentation
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A variety of problem-solving techniques have been presented for students over the years which are all helpful in tackling problems both in the classroom and in the real world. Several of these techniques are presented here in this section.

Identifying and classifying all “known” conditions

An important step in solving certain types of problems, especially quantitative problems where calculations are necessary to obtain precise answers, it is often useful to list all the known quantities available to us relevant to the problem. Similarly, taking the time to list all relevant (and possibly relevant) mathematical formulae we might apply to the solution is a helpful step.

One way to save time applying the latter suggestion in a classroom setting is to keep a concise reference card or file filled with formulae you’ve been learning within that course. This reference may be referred to as often as necessary, without having to re-write the equations for each and every problem, thus eliminating unnecessary effort.

Re-cast the problem in a different format

Many people find it easier to grasp the nature of a problem – and by extension, that problem’s solution – if they can look at an illustration of the problem. Therefore, a helpful step in solving problems described to you in words is to translate those words into a picture to look at.

If you are one of those people for whom drawing is a challenge, take heart in the fact that this is a skill you can build. Practice is the key to honing this skill. With this in mind, make it a habit to sketch some kind of illustration for every problem you are asked to solve. If you are working in teams to solve a problem, a collaborative sketch goes a long way toward coordinating problem-solving efforts and ensuring everyone on the team has the same view of the problem.

For some people, describing a problem verbally is helpful in solving it. If your brain tends to work like this – understanding concepts and situations better when they are cast into clear prose – then you may find it helpful to first draft an explanatory paragraph of the problem in your own words. This is also an exercise lending itself well to team-based problem solving, as the entire team can help each other describe the nature of the problem.

Working backwards from a known solution

Sometimes we may gain insight into the solution of a problem by assuming we already know the answer to a similar problem, then working “backward” to find the problem from that assumed solution.

An application of this problem-solving strategy is found learning how to decode binary bits that have been encoded using the Manchester standard. With Manchester encoding, binary bits are represented by the rising and falling edges of square-shaped waveforms rather than high and low states themselves. For example:

Seeing this example, we note how each binary “0” bit is represented by a falling edge, while each “1” bit is represented by a rising edge.

Where most students encounter trouble is in situations where they have been given a Manchester encoded waveform and must decode it into its representative bit stream. Take this for example:

Most students’ first inclination is to ask their instructor or their classmates for an algorithm to decode the waveform. “What steps should I take to figure out where the data bits are?” they will ask. This sort of “give me the answer” mind-set should always be discouraged, because it is the polar opposite of true problem-solving technique, where the student methodically searches for patterns and develops algorithms on their own.

A better approach is to encourage the strategy of working the problem backwards: begin with a known series of binary bits, and then develop a Manchester waveform from that. The act of encoding a binary string provides insight that will be useful in decoding the next Manchester waveform they encounter.

For example, let’s begin with the binary string 100011101:

We may begin the process of encoding this into Manchester format by sketching the rising- and falling-edges we know we will need for each bit:

Next, we can try connecting the tops and bottoms of these pulse edges to form a complete waveform. Soon, however, we will find that this is only possible where opposite bit states are adjacent to each other. Where identical bits follow in sequence, we are faced with sequential rising edges or sequential falling edges, which we cannot simply bridge at the tops or bottoms to make a full pulse:

This observation leads to the realization of why we need reversals in a Manchester waveform. The only way to connect repeating bits’ edges together is if the waveform goes through another rising or falling edge in order to be properly set up for the next edge we need to represent a bit:

Here we see the power and utility of working a problem “backwards”: it reveals to us the reason why things are the way they are. Without this understanding, problem-solving is nothing more than rote recall of algorithms, and limited in application. Any problem becomes simpler to solve once we fully understand its rationale.

Once we realize the purpose for reversals in a Manchester waveform, it becomes obvious to see that these reversals always fall between the bit transitions, and thus are always out of step with the frequency of the bits. Those edge transitions representing real data bits must always fall along a regular timed interval, with reversals being “half-steps” in between those intervals. We need only to look for the widest-spaced intervals in a Manchester waveform to distinguish those pulse edges representing real data bits, and then we know to ignore any pulse edges out of step with them.

Returning to our sample problem, where we were given a Manchester waveform and asked to decode it:

First, we identify the real data bit edges by widest spacing:

Now that we know which pulse edges represent bits, we may ignore those that do not (the reversals), and decode the waveform:

Using thought experiments

One of the most powerful problem-solving techniques available for general use is something called a thought experiment . Scientists use experiments to confirm or refute hypotheses, testing their explanations by seeing whether or not they can successfully predict the outcome of a certain situation by comparing their predictions against real outcomes. While this technique is extremely useful, it might not always be practical or expedient. A useful alternative to real experiments is to mentally “model” the system and then imagine changing certain elements or variables within that model to deduce the effects.

Albert Einstein famously applied “thought experiments” to the formulation of his Theory of Relativity, for the very simple reason that he lacked the resources and technology to actually test his ideas in real life. Working as a patent clerk, he would imagine what might happen if an observer were to travel at or near the speed of light. One particular example of this is the anecdote of Einstein observing a clock tower as he rode a trolley traveling away from the tower. “What would an observer see,” he wondered, “as he viewed the clock’s face while traveling away from it at the speed of light?” Concluding that the clock’s face would appear to be frozen in time was one of the surprising “experimental” results leading Einstein to a more rigorous examination of physical effects at extremely high velocities.

“Thought experiments” are useful in solving a wide variety of problems, because they allow us to test our understanding of a system’s behavior. By imagining certain conditions or variables changing in a system and then asking ourselves what the effects will be, we probe our own understanding of that system, often times with the result being that we are able to predict its behavior under conditions that baffle us at first.

You will find “thought experiments” scattered throughout this book, used both as illustrations of problem-solving strategies and also as a tool to explain how certain technologies function. An example of this is the section explaining non-dispersive analyzers, which are instruments employing the absorption of light by certain species of chemicals in order to detect the presence and measure the quantities of those chemicals. Beginning in section 23.6 on page , a series of “thought experiments” are used to explore the principles used to identify chemicals by light absorption. This series of virtual experiments becomes most valuable when this section explores the analyzer’s ability to selectively measure the presence of one light-absorbing chemical to the exclusion of other light-absorbing chemicals within the same mixture.

Explicitly annotating your thoughts

Suppose you were asked to solve this multiplication problem, without the use of a calculating machine of any kind, but with access to paper and a writing tool:

Your primary school education should have prepared you to solve elementary arithmetic problems of this kind, by a process of digit-by-digit multiplication and addition, to arrive at an answer of 1,955,096. The procedure, while tedious, is rather simple: manually multiply the top numeral three times over by successive digits of the bottom numeral, noting any “carried” quantities as you do so, then sum those three subtotals together (padded with zeros to represent the place of the bottom numeral’s digit) to arrive at the final product.

Now suppose you were asked to solve the exact same multiplication problem, but this time doing the same digit-by-digit arithmetic all in your mind, without the use of a writing tool to annotate your work. Suddenly this elementary task becomes nearly impossible for anyone who isn’t a mathematical savant. What made the difference between this problem as an elementary exercise and this same problem as a nearly impossible feat? The answer to this question is short-term memory : most people do not possess a good enough short-term memory to mentally manage all the intermediate calculations necessary to complete the calculation. This is why people learn to annotate their work when performing manual multiplication, so they don’t have to rely on their limited short-term memories. The freedom to write your steps on paper converts what would otherwise be a Herculean feat of arithmetic into a rather trivial exercise.

Annotating your intermediate steps as you solve a problem is actually an excellent general problem-solving strategy, applicable to far more than just arithmetic. Some examples of annotating intermediate steps are listed here:

• Reading a complex document : annotating your thoughts, questions, and epiphanies as you read the text allows you to derive a better understanding of the text as a whole.
• Learning a new computer application : noting how features are accessed and identifying the necessary conditions for each feature to work helps you navigate the software more efficiently.
• Following a route on a map : marking where you started, where your destination is, and where you have traveled thus far helps you see how far you still need to go, and which alternative routes are open to you.
• Analyzing an electric circuit : annotating all calculated voltages, currents, and impedances on the diagram helps you keep track of what you know about the circuit and where to go next in your analysis.
• Troubleshooting a system fault : noting all your diagnostic steps and conclusions along the way helps you confirm or disprove hypotheses.

Sadly, many students attempt to solve new types of problems analogously to performing multiplication without paper and pencil: they attempt to mentally manage all their intermediate steps, not writing anything down that would help them later. As a result, students tend to get “lost” when trying to solve new problems simply because they cannot readily reference of all their thoughts along the way. Most people simply give up when they begin to feel “lost” in solving a problem, thinking that if they cannot mentally picture the solution in its entirety then they have no hope of attaining it. Let’s face it: how soon would you give up on multiplying 3418 \(\times\) 572 without a calculator if you believed the only alternative was to manage all the arithmetic in your head?

One reason why students default to the “mental-only” approach when approaching new problems is that their educational experience has only presented annotation for specific types of problems. Thus, marking all the carry digits and subtotals is something they “only do” when performing multiplication by hand; marking calculated voltages and currents on a schematic diagram is something they “only do” when solving DC resistor circuits; taking notes when reading is something they “only do” when completing a book report. In other words, students see annotation only in very specific contexts, and so they may fail to see just how widely applicable annotation is as a problem-solving strategy. What teachers should do is model and encourage annotation as a problem-solving technique for all types of problems, not just for some types of problems.

To illustrate how this might be done in the context of control system analysis, let us suppose we were asked to determine the effect of flow transmitter FT-24 failing with a low (no-flow) signal in this ratio control system, part of a process for manufacturing ammonium nitrate fertilizer:

Before it is possible to analyze the effects of a transmitter failure, we must first determine what the system ought to do in normal operation. Natural questions to ask might include the following:

• Where do the instrument signals come from and where do they go to?
• What does each instrument signal represent?
• What is the direction of action for each controller in the system?

With just a basic understanding of ratio control systems, we may answer all of these questions by close examination of the P&ID segment, and also annotate those thoughts and conclusions on the diagram in order to help us analyze the system’s response. Starting with the first two questions of where signals originate and terminate and what each signal represents, we may annotate this with arrows and text (shown in red):

We know that all transmitters output data, and so all signal arrows should point away from all transmitters and toward all controllers. We know that all valves receive data, which means arrows must point toward the control valve. The first tag letter of each transmitter (AIT, FT) tells us its measurement function: chemical pH and flow, respectively. The fact that FT-23 is mounted on the same pipe as FV-23 tells us FT-23 must send controller FFC-23 its process variable (captive flow), making the other flow signal (from FT-24) the “wild” flow in this ratio control scheme. AIC-28’s task is to control pH exiting the neutralizer, so we know its output signal must call for a neutralizing reagent, in this case nitric acid. This tells us the signal between AIC-28 and FFC-23 must be a cascade output-setpoint link, with AIC-28 as the master controller and FFC-23 as the slave controller.

Now we turn to the question of controller action, since we know the direction of each controller’s action (e.g. direct or reverse) is significant to how each controller will react to any given change in signal. Here, ‘thought experiments’’ are useful as we imagine the process variable changing due to some load condition, and then determine how the controller must respond to bring that process variable back to setpoint.

When we annotate the action of each controller, it is best to use symbols more descriptive than the words “direct” and “reverse,” especially due to the confusion this often causes when distinguishing the effects of a changing PV signal versus a changing SP signal. In this case, we will write a short formula next to each controller denoting its action according to how the error is calculated (\(e = \hbox{PV} - \hbox{SP}\) for direct action and \(e = \hbox{SP} - \hbox{PV}\) for reverse action). We may also write “+” and “\(-\)” symbols next to each input on each controller to further reinforce the direction of each signal’s influence:

FFC-23 is the best controller to start with, since it is the slave controller (in the “inner-most” control loop of this cascade/ratio system). Here, we see that FFC-23 must be reverse-acting, for if FT-23 reports a higher flow we will want FV-23 to close down. This means the remote SP input must have a non-inverting effect on the output: a greater signal from AIC-28 will increase nitric acid flow into the neutralizer. Following this reasoning, we see that AIC-28 should be direct-acting, calling for more nitric acid flow into the neutralizer as product pH becomes more alkaline (pH increases).

The purpose of the ratio control strategy is to balance the “wild” flow of ammonia into the neutralizer with a proportional flow of nitric acid. This is in keeping with principles of chemical reactions (stoichiometry) and mass balance. Therefore, we would expect an increase in ammonia flow to call for a proportionate increase in nitric acid flow, giving the wild flow signal a non-inverting effect on FFC-23.

Only at this point in time are we fully ready to analyze the effects of FT-24 failing with a low-flow signal. Once again, we may annotate the failure on the diagram as well, arbitrarily electing to use blue “up” and “down” arrows and bold text to indicate the directions of change for each signal immediately following the failure of FT-24:

As FT-24’s signal fails low, the “wild” flow signal to FFC-23 goes low as well. Since we have already determined that input has a non-inverting effect on the ratio controller, we may conclude control valve FV-23 will close as a result, decreasing the flow of nitric acid into the neutralizer. This analysis becomes trivial after we’ve done the work of annotating the diagram with our own notes showing how the instruments are supposed to function. Without this set-up, the task of analyzing the effects of FT-24 failing low would be much more difficult.

• Textbook Index

Lessons in Industrial Automation

Volumes ».

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• Fluid Systems
• Control Systems
• Measurement

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• 1 Calculus in Industrial Process Measurement and Control
• 2 Physics in Industrial Instrumentation
• 3 Chemistry in Industrial Instrumentation

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• 6 More Principles of Industrial Instrumentation (Animated)
• 7 Educational Concepts and Models for the Field of Instrumentation - Advice for Teachers
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15 Problem-Solving Strategies for Projects and Teams

In project management and team collaboration, problem-solving is the process of identifying and resolving issues that arise during a project. It is a crucial skill that helps fix broken processes, improve performance, and identify opportunities. Problem-solving enables project managers and team leaders to overcome challenges and achieve success.

In this blog article, we will explore 15 problem-solving strategies that can revolutionize your approach and help you achieve success. From effective communication techniques to fostering collaboration, these strategies are designed to tackle the most common obstacles encountered in project management. Get ready to unlock the potential of your projects and teams with these tried-and-tested problem-solving strategies!

The 5 Whys Analysis

This problem-solving technique aims to uncover a problem's underlying cause by repeatedly asking the question, "Why?". The goal is to dig deep and identify the root cause rather than addressing surface-level symptoms. By asking "Why?" five times or more, depending on the complexity of the problem, you and your team members can gain valuable insights into the chain of events or processes that led to the issue. This method helps expose weaknesses, improve processes, and achieve project goals.

SWOT Analysis in Project Management

A SWOT analysis is a valuable tool in project management that helps identify and evaluate the internal pros and cons, weaknesses and strengths, and the external impacts that can threaten your project. By examining these factors, project managers can better understand the project's current state and potential risks.

SWOT analysis provides insights that help decision-making, resource allocation, and risk mitigation strategies. It allows project teams to counter threats, address weaknesses, and capitalize on strengths, ultimately enhancing project success.

Bring in a Facilitator

A skilled facilitator can provide objective guidance, ensure everyone's participation, and create a safe space for open discussions. They can help the project team effectively identify and evaluate strengths, weaknesses, opportunities, and threats.

Additionally, a facilitator can assist in summarizing and documenting the analysis, ensuring clarity and alignment among team members. Bringing in a facilitator enhances quality and efficiency, leading to better project outcomes.

Root Cause Analysis

Root Cause Analysis is a systematic approach used to identify the underlying causes of problems or incidents. It involves investigating the factors contributing to an issue rather than just addressing the symptoms.

By understanding the root causes, organizations can develop effective solutions to prevent similar problems from recurring in the future. This analysis helps improve processes, enhance quality, and reduce risks, ultimately leading to better outcomes and customer satisfaction.

Kipling Method

Also known as the 6 W's, this technique is used to gather information and comprehensively understand a situation. It involves asking and answering six key questions: Who, What, Where, When, Why, and How. Addressing these questions helps team members and leaders analyze and evaluate a problem or decision from various angles, ensuring a thorough examination of the topic at hand. This method is widely used in journalism, problem-solving, and decision-making processes.

Work Backward

Working backward is a problem-solving approach where you start with the desired outcome and then identify the steps needed to achieve it. This method allows you to break down complex problems into smaller, manageable tasks.

By starting with the end goal in mind, you can create a clear roadmap and prioritize actions accordingly. Working backward helps ensure that your efforts are focused and aligned with the desired outcome, leading to more efficient and effective problem-solving.

Trial and Error

Trial and Error is a problem-solving approach that involves trying different solutions and learning from the outcomes. It is a standard method used to discover what works and what doesn't in various situations. By systematically testing different options, you can identify the most effective solution through a process of elimination.

Trial and Error allow team members flexibility and adaptability, as it encourages learning from mistakes and refining strategies based on feedback. This approach can be beneficial when dealing with complex or ambiguous problems that require experimentation.

Risk Analysis and Mitigation within Teams

Risk analysis and mitigation play a crucial role in project management. By identifying and planning for potential risks, teams can prevent problems. One effective way to facilitate this process is by utilizing project management software.

Project management software such as ActiveCollab provides a centralized platform where teams can document and track risks throughout the project lifecycle. This software often includes features such as risk registers , where risks can be identified, categorized, and assigned to team members for mitigation. Additionally, the software may offer risk assessment tools that help teams evaluate the impact and likelihood of each risk.

With ActiveCollab, teams can collaborate in real time, ensuring all members can access the latest risk information. This promotes transparency and allows immediate communication and decision-making regarding risk mitigation strategies.

Using project management software for risk analysis and mitigation, your team members can proactively address potential challenges, minimize project disruptions, and improve project success rates.

Implementing Conflict Resolution Techniques

Conflict resolution techniques are essential for maintaining healthy relationships and fostering a positive work environment. Some effective methods include active listening, communication skills training, mediation, negotiation, and compromise. Conflicts can be understood and resolved more effectively by actively listening to all parties involved and encouraging open and honest communication.

Mediation allows a neutral third party to facilitate discussions, while negotiation and compromise help find mutually agreeable solutions. These techniques promote understanding, empathy, and collaboration, improving relationships and increasing productivity.

Scenario Planning and Forecasting

Scenario planning and forecasting are essential tools used by organizations to anticipate and prepare for future uncertainties. Scenario planning involves creating multiple plausible narratives or scenarios to explore possible futures, allowing decision-makers to identify risks and opportunities. Forecasting, on the other hand, uses historical data and statistical models to project future outcomes.

Combining these approaches allows businesses to develop robust strategies and make informed decisions in an ever-changing and unpredictable environment. This proactive approach helps organizations adapt, minimize risks, and seize opportunities, ensuring long-term success and resilience in an uncertain world.

Brainstorming and Ideation Sessions

Brainstorming and ideation sessions are great for generating creative ideas and solutions. You can bring together a diverse group of individuals while these sessions help them with collaboration and free thinking. Participants in brainstorming sessions are encouraged to share their thoughts and ideas without judgment, allowing for a wide range of possibilities to be explored.

The goal is to generate as many ideas as possible, with the understanding that quantity leads to quality. Through active listening and open-mindedness, participants can build upon each other's ideas and spark new insights. This collaborative process fosters innovation and can lead to breakthrough solutions to complex problems.

Design Thinking

Design Thinking is a problem-solving approach that emphasizes empathy, creativity, and collaboration. It involves understanding the needs and experiences of users, generating a wide range of ideas, prototyping, testing solutions, and iterating based on feedback.

It encourages a human-centered and iterative mindset, which leads to exploring multiple possibilities before arriving at a final solution. Design Thinking enables teams to approach challenges with an open mind, fostering innovation and driving meaningful change. By putting people at the heart of the process, Design Thinking helps create solutions that meet their needs and aspirations.

Creating a Feedback Loop with Team Members

Creating a feedback loop with team members leads to a culture of continuous improvement. Regularly soliciting positive and constructive feedback allows open communication, builds trust, and enhances teamwork. Encourage team members to share their thoughts, ideas, and concerns in a safe and non-judgmental environment.

Actively listen to their feedback, acknowledge their contributions, and provide actionable insights to help them grow professionally. Remember, a well-functioning feedback loop promotes collaboration, boosts morale, and ultimately leads to better outcomes for the entire team.

Agile Principles for Efficient Problem-Solving (Enhance Flexibility and Responsiveness)

Agile principles are values and practices that enhance flexibility and responsiveness in problem-solving. These principles prioritize individuals and interactions, working solutions, customer collaboration, and responding to change.

Agile principles promote a more efficient problem-solving process by encouraging frequent communication and collaboration. Iterative development, continuous feedback, and adaptive planning are key components of agile problem-solving, allowing teams to adapt and respond to changing requirements quickly. Focusing on delivering value to the customer and embracing change enables organizations to address problems efficiently and effectively.

Importance of Effective Problem-Solving

Effective problem-solving is crucial in project management as it ensures that issues are identified, analyzed, and resolved promptly and efficiently. By integrating problem-solving strategies with project management software like ActiveCollab, teams can enhance collaboration and streamline workflow.

Integrating problem-solving strategies with project management software allows for better communication and coordination among team members. It enables teams to track the progress of problem-solving activities, assign tasks, and monitor deadlines, ensuring everyone is on the same page. This integration also facilitates the sharing of information and knowledge, enabling teams to leverage their collective expertise and experience.

Moreover, project management software such as ActiveCollab provides a centralized platform where team members can document and access relevant information, making it easier to analyze problems and make informed decisions. It also allows the implementation of feedback loops, enabling continuous improvement and learning from past experiences.

In conclusion, integrating problem-solving strategies with ActiveCollab enhances teamwork, improves communication, and facilitates the efficient resolution of issues. This integration ultimately contributes to the successful execution of projects and achieving desired outcomes.

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Turn your team into skilled problem solvers with these problem-solving strategies

Picture this, you're handling your daily tasks at work and your boss calls you in and says, "We have a problem." 

Unfortunately, we don't live in a world in which problems are instantly resolved with the snap of our fingers. Knowing how to effectively solve problems is an important professional skill to hone. If you have a problem that needs to be solved, what is the right process to use to ensure you get the most effective solution?

In this article we'll break down the problem-solving process and how you can find the most effective solutions for complex problems.

What is problem solving? 

Problem solving is the process of finding a resolution for a specific issue or conflict. There are many possible solutions for solving a problem, which is why it's important to go through a problem-solving process to find the best solution. You could use a flathead screwdriver to unscrew a Phillips head screw, but there is a better tool for the situation. Utilizing common problem-solving techniques helps you find the best solution to fit the needs of the specific situation, much like using the right tools.

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4 steps to better problem solving

While it might be tempting to dive into a problem head first, take the time to move step by step. Here’s how you can effectively break down the problem-solving process with your team:

1. Identify the problem that needs to be solved

One of the easiest ways to identify a problem is to ask questions. A good place to start is to ask journalistic questions, like:

Who : Who is involved with this problem? Who caused the problem? Who is most affected by this issue?

What: What is happening? What is the extent of the issue? What does this problem prevent from moving forward?

Where: Where did this problem take place? Does this problem affect anything else in the immediate area? 

When: When did this problem happen? When does this problem take effect? Is this an urgent issue that needs to be solved within a certain timeframe?

Why: Why is it happening? Why does it impact workflows?

How: How did this problem occur? How is it affecting workflows and team members from being productive?

Asking journalistic questions can help you define a strong problem statement so you can highlight the current situation objectively, and create a plan around that situation.

Here’s an example of how a design team uses journalistic questions to identify their problem:

Overarching problem: Design requests are being missed

Who: Design team, digital marketing team, web development team

What: Design requests are forgotten, lost, or being created ad hoc.

Where: Email requests, design request spreadsheet

When: Missed requests on January 20th, January 31st, February 4th, February 6th

How : Email request was lost in inbox and the intake spreadsheet was not updated correctly. The digital marketing team had to delay launching ads for a few days while design requests were bottlenecked. Designers had to work extra hours to ensure all requests were completed.

In this example, there are many different aspects of this problem that can be solved. Using journalistic questions can help you identify different issues and who you should involve in the process.

2. Brainstorm multiple solutions

If at all possible, bring in a facilitator who doesn't have a major stake in the solution. Bringing an individual who has little-to-no stake in the matter can help keep your team on track and encourage good problem-solving skills.

Here are a few brainstorming techniques to encourage creative thinking:

Brainstorm alone before hand: Before you come together as a group, provide some context to your team on what exactly the issue is that you're brainstorming. This will give time for you and your teammates to have some ideas ready by the time you meet.

Say yes to everything (at first): When you first start brainstorming, don't say no to any ideas just yet—try to get as many ideas down as possible. Having as many ideas as possible ensures that you’ll get a variety of solutions. Save the trimming for the next step of the strategy. 

Talk to team members one-on-one: Some people may be less comfortable sharing their ideas in a group setting. Discuss the issue with team members individually and encourage them to share their opinions without restrictions—you might find some more detailed insights than originally anticipated.

Break out of your routine: If you're used to brainstorming in a conference room or over Zoom calls, do something a little different! Take your brainstorming meeting to a coffee shop or have your Zoom call while you're taking a walk. Getting out of your routine can force your brain out of its usual rut and increase critical thinking.

3. Define the solution

After you brainstorm with team members to get their unique perspectives on a scenario, it's time to look at the different strategies and decide which option is the best solution for the problem at hand. When defining the solution, consider these main two questions: What is the desired outcome of this solution and who stands to benefit from this solution? 

Set a deadline for when this decision needs to be made and update stakeholders accordingly. Sometimes there's too many people who need to make a decision. Use your best judgement based on the limitations provided to do great things fast.

4. Implement the solution

To implement your solution, start by working with the individuals who are as closest to the problem. This can help those most affected by the problem get unblocked. Then move farther out to those who are less affected, and so on and so forth. Some solutions are simple enough that you don’t need to work through multiple teams.

After you prioritize implementation with the right teams, assign out the ongoing work that needs to be completed by the rest of the team. This can prevent people from becoming overburdened during the implementation plan . Once your solution is in place, schedule check-ins to see how the solution is working and course-correct if necessary.

Implement common problem-solving strategies

There are a few ways to go about identifying problems (and solutions). Here are some strategies you can try, as well as common ways to apply them:

Trial and error

Trial and error problem solving doesn't usually require a whole team of people to solve. To use trial and error problem solving, identify the cause of the problem, and then rapidly test possible solutions to see if anything changes. 

This problem-solving method is often used in tech support teams through troubleshooting.

The 5 whys problem-solving method helps get to the root cause of an issue. You start by asking once, “Why did this issue happen?” After answering the first why, ask again, “Why did that happen?” You'll do this five times until you can attribute the problem to a root cause. 

This technique can help you dig in and find the human error that caused something to go wrong. More importantly, it also helps you and your team develop an actionable plan so that you can prevent the issue from happening again.

Here’s an example:

Problem: The email marketing campaign was accidentally sent to the wrong audience.

“Why did this happen?” Because the audience name was not updated in our email platform.

“Why were the audience names not changed?” Because the audience segment was not renamed after editing. 

“Why was the audience segment not renamed?” Because everybody has an individual way of creating an audience segment.

“Why does everybody have an individual way of creating an audience segment?” Because there is no standardized process for creating audience segments. 

“Why is there no standardized process for creating audience segments?” Because the team hasn't decided on a way to standardize the process as the team introduced new members. 

In this example, we can see a few areas that could be optimized to prevent this mistake from happening again. When working through these questions, make sure that everyone who was involved in the situation is present so that you can co-create next steps to avoid the same problem. 

A SWOT analysis

A SWOT analysis can help you highlight the strengths and weaknesses of a specific solution. SWOT stands for:

Strength: Why is this specific solution a good fit for this problem? 

Weaknesses: What are the weak points of this solution? Is there anything that you can do to strengthen those weaknesses?

Opportunities: What other benefits could arise from implementing this solution?

Threats: Is there anything about this decision that can detrimentally impact your team?

As you identify specific solutions, you can highlight the different strengths, weaknesses, opportunities, and threats of each solution. 

This particular problem-solving strategy is good to use when you're narrowing down the answers and need to compare and contrast the differences between different solutions. 

Even more successful problem solving

After you’ve worked through a tough problem, don't forget to celebrate how far you've come. Not only is this important for your team of problem solvers to see their work in action, but this can also help you become a more efficient, effective , and flexible team. The more problems you tackle together, the more you’ll achieve. 

Looking for a tool to help solve problems on your team? Track project implementation with a work management tool like Asana .

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Cognitive control, intentions, and problem solving in skill learning

Wayne christensen.

1 Philosophy, University of Barcelona, Barcelona, Spain

Kath Bicknell

2 School of Social Sciences and School of Psychological Sciences, Macquarie University, Sydney, Australia

We investigate flexibility and problem solving in skilled action. We conducted a field study of mountain bike riding that required a learner rider to cope with major changes in technique and equipment. Our results indicate that relatively inexperienced individuals can be capable of fairly complex 'on-the-fly' problem solving which allows them to cope with new conditions. This problem solving is hard to explain for classical theories of skill because the adjustments are too large to be achieved by automatic mechanisms and too complex and rapid to be achieved by cognitive processes as they are usually understood. A recent theory, Mesh, can explain these results because it posits that skill-specific cognitive abilities develop during skill learning and that control typically involves an interplay between cognitive and automatic mechanisms. Here we develop Mesh further, providing a detailed explanation for these problem solving abilities. We argue that causal representation, metacognitive awareness and other forms of performance awareness combine in the formulation and control of action strategies. We also argue that the structure of control present in this case is inconsistent with Bratman's model of intentions, and that, in the face of high uncertainty and risk, intentions can be much more labile than Bratman recognises. In addition, we found limitations and flaws in problem solving which illuminate the representations involved. Finally, we highlight the crucial role of social and cultural learning in the development of complex skills.

Introduction

What is learned during skill learning? What role does cognitive control—the form of control involved in flexible, goal-directed thought and action—play in the learning process? Classical skill theories, such as those of Fitts and Posner ( 1967 ) and Anderson ( 1982 ), treat cognitive control as responsible for discovering the structure of the actions that the skill requires, and for their initial implementation, but as being supplanted by more efficient automatic processes as learning progresses. Most work on skill focuses on the development of automaticity and the abilities and mechanisms that automatically-produced skills might involve. But humans show an exceptionally high degree of flexibility in skilled action, including forms of flexibility that rely on problem solving to construct solutions, as opposed to the deployment of pre-learned solutions. This kind of flexibility has not received much attention, even though it is arguably the critical ability underlying the richness and diversity of human skill.

The classical view sees skill as automating because it regards cognitive control as fundamentally unsuited to the demands of skilled action control. Cognitive control is thought to be slow, serial, and as having limited capacity, whereas automatic processes are fast, parallel, and have high capacity (Shiffrin & Schneider, 1977 ; Evans & Stanovich, 2013 ). Cognitive control uses highly generalised representations and problem solving methods which are an inefficient means for producing the specialised responses of skill (Anderson, 1982 ). In other words, cognitive control is specialised for reasoning, not action control, and it is a clumsy tool to use for action control. But it's questionable whether cognitive control is really fundamentally unsuited to skilled action control, as assumed by the classical view. Certainly, early cognitively-driven efforts to perform a skill are clumsy, and working memory is often overloaded by task demands. But significant degrees of fluency emerge long before strong automaticity could be in place. Skill research tends to focus on motor skill in particular, but if we consider expertise research more generally it is clear that experts can acquire domain-specific cognitive skills which can allow them to rapidly process large amounts of information (Ericsson & Kintsch, 1995 ) and engage in powerful, domain-specific forms of problem solving (Chi et al., 1982 ). There is no obvious reason why skills that involve a strong motor component might not also incorporate cognitive abilities involved in control and problem solving.

A recent skill theory called Mesh proposes just this (Christensen & Sutton, 2018 ; Christensen et al., 2016 , 2019 ). It claims that almost all skills incorporate an important cognitive component, including those which are paradigmatically motoric like golf putting. Skilled performance is produced by meshed cognitive and automatic processes which are generated by the cooperative operation of many neural systems. Cognitive processes provide flexibility by shaping action to the context and by solving the problems that complex, variable environments and tasks present. This paper extends Mesh by investigating the nature of skilled problem solving more closely. We conducted a field study investigating adjustments to major task changes by a rider with several years mountain bike riding experience (and many years of road riding experience) but beginner-level mountain bike technique. This was one of the authors, Wayne. Kath, who is a highly experienced rider, provided instruction. Guidance from Kath prompted major changes in Wayne's riding technique. A change in bike during the field study also encouraged significant adjustments in Wayne’s riding.

Because Wayne was not a raw novice he possessed mountain-biking-specific problem solving abilities which allowed him to cope with these large changes more fluently than might be expected based on the classical view. His experiences navigating rocky descents and ascents, and challenging log roll-overs, help to illuminate the nature of the control involved in this kind of problem solving. These problems involve high uncertainty and significant risk, and control processes flexibly adjust action to manage this risk and uncertainty. The learner is learning the structure of the problem as they try out solutions, and the strategy is monitored, modified and sometimes abandoned during execution. Evaluation is complex, employing a rich set of criteria and flexible holistic assessment. To understand these features of control we need a more expansive concept of control and a more labile picture of intentions than the standard picture recognises (Bratman, 1987 ).

The problem solving that Wayne engaged in also helps to illuminate the nature of the representations involved. Recently there has been considerable interest in characterising the type of the representations that are required for the ability to perform actions (Pacherie, 2011 ). Most accounts have focused on representations of action form , with an emphasis on schemas (e.g. Mylopoulos & Pacherie, 2018 ; Fridland, 2021 ). In contrast, some accounts have focused on the representation of causal structure (Christensen et al., 2015 ; Goldenberg, 2013 ).

Here we endorse the view that causal representation plays an important role, and show how this kind of representation supports flexibility. We suggest that Wayne employed causal representation to identify the structure of the problems he faced and formulate solutions. Because he could understand the causal significance of some of the features of the altered technique and equipment he could rapidly formulate new strategies to cope with and exploit the changes. But Wayne's problem solving abilities showed strong limitations which are also revealing. Wayne failed to properly implement a key riding technique even though he understood the technique abstractly and thought he was implementing it. The problem was diagnosed by Kath, who corrected his implementation. His difficulty involved a failure to properly map between an abstract representation of the technique and the representations used in the control of execution. The latter have particular a type of content, in particular a systematic representation of the state space of execution control . They also have a characteristic perspective that we call the perspective of execution control . This perspective is related to, though distinct from, the type of perspective described by (Pavese, 2019 ) in her concept of practical modes of presentation.

In Sect.  2 we present the theoretical context for the study and describe its conception. In Sect.  3 we give an overview of the activities we conducted. Section  4 then analyses these experiences in close detail using the theoretical ideas developed in Sect.  2 .

Context and approach

This paper centers on a field study which is analysed for its theoretical implications. In both philosophy and psychology this is an unusual method which requires some explanation and defense. In a separate paper, Christensen (in preparation) develops a general argument that cognitive ecology should be a central discipline in psychology, that ecological methods should be incorporated into philosophy just as experimental methods have been, and that the present lack of attention to cognitive ecology is a serious limitation on the development of deep theory in both psychology and philosophy of mind (see also Bicknell & Sutton, 2022 ). Here we focus on a more restricted argument which highlights some of the ways that the theoretical issues we are concerned with in this paper are sensitive to ecological data.

As noted, we are concerned with skill learning, and with the role of cognitive control, intentions, and action strategies in skill learning. These are evolved adaptive traits whose structure and function are shaped by a complex mixture of evolutionary adaptation and learning. It follows that understanding the nature of the adaptive functions and mechanisms depends critically on understanding the ecological problems they respond to. Consequently, we need to investigate these phenomena in the context of the ecological problems they are adapted to solve. This requires the use of ecological methods to investigate the nature of these problems. We currently lack a detailed understanding of these ecological problems.

Bratman's ( 1987 ) theory of intentions is an especially relevant case where theory is sensitive to the details of these ecological problems. 1 Bratman’s account is widely accepted and is used as a framework by numerous contemporary researchers. Mylopoulos and Pacherie’s ( 2018 ) account of intentional action control is an example which we will compare with our own model. Bratman characterises intentions based on an analysis of human planning behavior. In developing his theory Bratman focuses primarily on "ordinary, humdrum cases in which future-directed intentions and partial plans lead without great difficulty from prior deliberation to later conduct" (p. 12). He sees these kinds of cases as contrasting with more complex cases that involve difficulties of self-control. He writes,

Such examples are quite fascinating. But I think we get a distorted view of future-directed intention if we take them as paradigmatic of intention. It is best, I think, to begin with ordinary, garden-variety cases in which, without major psychological resistance, future-directed intentions and partial plans support coordination in the lives of limited agents like us. It is here that we need to look to get at the major regularities, roles, and norms in terms of which we can understand intention and its associated commitment . (p. 12, emphasis added.)

In this passage Bratman shows an awareness of the importance of the ecological representativeness of types of cases. However, Bratman's ecological picture is itself flawed. Bratman appears to assume that ordinary humdrum cases “without great difficulty” are ecologically typical or predominant while cases with major psychological conflict are exceptional. He also assumes that these are the only significant variations to be considered. Bratman's examples are indeed, for the most part, mundane, such as going to the library to borrow a book, or deciding whether to have a milkshake for lunch. These cases involve low stakes, high information, low complexity, context stability, and low time pressure. Only one of his examples, a presidential TV debate, involves high stakes, high complexity and high time pressure. Bratman uses the complexity and time pressure of the case in his analysis but he doesn't systematically examine these features as such or consider the significance of variation in them for his theory. All these features vary greatly across naturally occurring human ecological contexts. And crucially, these variations have important theoretical implications. The basic structure of Bratman’s model of intention involves a phase of deliberation in which an intention is formed, followed by a phase in which evaluation of the intention has ceased and cognitive processes are devoted to implementation. Intentions serve as fixed anchor points which structure cognition and behavior after they have been adopted. But this model may be best suited to cases that involve low stakes and high information. In cases with significant uncertainty and high stakes we might expect evaluation of the intention to be ongoing during execution as information comes in. In some kinds of cases it is possible to abort an action part way through or change its fundamental nature. Accordingly, in these kinds of contexts intentions may be relatively labile, being evaluated, modified, and sometimes abandoned during execution as information about the action is acquired. Skill learning is an important context in which this kind of pattern is likely.

Lack of ecological data is also a problem for skill theory. Classical skill theories, such as those of Fitts and Posner ( 1967 ) and Dreyfus and Dreyfus ( 1986 ), propose that skill acquisition has a well-defined stage structure culminating in a final stage of full automaticity. Fitts and Posner's model has just three stages, while Dreyfus and Dreyfus's account has five. Yet skills are extremely diverse, ranging from the ability to play tic-tac-toe to being a concert pianist or astrophysicist. It is unlikely that a simple three or five stage model provides a satisfactory fit to all the skill acquisition pathways involved in developing such diverse expert abilities. In athletics, high jumping has a standardised, invariant task structure, and a single action strategy, the Fosbury Flop, has dominated since the late 1960s. In contrast, in competition bouldering route setters set highly diverse novel climbing problems at each competition. Climbers are allowed only four minutes to inspect the wall and formulate strategies. Climbers try out varied strategies, often employing strategies which suit their individual abilities and physical characteristics, such as explosive strength, limb and hand size, or flexibility and balance. 2 Multiple strategies may succeed on a given problem, including strategies not anticipated by the route setters. 3 At odds with classical skill theories, climbers and commentators often describe bouldering at elite levels as highly cognitive. This is less often said about high jump. To get a better understanding of skill we need a fuller picture of this diversity.

In this paper we take a small step towards filling out this picture. Our objective is to bring detailed theoretical analysis into a close engagement with detailed empirical ecological reporting and analysis. While our sample is a tiny slice of the big picture, close analysis reveals that it has features with wide-ranging theoretical significance.

Ecological context

The ecological context we have focused on is that of socially-guided learning of a complex, fast-paced skill in a variable, physically demanding environment. Specifically, we have focused on the problems of coping with major changes in mountain biking technique and equipment in an individual at a relatively early stage of skill development, with some experience but beginner-level technique. The ability to acquire complex, fast-paced sensorimotor skills is highly developed in primates, with arboreal lifestyle being a primary ecological basis for this. Primates show high levels of behavioral flexibility, manifested in foraging strategies, communication, social behavior and tool use. Amongst primates, humans are exceptional in showing an extremely highly developed capacity for flexible skill acquisition. This plays a fundamental role in human lifestyles. Human skills tend to be highly social, often acquired through social learning and exercised in social contexts.

Cognitive control plays a central role in learning complex novel skills. It is accordingly likely that in human evolution there has been selection on the capacity for cognitive control for functions that contribute to complex skill learning. Some contributions of cognitive control are probably not specific adaptations for skill learning, but rather more general abilities that contribute to skill learning amongst other important adaptive human capacities. Conversely, skill learning is likely to shape the mechanisms of cognitive control generally, both via selection and activity-dependent plasticity. During skill learning, new capacities for cognitive control are acquired, so some of the capacities and mechanisms involved in cognitive control may be more apparent (see for example, Bicknell, 2021 ; Bicknell & Brümmer, 2022 ; Downey, 2022 ).

Our rationale for focusing on the ability to cope with major changes in technique and equipment is that this is a demanding context which should illuminate mechanisms for control of performance and skill learning.

Testing the classical procedural-cognitive contrast

We can make an initial framing in terms of the standard distinction between implicit and explicit processes. Broadly, the kind of procedural or implicit learning usually associated with skill acquisition occurs slowly and incrementally, and the abilities which result are relatively inflexible (Reber et al., 2019 ). These mechanisms should hence be unable to respond to large, rapid changes. In addition, the classical view of skill claims that cognitive processes are ill-suited to the control of skilled action. Cognitive control processes should therefore struggle to cope with major novel changes to the way a task is performed. In contrast, Mesh theory claims that cognitive control of action improves with skill learning and incorporates several features which allow relatively efficient control, including representation of the causal structure of action problems and metacognitive and other kinds of performance awareness (Christensen et al., 2015 , 2019 ). It is consequently better placed to explain relatively fluent adjustments to major novel changes if, as we expected, these do occur.

Forms of flexibility

We also need a more fine-grained framing of the rationale for looking at large changes in technique and equipment. This is because numerous motor control mechanisms have been proposed which are capable of flexibility in various forms. In the situations we're concerned with it is plausible that multiple forms of flexibility play important roles.

One of the simplest forms of flexible control is feedback control. Here, feedback corrects deviation from a reference. The goal can be achieved from any point in a state space 'basin' defined by the abilities to detect deviations and produce control inputs which drive the system towards the goal state. Trace theory (Adams, 1971 ) and the control law model (Fajen, 2005 ; Gibson, 1979 ) are theories of skilled action production based on feedback. Another class of control system achieves greater flexibility by means of generalised sensorimotor mappings. Theories of this type include Schmidt's ( 1975 ) schema theory , the internal models approach (Daniel M. Wolpert & Kawato, 1998 ; Wolpert et al., 1995 ), Optimal Feedback Control theory (Todorov, 2004 ), and the coordinative structures of dynamical systems theory (e.g. Kelso & Zanone, 2002), which generate high order patterns in movement. The perception-motor mappings are generalised in the sense that they generalise from practiced to unpracticed contexts based on similarity.

Calibration is a form of flexibility in which the parameters of a control function are adjusted to suit the context. A different kind of flexibility is achieved by restricting regulation only to variables that affect goal-achievement (Todorov, 2004 ; Tseng et al., 2003 ). Restricting control only to variables that affect goal-achievement is resource-efficient and can have the effect of decoupling variables important for the goal from those which aren't, which buffers performance against variations in unimportant variables. Yet another kind of flexibility involves control of the way a strategy is executed. Impedance control , or the control of the stiffness of the motor system, is an example (Franklin et al., 2008 ). Thus, a given action type can be performed while maintaining varying degrees and forms of stiffness. Control of stiffness can have a variety of functional benefits. For instance, increasing stiffness can reduce the degrees of freedom present in a movement and hence simplify the movement problem, while reducing stiffness can reduce the negative consequences of impacts that arise as a result of errors.

A key form of flexibility, sometimes called equivalence , involves the ability to achieve a given goal using multiple qualitatively distinct movement patterns. Ranganathan et al. ( 2020 ) identify two kinds of mechanism capable of this kind of flexibility. The first type involve high order task-specific coordination functions which constrain the dynamics of the system in a way that allows multiple coordination modes, or synergies . The second consist of explicit strategies (Christensen & Bicknell, 2019 ; Christensen et al., 2015 , 2019 ; Shepherd, 2017 ; Taylor & Ivry, 2012 ). Ranganathan et al. ( 2020 ) suggest that flexibility is likely to be based on synergies when the variations in movement pattern are relatively small and the task constraints can be learned over a long period of time. Explicit strategies are likely to be used when the variations in movement patterns are large and the task constraints change over short time scales. The situation we are examining has these features so it should evoke the use of strategies.

Problem solving

The key question that then arises with regard to action strategies is how they are formulated and controlled using cognitive processes. As we saw, according to the classical view (e.g. Anderson, 1982 ) cognitive control includes no specialisations for action control. An alternative view, adopted in Mesh, is that control of action is one of the primary functions performed by cognitive control, and it incorporates mechanisms acquired through evolution and learning that are specialised for this role. These mechanisms engage in problem solving processes which represent the structure of action problems and construct solution strategies. Cognitive control then governs the implementation of these strategies.

Problem solving is a relatively understudied issue in motor control research, which is surprising on ecological grounds given the high degree of diversity and flexibility of human motor abilities, and the importance of flexible motor abilities in human evolution. Bernstein's ( 1996 ) concept of dexterity is an exception to this neglect. Bernstein characterises dexterity as the ability to find solutions to novel motor problems, and he regards it as central to human motor ability. Dexterity in this sense is likely to have fairly deep evolutionary roots, being important for locomotion in arboreal primates, for example. A recent study of squirrels illuminates some of the kinds of motor problem solving that an arboreal lifestyle involves, including adjusting to the flexibility of branches, distances, and the three-dimensional configuration of space and surfaces. 4 Human dexterity shows greatly enhanced range and depth, in the sense that humans are able to solve a much wider variety of motor problems and much more complex problems (Gibson 1979 ).

Causal representation and problem solving

Mesh treats the capacity for flexible problem solving as central to human skill and proposes that it incorporates three key ingredients. Firstly, there is the ability to flexibly represent problems as causally structured wholes by means of causal models . These represent problems as structured wholes incorporating constituents and relations. At least some constituents must be represented as able to vary in state, requiring a distinction between variables and the values that variables can take. We will refer to some variables as parameters , where by this we mean key features of a type, such as an action type. The representation of causal relations requires that parameters are represented as related by production relations, such that, in the simplest case, a change in the of state of a particular parameter produces a change in the state of a second parameter.

Most accounts of action representation have focused on the representation of action form , such as motor patterns, schemas and automated procedures (Anderson, 1982 ; Buxbaum, 2001 ; Fridland, 2021 ; Pacherie & Mylopoulos, 2020 ; Schmidt, 1975 ; Wolpert et al., 1995 ). These theories propose that, when provided with a goal in a particular context, the motor system predicts what action structure will achieve the goal and then produces that structure. Crucially, there is no representation of the causal relation between the action structure and the goal, or between components of the action structure. In contrast, causal theories claim that action control incorporates explicit representation of causal relations. For instance, the individual might use awareness of the weight of an object in order to estimate how much force to use in picking it up, or awareness of the mechanical properties of a knife blade to control its manipulation when using it as a prying lever. Thus, Goldenberg ( 2013 ) proposes that action control employs a mechanical problem-solving system that represents objects and the body in terms of parts and properties relevant to action problems. Pavese ( 2021 ) argues that these representations of causal principles are practical concepts, or concepts used for intentional control of action. Somewhat similarly to Goldenberg, we suggest that causal control models are employed in action control which explicitly represent causal structure involved in action and help to identify control acts that can achieve goal states (Christensen & Bicknell, 2019 ; Christensen et al., 2015 ).

It is plausible that cognitive action representations include representations of both action forms and causal structure, but causal representation is crucial for intentional control and flexible problem solving. Intentional control of action characteristically involves producing an action with particular features because an action with these features will bring about a goal. The representation of instrumental relations is based on the representation of causal relations. Flexible problem solving involves representing the causal structure of novel problems and finding a means to produce a causal intervention which will bring about a goal state. 5

We can further illustrate the role of causal control models in action control using the example of braking. A causal control model involved in the control of braking will represent key causal factors such as braking strength , grip , speed , and braking distance as distinct, interrelated components of braking. This allows the individual to formulate a wide range of braking strategies, and adopt strategies appropriate to the conditions and their goals. Some of the possible strategies include early braking , in which gentle braking is applied far from the point at which halting or a desired speed is attained, and late braking , in which a high speed is maintained until relatively close proximity to the target point and speed is rapidly reduced by means of hard braking. A much more advanced example of the use of causal representation in formulating action strategies can be seen in a video lesson by the climber Tomoa Narasaki. 6 Narasaki is one of the best boulderers in the world, and has a dramatic style which involves frequent use of leaps between climbing positions that are far apart. These moves are called 'dynos'. In this video Narasaki explains his technique for performing a particular kind of dyno. What is of most relevance here is that he gives a detailed rationale for each component of the technique that is based on a deep causal understanding of the technique. This causal representation includes principles that can be used, not just for this particular technique, but for refining other techniques and formulating new strategies.

Our notion of causal control models is related to Pacherie's (Mylopoulos & Pacherie, 2017 ; Pacherie, 2011 ) concept of executable action concepts . Pacherie ( 2011 ) illustrated the idea of executable action concepts by contrasting them with observational action concepts which may not be executable. Thus, a spectator at an ice-skating competition may acquire the concept of a triple-axel by watching it being performed, but is unlikely to be able to perform it themselves. Pacherie argues that, since possession of the observational concept doesn't guarantee the ability to perform the action, in order to possess an executable action concept the individual must already possess motor representations capable of producing the movement. Mylopoulos and Pacherie ( 2017 ) argue that executable action concepts are executable in virtue of being linked to motor schemas which are acquired through bottom-up learning processes.

A difficulty with this account, however, is that bottom-up motor learning in most cases depends on the action being first produced intentionally. Indeed, it has been a standard assumption that skill learning involves an initial phase in which the action is produced intentionally (Anderson, 1983 ; Fitts & Posner, 1967 ). There are possible exceptions in which the structure of the movement is produced incidentally as part a larger action and consolidated by bottom-up learning (Sun et al., 2001 ). Sequence learning tasks such as the serial reaction time task (SRTT) are designed to exploit this possibility as a means for studying implicit learning. In the SRTT the participant presses buttons in response to cues (Robertson, 2007 ). They are not informed that the sequence of cues/button presses contains a pattern. On subsequent tests participants are faster at the task, indicating they have some learning of the sequence. It was hoped that tasks like this would reveal purely implicit learning, operationally measured as speeded response combined with lack of explicit awareness of the sequence. However, participants do learn some of the sequence structure explicitly while performing the task and this appears to fully explain speed improvements (Krakauer et al., 2019 ). Thus, even in tasks specifically designed to elicit implicit, bottom-up learning it has proven difficult to do so. Masters and colleagues have attempted to develop training methods which allow the movement patterns of a skill to be learned largely or entirely implicitly (Masters, 2000; Poolton et al., 2006). However, it has proved difficult to apply these methods to complex skills (e.g. Poolton & Zachry, 2007). For most complex real-world skills like performing a dance step or changing gear in a manual car there is no other practical way to initially generate the action than by intentional control.

Thus, for the most part, the individual must already be able to intentionally produce the action before bottom-up motor learning can start to occur. Bottom-up motor learning refines and consolidates movement patterns that are intentionally produced. It doesn't construct novel movement patterns. Pacherie is right that to develop an executable action concept the individual must already possess motor representations capable of producing (at least an approximation of) the reference movement pattern. But in the initial stages of motor skill acquisition the individual does not have an integrated motor representation that is specific to the movement pattern being learned. The individual usually needs to construct a cognitive representation of the desired movement as an integrated structure assembled from intentionally controllable motor components.

This brings us to a crucial phenomenon that any theory of action and skill must accommodate, namely the ability to intentionally produce novel movement patterns. The basic level of control in intentional action is not the ability to produce 'basic actions', in the philosophical sense, it is the ability to intentionally control movement parameters such as postural parameters, direction, distance, speed, force, and so on. To intentionally produce novel movement patterns as functionally integrated wholes it is necessary to represent causal interdependencies amongst at least some of these parameters, such as between position, distance, speed, time, and force. 7 Thus, causal control models of the same kind as we described for braking are used in the fundamental control of movement.

Infants and young children learn a repertoire of basic coordinated actions, including pointing, reaching, grasping, manipulating, stepping, and so on. These actions are basic in the sense that they come to function as units which will be employed in the construction of more complex actions. They incorporate stereotyped movement patterns and their control is likely to incorporate linked conceptual and motor schema representations in the way that the Mylopoulos and Pacherie model describes. But they are intentionally controllable , in the sense that their parameters can be intentionally adjusted to achieve a variety of goals. To explain this we need to supplement the Mylopoulos and Pacherie model of executable action concepts with the account of causal control models that we are proposing. Typically, in skill acquisition, such as when learning to play a musical instrument, more basic intentionally controlled movement capacities are adapted for the specific demands of the skill. During skill learning cognition leads in the construction of new actions to suit the task demands. Once a novel action structure has been constructed, consolidation and refinement will occur across the whole control system, including the formation of integrated motor schemas and the formation of integrated causal control models.

Translation between representational systems

Theories of action control face the problem of understanding translation between and within representational systems during action control, including multiple perceptual modalities, visual and verbal linguistic representations, emotion experience, gestures, computer and web interface 'languages', the iconography and signaling conventions of driving on roads, maps, music representational systems such as notation and tab, and so on. 8 Hierarchical models of intentional control, such as that of Mylopoulos and Pacherie ( 2018 ), must explain translation across different levels of abstraction. Translation across all these representational systems and levels plays a central role in problem solving and flexibility. Abstract goals and plans must be interpreted in more concrete situational terms. Flexibility hinges on being able to vary the way actions are performed in relation to features of the situation while realising the features of action critical to achieving the goals. Learning involves abstracting action features from instances in a way that allows varied concrete implementations. Recent philosophical theories of the architecture of action control, such as the DPM and Mesh models, have not so far addressed these issues but there is a long tradition of work on them in other fields (Fitch & Martins, 2014; Lashley, 1951; MacKay, 1982; Ondobaka & Bekkering, 2012). 9

A connected issue that has received recent attention in philosophy is that of the perspective of the representations involved in action control. Pavese ( 2019 ) develops the idea that some representations have a distinctive practical mode of presentation or practical perspective . She argues that these are imperative representations which specify a method of performing a task in terms of the abilities of a system that can implement the method. She claims that motor commands and motor schemas are examples of this kind of representation. We agree this is an important form of practical perspective, but we need to also understand the form of practical perspective of the representations used in the problem solving by which schemas and motor commands are formulated and evaluated. We'll call this the perspective of control . The perspective of control encompasses all of the phases, levels and aspects of control, many of which have their own perspectival characters, including those of distal decision-making and proximal control of execution.

The structure of control

In addition to causal control models, previous explications of Mesh have identified two further components of action control: forms of higher order performance and metacognitive awareness. Before describing these in detail, however, it will help to clarify the structure of control. Mesh is similar to the DPM model of Mylopoulos and Pacherie ( 2018 ) in depicting action control as involving a hierarchical structure. Mesh has not yet been very specific about the details of the nature of the control involved in the hierarchy, whereas the DPM model, based on Bratman's account of intentions (along with Searle ( 1983 ), Brand ( 1984 ), and Mele ( 1992 )), specifies a control organisation that involves multiple levels of intentions which are responsible for specific aspects of action control. In particular, a distal intention, commonly formed outside the action context, represents the overarching goal of the action. Proximal intentions are formed which specify how the distal intention is to be implemented in the context of performance. Motor intentions specify the motoric means by which proximal intentions are implemented. Here we extend Mesh by specifying the structure of control in more detail. This account shares with the DPM model the idea that intentional action often involves a hierachical goal structure, but departs from it in certain respects which in part stem from a departure from Bratman's model of intentions. 10

One way to conceptualise control is as the ability of the agent to achieve its goals. We'll call this the goal-based conception of control. Mylopoulos and Pacherie ( 2018 ) and Shepherd ( 2021 ) employ this conception. 11 A different way to conceptualise control is as the ability of an agent to solve the problems that it faces. We'll call this the problem-based conception of control. Both concepts of control are useful but the problem-based concept is important for understanding adaptive control systems and the structure of control in skill learning. Thus, when we perform a full analysis of a biological control system we need to determine both the proximal goals (the represented goals) and the ultimate goals, which are solutions to adaptive problems faced by the biological agent. These problems are to a significant degree independent of and prior to the explicit goals of the control system. Solving them is often obligatory or very difficult to avoid. The relationship between proximal goals and adaptive problems will often be imperfect, and evolution will generally tend to bring the represented goals of organisms into alignment with their adaptive problems. Proximal criteria used in the control of eating include satisfying hunger and enjoyment of the experiences of eating. The primary adaptive goal is nutrition. Humans can adopt conceptualised nutrition as an explicit goal of eating but they need not. The proximal control criteria for eating can be satisfied while the adaptive problem is not. In cases where conceptualised nutrition is a goal of eating it may correspond imperfectly to objective nutrition. Thus, goal-based and problem-based control can be dissociated.

Humans are a highly social species and are exceptionally flexible in developing varied lifestyles and technologies which have allowed the colonisation of almost every kind of terrestrial environment on earth. This flexibility in lifestyles is founded on an exceptional capacity for flexible skill learning. Human evolution has thus endowed us with skill learning capacities which are extremely good at solving the ecological and social problems we face. Uncertainty plays a central role in this flexibility. Humans face a fundamental and pervasive uncertainty concerning their goals. Their goals correspond imperfectly to their problems and they must learn about the structure of the problems that they have. Problem discovery thus plays a central role in skill learning. Skill learners typically begin with poor representations of their problems. Their goals correspond imperfectly to their problems and they must learn about the structure of the problems that they have, and learn to form better goals. This learning occurs at every level, from the specific problems involved in performing particular actions up to and including self-conception, whether to engage in the skilled activity at all, and to what degree.

More specifically, uncertainty and problem discovery play a key role in the structure of action evaluation. On a goal-based hierarchical model of control, such as the DPM model, performance at a given level of control is evaluated with respect to the goal at that level and to higher level goals. Thus, the success of motor performance is evaluated with respect to whether it achieves the goal specified by the M-intention, and whether this satisfies the goal specified by the P-intention. However, there are certain phenomena which arise quite commonly during skill learning which don't fit this model very well. An action can go according to plan yet be assessed negatively. For example, an inexperienced guitarist might perform with a band at a gig in a way that they have planned to, and which they consider to be aligned with their norms for playing well, yet later evaluate their performance negatively when they review a recording. This later evaluation may be based on performance norms they had not previously considered, but which are highlighted when they assess their performance from the perspective of a listener and compare it to performances of more advanced players they admire. For example, they might realise that their playing was overly busy, failing to complement the song, and too loud, overshadowing the rest of the band. 12

An action can also go contrary to plan yet be assessed positively; a mistake which proves to be a 'happy accident'. For instance, you might accidentally shake out more hotsauce on your eggs than intended, yet regard the outcome as superior to the intended quantity of hotsauce.

These possibilities can't be explained if the only evaluative criteria are the goals specified by the intentions.

The action evaluation system

To understand these phenomena we need to recognise a broader set of evaluative criteria. While it is often the case that a specific explicit goal operates as a primary focus of action selection and regulation processes, this goal is only one item amongst a complex set of criteria used to evaluate the action. Some criteria are low level and generalised. Thus, all actions are evaluated for efficiency, regardless of whether efficiency is an explicit constituent of the content of the goal of the action. Other criteria are higher level and also generalised. A bluegrass musician will evaluate their playing according to their internalised aesthetic norms for bluegrass music. Some norms are specific to the action type, such as technique criteria. Some norms are specific to an individual, such as a personal playing style. In the performance of any given action an ensemble of criteria will be operative in evaluation processes. Criteria other than the primary goal can be used to evaluate the primary goal and its implementation. These additional criteria are themselves imperfect and subject to learning. A novice has evaluative criteria for the skill which are impoverished and poorly reflect the norms of the skill domain. Experts often have very rich, articulated evaluative criteria. For this reason, instruction and other forms of social feedback can play a vital role in guiding learners. Techniques for self-assessment which use an external perspective, such as recording and analysing performances, are also very valuable because they allow the individual to better apply performance norms they have acquired from an observer's perspective to their own performances.

Thus, we add to Mesh the proposal that skilled action evaluation is based on an action evaluation system (AES) which develops during skill learning. 13 The AES plays a role in the cognitive processes of intention formation and in the control of action execution. Action evaluation is holistic: no single criterion has strict dominance (e.g., there is no strict master goal) and the weighting of criteria can vary across contexts. The breadth and depth of evaluation will vary across contexts, but a complex set of criteria are often operative in the control of action execution. 14

In this respect the account departs from Bratman's model of intentions. As described above, Bratman's model has a strongly punctate structure in which there is a phase of deliberation which culminates in commitment to an intention, followed by a phase in which cognitive processes are focused on implementation of the intention and evaluation of it is bracketed. Bratman's rationale for the bracketing of intention evaluation is based on cognitive resource limitations: he claims that it is not possible to continuously determine the best course of action at each point in time. But while it is true that it isn't possible to perform a comprehensive analysis of the best course of action at each point in time, this doesn't imply that intention evaluation must have the punctate structure of his model. Bratman recognises that intentions may be reconsidered when stakes are high and 'new information comes in', but he regards this as exceptional. He says that it is reasonable to have a default presumption in favour of plan stability rather than reconsideration. Concerning the circumstances in which reconsideration is reasonable, he writes:

Sometimes the stakes are quite high, and there is an opportunity for calm and careful reconsideration of one's prior plan. It seems plausible to suppose that it is in the long-run interests of an agent occasionally to reconsider what he is up to, given such opportunities for reflection and given that the stakes are high, as long as the resources used in the process of such reconsideration are themselves modest ( 1987 , p. 67).

Our model is very different. It is common to have highly imperfect information and it is hence adaptive to continue to evaluate intentions after they have been adopted, including during action execution. This allows them to be flexibly modified and abandoned as more information is gained and as circumstances change. Here we need to distinguish between evaluation of implementation intentions involved in carrying out a plan and evaluation of the overarching intention. We claim that evaluation occurs at every level. The breadth and depth of evaluation varies, and it is certainly true that there is greater opportunity for deep and wide evaluation before and after performance compared with during, but nevertheless, higher levels of control can be 'in play' during performance. Thus a professional bike rider might, during a race, re-evaluate their ability, re-evaluate their strategy for a particular obstacle, re-evaluate their race strategy, or pacing plan (Christensen & Bicknell, 2019 ; Sutton & Bicknell, 2020 ). More broadly, an athlete may re-evaluate their strategy for the season, and might even re-evaluate their commitment to racing at this level. For instance, an older rider near the end of their career might switch during a race from assessing themselves as still being competitive at the highest level to no longer being competitive, and decide on this basis to retire. At the other end of the skill career time line, we can expect that evaluation of commitment to the skill to commonly occur during performance during early phases of acquisition and at key career stages.

The basis for such evaluation is the individual's AES. A well-developed, adaptive AES represents relevant evaluative criteria at various stages of intention formation and action performance.

This model of control differs from the DPM model. In keeping with Bratman's model, on the DPM model distal intentions are ascribed the function of terminating practical reasoning about what to do. Evaluation of success is goal-based and top-down. In contrast, our model places more weight on bottom-up processes in which higher-level goals are revised in response to information gained during action execution. Evaluation of intentions does not necessarily terminate with their adoption. Evaluation of success is not only with respect to achieving the goals specified by intentions. We think the DPM model can be readily amended to accommodate the phenomena we're describing, but these are nonetheless features of control that have high significance because they play important roles in learning and flexibility.

Performance and metacognitive awareness

Performance and metacognitive forms of awareness play a key role in action evaluation. In contrast, on Bratman's account reconsideration is based on habits and dispositions, deliberation, or by policies. He thus fails to recognise the importance of such forms of awareness. Confidence, for example, can have performance and metacognitive forms. Performance confidence is awareness of the likelihood of action success. Metacognitive confidence is awareness of the extent to which sufficient information is available for effective decision-making and control. When both kinds of confidence are high, as they are likely to be when a philosophy professor makes a plan to go to the library to borrow a book, depth of evaluation can be low during decision making and performance. When these forms of confidence are much lower, as they will be when learning a new mountain bike riding technique, depth of evaluation will tend to be higher in all stages of action.

According to Mesh, causal representation contributes to performance and metacognitive awareness. A causal control model represents the causal features of the situation relevant to action decisions and control. In the case of braking this will include causal features such as the nature of the surface, the amount of grip, and the amount of braking force that can be applied. Thus, the causal control model is the basis for awareness of the performance envelope , or range of performance states that are available. In this case, awareness of the performance envelope includes awareness of the range of braking forces that can be applied without losing grip. Awareness of the performance envelope serves as a basis for evaluating whether to continue with an action during performance. If a breakdown is likely it may be best to abandon the action. At a more finegrained level, awareness of the performance envelope allows the formulation and modification of action strategies. If grip proves to be unexpectedly high, for instance when using a new type of tire, braking strategies can be modified accordingly. When the individual is uncertain they may adopt a conservative strategy and attempt to gather more information (Christensen & Bicknell, 2019 ). Thus, if they are unsure of how powerful their brakes are, for example if they are on a new or recently serviced bike, they might use early braking or ride at a slower speed as they assess the performance of the brakes. If the individual is confident they may operate closer to the edges of the envelope.

Social and cultural learning and the degrees of freedom problem

The standard approach to skill is individualistic. When seeking to understand advanced skills the focus is on the autonomous abilities of individual experts. When seeking to understand skill learning the focus is on the processes by which an individual's abilities are transformed from novice to expert. Of course, it is understood that teaching and other forms of social learning play a role in skill learning. It's understood that some skills, such as theatre, dance, music, and team sports, involve collective action. Indeed, there is burgeoning transdisciplinary interest in collaborative experiences of, and influences on, skilled performance (Bicknell & Sutton, 2022 ). Nevertheless, skill theories treat social phenomena as secondary, or subtopics of skill. Mesh has followed this individualistic orientation (but see Christensen & Sutton, 2018 ).

Attention to the larger evolutionary and ecological context indicates that we should see individual and social aspects of skill as fundamentally interwoven. Human skills are exceptionally flexible, complex, and are generally acquired and practiced in highly social ways. These associations are not accidental. The flexibility of human skill is founded on a sensorimotor system capable of many 'degrees of freedom', being able to adopt an extremely large number of configurations that can be structured in many ways over time. The diversity and complexity of human skills, in comparison with other species, is possible only because of this underlying potential. But the high dimensionality of the human sensorimotor system, combined with the complexity of many skills, presents difficult problems for learning and control. The learner confronts an extremely large problem space in which solutions must be found. The degrees of freedom of the sensorimotor system must be steered in ways that realise solutions (Bernstein, 1996 ). Learning thus presents extremely difficult search and control problems.

One way that learning is made tractable is to acquire skill progressively, beginning with basic abilities that present relatively simple problems and moving to progressively more complex abilities that build on the simpler abilities (Bryan & Harter, 1899 ). Another way that learning is made tractable is by making it social. Experts and peers provide models of high quality solutions. Teachers can guide learners through the extended pathways to complex solutions. A skill community is able to explore the space of possibilities and solutions far more effectively than a lone individual. When a member of a community discovers a superior solution or other form of valuable skill knowledge, this can be propagated through the community (Goodwin, 1994 ). Many discoveries can be combined into complex, sophisticated methods.

Rival expectations

Based on the preceding discussion we can distinguish between two main contrasting sets of expectations for the situation we are investigating. Based on the classical view we should expect major changes to technique and equipment to be highly disruptive for two reasons. Firstly, automated forms of flexibility should be unable to cope with these kinds of changes because they require qualitatively new sensorimotor patterns. Secondly, cognitive processes should also struggle to cope because they employ general purpose representations and problem solving methods. They are hence poorly suited to skilled action control, and should be overwhelmed by the alterations to the complex relations involved in action production. Mesh yields a different set of expectations. An individual with a significant amount of experience will have developed mechanisms for the cognitive control of action which allow relatively fluent coping based on problem solving. This problem solving will be based on causal representation and an action evaluation system employing a complex set of criteria. Control will be more flexible than depicted by Bratman's model, with continuous evaluation even at higher intentional levels. This problem solving will be imperfect, however, especially in an individual whose technical abilities are fairly basic. Social guidance from an expert can supplement individual problem solving by directing the learner to better solutions.

The nature of our study

We conducted a field study investigating responses to major changes in a rider with several years mountain biking experience but beginner-level technique.

We employed a researcher-practitioner approach, in which the authors served the dual roles of investigators and participants (see Bicknell, 2021 ; Downey, 2022 ; Downey et al., 2015 ; McIlwain & Sutton, 2014 , 2015 ; Nemani & Thorpe 2016 ; Olive et al. 2016 ; Ravn, 2022 ; Samudra, 2008 ; Spinney, 2006 ; Sutton & Bicknell, 2020 ). This approach brings attention to theoretically and ecologically significant aspects of skilled action in contexts that are difficult to capture in the laboratory, from the armchair, or when the researcher is unfamiliar with the nuances of a particular community of practice. Our experiences were undoubtedly shaped by our theoretical interests, and the evidence should be viewed as exploratory and tentative. Validation of the kinds of phenomena we describe is needed using other methods. These include broader ecological sampling and laboratory investigation. It is especially important that ecological methods be employed which use structured data gathering in close temporal proximity to performance with theoretically naïve participants. But it must be emphasized that all methods have strengths and limitations. The best overall research strategy is to use a large methodological toolkit and seek to buttress each investigative mode with convergent evidence from others.

The two authors of this paper have differing levels of mountain bike expertise. Kath has been a mountain bike rider for over twenty years. She has worked for global cycling media for more than ten years, taught skills clinics, raced domestically and internationally, and has written academic papers and a PhD on the sport. Wayne, in contrast, has very limited mountain bike riding experience. He is not a raw novice; he has extensive experience of recreational and commuting riding on roads and about fifteen years previously he spent about a year mountain biking regularly, riding once or twice a week. This experience included twisty 'singletrails', tracks roughly the width of a foot trail, with rutting and differences in grip due to the type of dirt underneath his wheels (eg. loose and skatey or smoother ‘hardpack’). He had largely avoided more 'technical' trails including features like ‘rock gardens’ and logs. With respect to mountain bike riding he was self-taught and as a result had not acquired some important basic mountain bike riding techniques. As part of research for a previous paper (Christensen et al., 2015 ), he read a mountain bike instruction book and gained some familiarity with basic mountain bike techniques this way. Kath had also given him some instruction during the study. He had not, however, spent a significant amount of time practicing these techniques or received any further ‘live’ riding instruction.

The fieldwork session was designed to explore and document Wayne’s experience of the trails through an initial ride with no input from Kath, followed by two major changes. After observing Wayne ride an initial series of beginner-intermediate singletrails, Kath provided instruction on core mountain bike riding techniques, which Wayne then attempted to employ. Secondly, after riding the track on his own bike, Wayne then switched to Kath’s bike, which was a more modern and capable design.

In more detail, at the time of the study Wayne’s bike was approximately ten years old, an aluminium ‘cross country’-style hardtail (meaning no rear suspension), with 26″ wheels, hydraulic disc brakes, and basic front suspension in need of a service. Kath’s ‘trail’-style bike was a few months old. The design reflected substantial changes in bike technology, trends and manufacturing materials. These included: larger 29″ wheels with wide 2.4″ tubeless tyres, which roll over obstacles with more ease and traction compared to Wayne’s smaller wheels with narrower tyres; a more stable and relaxed geometry, which adds traction and confidence on climbs and descents; far more sophisticated front and rear suspension for better traction and compliance, and other modern features such as a 1 × 12 drive train (so no shifting is needed with the left hand) and a dropper seat post which allows the rider to press a lever on the handlebars and move the seat downwards so it doesn’t catch on their thighs when they move their body toward the rear of the bike while descending. The brakes were far more powerful, the frame material (carbon) more compliant, and the handlebars substantially wider, again providing enhanced stability and control. Kath and Wayne are a similar height, meaning they both ‘fit’ the same size frame. However, when Wayne switched to Kath’s bike the contact points were adjusted for Wayne, with the seat height being modified and his own pedals used.

The trail used for this investigation, a popular cross-country loop at the Ourimbah trail network in New South Wales, Australia, was chosen on the basis that Wayne could tackle it with reasonable safety, with guidance from Kath, but which contained obstacles that were more challenging than Wayne's prior experience made him comfortable with. During the ride, Kath gave Wayne the kind of instruction and induction into the mountain bike subculture that would be characteristic of an experienced rider taking a friend on a ride for the first or second time. For example, when Wayne was concerned about riding over a log obstacle she explained and demonstrated key body movements and the amount of speed needed to do it safely, but also encouraged him to walk the obstacle and try it again later if he preferred.

We took photos and recorded video of Wayne’s successive attempts at challenging obstacles on both bikes, and recorded video of Wayne describing his immediate responses to these experiences. We met the next day to write notes on the experience.

Riding the Ourimbah cross-country track

There were two aspects that stood out as especially noteworthy in Wayne’s experience: a change in technique that dramatically improved descending steeper trails, and adaptations to the increased performance capabilities provided by the second bike, particularly due to its stability over rough terrain.

At the beginning of our fieldwork session, Wayne rode a short loop of ‘singletrails’ without instruction or interference from Kath, who rode behind him, watching and observing. These trails included several relatively steep descending sections of trail, linked together by narrow, winding, rocky connections and the occasional smooth section for the rider to relax and catch their breath. (Understandably) Wayne was riding somewhat nervously and cautiously. He felt that his approach to riding these trails was reasonable given his limited overall experience and that he had not ridden a mountain bike trail in a number of years. Riding behind him, however, Kath could identify specific technical problems. She could see and hear the rear wheel skidding and noticed that Wayne’s body position (and centre of gravity) was quite far forward on the bike. This puts a lot of pressure on the front wheel, which causes several problems while descending, the most severe of which is that it increases the risk of flipping over the front of the bike. In the mountain biking subculture this is referred to as an ‘OTB’ (over the bars)—the standardization of the term suggesting it is a fairly common experience but one to be wary of.

We paused on a long flat section of fire road to discuss the experience of the first section of trails. Worried that Wayne was going to injure himself, Kath provided instructions that would help him ride the section more smoothly and safely on the next attempt. She explained a technique for descending steep obstacles, which involves keeping the feet balanced evenly on the pedals while the rider moves their bodyweight rearward on the bike to maintain balance and stability. She explained this to Wayne verbally, along with an arm gesture showing the effect of weight on the bike in relation to the terrain. She used the cue words ‘butt back’ and ‘weight back’ as a shorthand way to emphasize and direct this technique after the initial description of what to do. Taking advantage of the less threatening and challenging terrain provided by the wide, flat fire road, Kath showed Wayne an exercise which encouraged him to experiment with how far he could move his body rearwards while maintaining momentum on the bike. This involved riding in a straight line at a moderate speed, moving his butt behind the saddle and allowing it to graze the rear tyre. This exercise was designed to increase his awareness of how much space he had to move rearward on the bike, what this felt like in practice, and how this sensation differed to what Wayne thought was the near-maximum amount he could get his bodyweight behind the bike. 15 The sound and feel of grazing the tire provide aural and kinaesthetic cues that signify the rider has succeeded in the task when it is not possible to see, visually, how far back they have reached.

We then rode the same short sequence of trails again. Wayne now focused on getting behind the saddle during any steep downward sections of trail. The result was a dramatic improvement in the controllability of the bike. This transformed the experience of sustained descents and short, sharp drops or ‘rolldowns’. Rather than feeling threatening, the experience of riding these obstacles felt relatively comfortable—it felt less steep, less rough, less like he was about to have a crash. After applying the technique cautiously to a rocky ‘stepdown’, Wayne immediately began to apply the same technique to other steep, downward sections of the trails: log rolls, steep downward corners, rocky rolldowns, anything where the front of the bike is much lower than the rear of the bike while riding a trail obstacle.

After a longer lap of the trail on Wayne’s bike, Kath gave him her own bike to use for the second lap. She had another bike waiting in the car to facilitate this exercise. The technological differences described above between Kath’s bike and Wayne’s older model bike indicate that Kath’s bike would be more stable, compliant, absorbent and confidence inspiring. While this makes sense on paper, Wayne was nevertheless astonished at how much more capable the bike was in practice. The most immediately striking feature of the bike to him was its greater stability, and the fact that this allowed much better low speed control than his own bike. In comparison, the relative instability of Wayne’s own bike meant it would often feel like it was going to tip over at slow speeds.

The combination of greatly enhanced stability and compliance had a dramatic effect on Wayne’s experience riding the trail. He was able to ride faster on bumpier sections, with the bike soaking up rocks and gaps between them that would produce strong jarring on his own bike. Within 10–20 min of riding this bike his confidence greatly increased. Many descents and ascents he had struggled on while riding a lap of the trails on his own bike—rooty straights, rocky windy uphills, small rocky step-ups, uphill corners, rutted entries into corners, descents littered with a messy array of small obstacles—were experienced as being much more ‘ridable’ than they had been earlier that same day. The bike maintained traction more easily allowing Wayne to pedal and guide it through the obstacle with more control, confidence and ease. He continued to approach some obstacles cautiously, however, and Kath spent some time teaching him to ride over a log, which looked difficult to him but in fact was not. His main difficulty was simply overcoming his fear of the obstacle so that his approach was fast enough to carry him over the rocky ramp that led to the log itself. We make no claims that Wayne was able to ride all sections of the trail, or ride flawlessly ever after. He subsequently crashed when he misjudged a rocky section later in the day, bruising his ankle and wrist and acquiring some grazing.

Finding theory in action

In this section we use the theoretical concepts introduced in Sect.  2.2 to characterise the processes by which Wayne adjusted to the new technique and the new bike. Wayne was familiar with these concepts, which makes it easy for him to describe his riding experiences in these terms. This familiarity also undoubtedly influenced Wayne's experiences during our field study. However, based on Kath’s extensive experience teaching skills clinics, and riding with mountain bikers at a diverse range of skillsets, we think that the kinds of problem solving Wayne engaged in are not unusual. In particular, his experiences of identifying control problems and experimenting with solutions were in many respects fairly typical for a beginner rider.

Simple forms of adaptation

Wayne adapted rapidly to the new technique and new bike. Some of this adaptation involved relatively simple forms of flexibility. Simple calibration changes in important parameters, such as braking forces and braking timing, played a role. Both of the major changes improved stability, which had the effect of making feedback control more tolerant or less 'twitchy'. Feedback control processes quickly recalibrated for the new tolerances. As riding became more manageable Wayne could relax more, and reduced bodily stiffness is likely to have made control easier; bumps and other perturbations which might have been jarring and disruptive would now be absorbed more effectively through his limbs. Experience selectively highlighted key parameters, allowing control to be more focused.

Problem solving using causal knowledge and metacognition

Nevertheless, the changes in technique and bike were large enough to require adjustments by means of strategies formulated using problem solving. Wayne's ability to adapt to large changes hinged critically on an ability to formulate and implement new action strategies 'on the fly'. We can illustrate this by describing in detail a change in riding strategy associated with the technique change of getting behind the saddle. Although Kath wasn’t aware of it, the rear wheel skidding that she observed during Wayne’s initial ride was partly the result of a deliberate braking strategy that he thinks of as ‘tail dragging’, which involves using primarily the rear brake. After adopting the behind-the-saddle technique Wayne switched to an equalized front-rear braking strategy. Wayne was familiar with rear-wheel-based braking from riding as a child, with this experience including riding ‘back pedal’ brake bikes and with using the rear brake to skid out the back wheel on loose surfaces when coming to a halt, a satisfying and popular maneuver. He knew, however, that equalized braking is regarded as the superior technique and he had generally used it in his previous mountain bike riding. He nevertheless initially used rear-biased braking as an improvised strategy in response to control problems that he was experiencing. Wayne wasn’t aware that his weight was too far forward, but he was aware of some of the consequences of this. The load on the front wheel caused instability and had the potential to cause the front wheel to lose grip and slide out, resulting in a crash (which Wayne thought of as a ‘washout’). 16 Wayne felt that the wheel was most likely to lose grip under braking and using a rear-biased braking strategy helped to reduce this risk.

Thus, causal awareness played a key role in the adoption of the tail-dragging strategy and the later switch to equalized braking. Here we should note that Wayne's perception of the most immediate and important risk that he faced differed from Kath's assessment. Wayne was preoccupied with the danger of a washout due to heavy front braking, whereas Kath viewed the primary risk in Wayne's riding during this phase as being a front wheel washout or OTB crash caused by insufficient rearward weight. Other riders in a similar situation might have interpreted their risks differently and adopted different strategies. Other strategies which reduce the risk of loss of control when riding a difficult descent include putting one or both feet on the ground and scooting down, using both brakes fairly heavily and ‘inching’ down the obstacle (generally ill-advised), avoiding braking all together and focusing on body position, balance and looking ahead to the exit of the obstacle, or getting off the bike and walking (or sliding) the bike down the obstacle.

Metacognition can also be seen in this example. While Wayne thought there was a danger of the front wheel sliding out, he didn’t know in detail in what conditions this could occur. He was still adjusting to the ‘feel’ of the bike on the terrain and was uncertain about the amount of grip available and the braking forces that could be used. That is to say, Wayne was aware that he lacked sufficient information for good control. Estimating these action parameters is complicated by the fact that they are strongly affected by the nature of the surface, which was variable, and by the fact that in a washout the loss of grip tends to be abrupt. In the face of this uncertainty, tail dragging combined with low speed was a relatively safe, conservative strategy. And it worked! Wayne did manage to ride these difficult sections of trail without crashing. Kath’s intervention was to help Wayne ride them more smoothly, more safely and, ultimately, more enjoyably.

Metacognition also influenced Wayne’s use of tail dragging in another way. Tail dragging is a simple strategy to employ because there is no need to precisely coordinate front and rear braking pressures. Wayne was experiencing high cognitive load because he needed to pick a line with care over the deep rutting of the trail to ensure that the front wheel did not glance off the side of a rut and get channeled down it, resulting in a crash. In addition, Wayne was experiencing significant jarring through the handlebars, and he was concerned that if he hit a bump while braking he might accidentally grab the front brake too hard. Tail dragging simplified the cognitive demands of braking and allowed him to direct more attention to line choice. That is, the choice of strategy was based in part on awareness of excessive cognitive load and the need to reduce this load.

One of the main problems with tail dragging is that it reduces effective braking power because braking force is provided by only one wheel, and because it often results in the rear wheel skidding. This in turn means that speed must be kept low. Partly for this reason Wayne maintained a fairly low speed during the descents, but he preferred to ride at a relatively low speed in any case to allow more time for line choice and to minimize the consequences of a crash. He thus didn’t regard the speed limitations of tail dragging as a reason to avoid it in this context.

However, after Wayne began getting fully behind the seat while descending he switched to equalized braking. This was because the control problems that prompted the tail dragging strategy had been largely eliminated. Independently of any detailed causal understanding, the rearward riding posture leads to several changes in the feel and handling of the bike which provide greater sense of control on steep sections of trail. Cues indicating instability are reduced and handling is improved. The arms are more extended, which reduces unwanted side-to-side rotation of the handlebars and, consequently, the front wheel (compared to the freedom of movement that comes with a larger bend at the elbows). But Wayne was more specifically aware that with his weight now towards the rear there was a greatly reduced risk that the front wheel would lock up under braking. The risk of an over-the-bars crash was also much lower. As noted, this danger had not been at the forefront of Wayne's mind but he was aware of it (he had experienced such a crash previously). Now that his weight was positioned rearwards, and the bike could rotate forwards without pitching him forwards, he became aware that an OTB crash was a lot less likely.

Indeed, the front wheel could now rise and fall much more easily as it tracked over obstacles. This made line choice less critical because there was less chance that the front wheel would glance sideways when it struck the side of a rut. This reduced cognitive load. There was less jarring through the handlebars, making it easier to judge and execute braking pressures. With braking distributed between front and rear, overall grip was increased and there was less chance of either wheel skidding. Wayne became more confident about applying much stronger braking pressures than he had previously. And since the improved handling made similar riding problems more tractable, he became more generally confident about tackling various kinds of descent obstacles. There were distinct limits to these improvements, however, and there were some descents that he still regarded as too challenging. For these he would dismount and walk.

We can illustrate changes in strategy in response to the new bike with the example of a decision to tackle a particular ascent. It was short, relatively steep and had a somewhat loose surface. Wayne tackled it several times on his own bike and once on Kath’s. On his own bike Wayne found the ascent challenging because he needed to begin with high momentum in order to climb it. There were two problems that contributed to this. One was that he had relatively little grip because of the geometry of his bike and the tires. Specifically, on this slope, with its loose surface, if his speed became too slow while using high power pedal strokes the rear wheel could lose traction and 'spin out'. The other was that his riding position on this bike had a relatively high and forward center of gravity, which meant that the bike felt unstable and 'tippy' when riding at slow speeds. If Wayne was going too slow he needed to come to a complete halt and dismount, or he would fall over. The approach to the ascent was downhill, and each time he made the approach he needed to quickly decide whether he had the right line and was going fast enough to make the ascent successfully. He made it up the first time on his bike but stopped on the second attempt because he didn’t think he was going fast enough.

On Kath’s bike Wayne decided to tackle the ascent even though his approach was slow. This point is worth emphasizing because it highlights the way that the different capacities of the new bike led him to use altered riding strategies for obstacles that he had not yet experienced on the bike. Had Wayne been on his own bike he would not have attempted the ascent with the approach that he had at this point. He did attempt the climb because he was confident that the low speed stability of the bike and its grip would allow him to ride it slowly, with less risk of falling over and less risk of losing traction. This proved to be the case. He found that he could come to a near halt during the climb without falling over, and the increased grip of the tires meant that he could use slow, high power pedal strokes without the rear wheel spinning out.

To sum up, Wayne was able to construct riding strategies ‘on the fly’ based on causal and metacognitive awareness. He could form, evaluate and modify strategies based on awareness of factors such as instability and threatened loss of grip. The strategies could take into account multiple factors, reflecting an awareness of how causal factors interrelate in riding. Wayne also selected and adjusted strategies based on sensed uncertainty and risk. This problem solving ability extended to large changes in causal relations associated with major changes in technique and equipment, and hence allowed him to cope with these changes.

It's important to note that Wayne's adaptations went beyond the formulation of specific strategies for particular problems. Wayne showed generalised learning in the sense that each major change allowed him to solve new classes of control problems. As he formulated and implemented new strategies he was also learning about the underlying causal structure of control. He was, thus, extending his causal control model as well as refining it.

Difficulties in adaptation

Difficulties and limitations in Wayne's adjustments are also revealing.

The new bike had only a rear derailleur rather than front and rear. This simplified changing gears but Wayne had well-entrenched gear changing methods which involved coordinated shifting of front and rear derailleurs. It's worth emphasising just how important gear changing is in mountain bike riding. With frequent, rapid changes in gradient and other trail features, it's necessary to change gears often. Smooth, fast riding depends on anticipative gear shifts, especially when the change in gear is large. When encountering a steep slope, for instance, the rider may need to shift from a high to a low gear, and be in the right gear to effectively apply power as speed slows. It's desirable to maintain as much speed and momentum as possible. Wayne's technique for such a situation involved making several shifts in sequence. An initial anticipative shift selects the middle or small front chain ring (lower range gears) and a rear gear that is medium-to-low but high enough to 'catch' the initial phase of slow-down and extend the speed and momentum. Multiple subsequent shifts downward are then made, using the rear derailleur, as slow-down continues, until the right gear for sustained climb is reached. Selecting the wrong gear for a shift disrupts the smooth progression. When the gear is too high or too low the rider will 'bog down' or spin, and either way lose speed and momentum. A further consideration on Wayne's bike was that his gears would sometimes not shift under heavy load, making it important to shift before high power output was required. This was not the case with Kath's bike, which shifted smoothly during high-power pedaling on climbs.

On Kath's bike Wayne had to inhibit his urge to operate the front gear system and reorganise the way that he made anticipative gear shifts. This required heightened attention. An especially attention-drawing feature of the alteration was that in the location where Wayne would operate his front derailleur there was a lever to activate the 'dropper' post. This, in combination with pressure (or lack of) on the saddle, lowered and raised the seat. Lowering the seat during descents gives more freedom to move backwards and forwards as needed. But having the seat drop is not something which the rider will want to happen unexpectedly when trying to change gears or pedal up a hill. When raised the seat would spring upwards to its normal position, and was, in effect, a spring-loaded piston driving towards the rider's crotch. This bike feature was unlike any that Wayne had previously experienced and he found it somewhat disconcerting.

Wayne was able to modify his gear change method and learn to use the dropper post, but these adjustments were more effortful and less smooth than those described in the previous section. Why this should be so raises interesting questions. In general, it's reasonable to expect that some modifications to control are easier to make than others because the control system is better prepared to handle some kinds of change than others. Piaget's ( 2015 ) distinction between assimilation and accommodation is one expression of this idea. In the Piagetian picture increasingly powerful/flexible forms of problem solving ability develop in a progressive sequence as more abstract/deep concepts are learned. The Einstellung (Luchins, 1942 ) and functional fixedness effects (Duncker, 1945 ) are manifestations of the somewhat contrary-seeming phenomenon of increases in rigidity with learning. There is no deep conflict, however. Learning can involve increases in rigidity with respect to some aspects of control together with increases in flexibility with respect to others.

We can develop a preliminary explanation for differences in difficulty in this case which draws on the resources developed in Sect.  2.2 . With respect to the new bike, changes in attributes such as stability and grip were relatively easy for Wayne to incorporate into his riding in at least an initial, basic way. This may be because, although the parameter values were substantially different to his own bike, the parameters themselves, and their role in control, were reasonably familiar. He could therefore adjust his existing methods relatively smoothly. But other differences involved more substantial changes in causal relations and more extensive changes in control operations. Thus, a familiar operation needed to be 'remapped' to a different mechanism with drastically different causal effects, along lines such as {[L-LEVER-OP → F-GEAR-OP] ⇒ [L-LEVER-OP → SEAT-OP]}. Since the operations involved considerable novel structure, the structure needed to be composed in working memory, with implementation and monitoring requiring greater attention than more familiar control operations.

More generally, based on the causal control model account we could expect that skill learning will often exhibit a somewhat Piagetian pattern of increases in generalisation and flexibility which arise as the learner learns to solve varied causal problems. More generalised causal representations develop which capture deeper structure, and more powerful and flexible forms of control develop in order to efficiently manage varied problems.

An even stronger limitation in Wayne's ability to solve the riding problems he was facing is evident in the fact that he needed instruction on the correct implementation of the behind-the-seat technique. During the initial ride he was aware that he was experiencing control problems and rode cautiously for this reason. But he was unable to diagnose the source of these problems to specific technical flaws. At this point he assumed that he simply needed more experience in order to improve calibration and refinement, as opposed to making large technical changes.

This failure in problem solving is all the more striking because he understood the technique abstractly and believed he was implementing it. When Kath explained verbally the technique of getting behind the saddle during descents, the information was already familiar. He had not known of the technique when he was mountain bike riding by himself many years previously, but he had since learned of it from a mountain bike instruction book. He knew that good riding technique involves shifting one’s weight backwards during a descent to maintain even weight distribution across both wheels. What he didn’t realize is that he was implementing this technique incorrectly. More specifically, he didn’t realize that he wasn’t moving nearly as far backwards as he could and should. From his perspective it seemed like he was moving backwards to about the limits of rearward movement for his body. This was far from being the case.

To understand how Wayne could be as mistaken as he was about this it will help to note that in riding on roads—which was the bulk of his riding experience—there is relatively little need for front-rear body movement. Consequently, a relatively small amount of rearwards movement felt like a lot. Moreover, although Wayne knew that it was important to maintain even weight distribution across the front and rear wheels, he was not used to maintaining this form of awareness and had been preoccupied by line choice. There is a distinctive ‘feel’ to a weight distribution that is too far forward in a descent, which notably involves pressure on the hands and wrists. Wayne had not yet learned to efficiently identify this and respond appropriately.

Thus, although Wayne was able to detect the front wheel instability and formulate a compensatory strategy, he failed to autonomously find a much more effective strategy. This stemmed from a poor representation of weight and balance and a poor awareness of his ability to adjust balance. He failed to properly relate the instability to a forward weight distribution and solve this by moving far enough rearwards. This is despite the fact that he knew the correct technique abstractly. A poor on-the-bike representation of balance contributed to a failure to properly interpret the abstract instructions.

We can interpret these points in terms of the concepts of causal control models, action evaluation systems, and problem discovery. Wayne experienced cues to poor control in his initial ride which prompted him to ride cautiously. But his ability to represent the causes of these problems was underdeveloped and so, while he found a solution that achieved the goal of riding the obstacle, he failed to find a more optimal (smoother, safer, speedier) solution. Once he had learned the superior technique his causal control model was altered and his capacity for action evaluation improved. He became aware of an expanded range of body movements and as he experimented with this range he gained new information about the interrelations between weight distribution, stability and handling. He could now interpret high pressure through the wrists as a sign of weight being too far forward. He could better interpret perceptual cues related to balance and perform bodily adjustments to modify weight distribution more appropriately. Putting this in more general terms, he had acquired a revised understanding of balance control on the bike which yielded a generalised improvement in his ability to solve riding problems.

The problem of translating between representational systems

The difficulties Wayne experienced involved a failure to properly translate between abstract and situated representational systems. As such, they help to reveal how these mappings are constructed. In the earliest stages of skill learning the individual must laboriously construct concrete interpretations of abstract action descriptions. This is hampered by two factors. Firstly, the individual lacks systematic representations of skill-specific phenomena at the level of concrete control of execution. Secondly, the individual lacks well-developed systematic mappings from abstract to concrete representations. In this case Wayne lacked a fully-developed systematic representation of the range of positions he could take on the bike and their relations to balance. Once he had learned to move his body backwards, and experienced the technique in an approximation of its correct form, he developed an awareness of balance and stability which he could relate to his abstract knowledge of the structure of the technique. He had thus developed a representation of the structure of the technique from the perspective of control which he could use for control. One way to describe this is that he had now acquired a relatively well-structured executable action concept for the technique. However, much more practice would be needed to consolidate this concept in relation to a well-developed causal control model for implementation.

Improvements in the structure of control

Wayne experienced a significant degree of uncertainty throughout the ride. His intentions, in particular the riding strategies he adopted, involved commitment that was always qualified and evaluated during performance. He maintained awareness of opportunities to abort actions and he did so on several occasions, such as the one described above where he initiated an ascent but stopped part way for fear of losing traction at a higher section of the ascent and falling over. He modified strategies both prior to and during execution to reduce problems, increase the chances of success, and, later, to exploit improved capabilities. His intentions were thus much more labile than Bratman's model recognises. This lability was based on an action evaluation system which could evaluate intentions against a complex ensemble of further criteria represented by the AES. Indeed, the learning process hinged on this.

Wayne's uncertainty was especially high in the initial stages of the ride. He was unsure of which trail sections he could and could not ride safely and he was unsure of whether his riding strategies would be effective in negotiating obstacles and avoiding crashes. His ability to evaluate his performance was also limited, as evident in his flawed diagnosis of the stability problems he was experiencing. Thus, his control ability was relatively poor in both the goal-based and problem-based senses. His ability to achieve the goals that he had was modest and not reliable enough to provide reasonable confidence. But he was also uncertain about his goals, and not able to form all of the right goals, because he lacked a good understanding of his riding problems.

Wayne’s control ability improved over the course of the ride in both the goal-based and problem-based senses. He became better able to achieve the goals that he had, and he became better able to solve the riding problems that he faced. This was based in part on improvements in his understanding of his riding problems, which included greater ability to evaluate action strategies and performance, and form appropriate goals. Improvements in evaluation ability, with enhanced ability to manage uncertainty, are critical to mountain biking, which routinely involves riding unfamiliar, challenging trails.

The technical improvements in particular involved a relatively deep form of problem discovery. As Wayne learned how to properly implement the behind-the-seat technique he was learning both technique-specific and more generalised representations. He was refining a technique-specific concept linked to a causal control model for the technique. These representations incorporated more generalised representations, such as of body position on the bike, balance state, terrain, grip, speed, and so on. These more generalised representations allowed improved representation of a larger set of riding problems. This enabled the formulation of an expanded range of riding strategies which were more effective. These representations also improved the ability to interrelate techniques and strategies by means of common features. Thus, when one strategy is succeeded by another, such as a descent followed by the negotiation of a corner, features of each can be related to each other and adjusted to provide good fit. The speed and line of the descent can be shaped to set up a good entry to the corner and an efficient cornering line, for instance. Action-specific representations are thus integrated into a global state-space of control which represents the situation, performance state, and action possibilities. This state-space can be more or less well integrated, and the more integrated it is, the better able the individual will be to produce coherent complex action.

Social and cultural influences

Wayne's prior mountain bike riding experience was not insignificant, involving about a year of riding one or more times a week on a mixed set of singletrack and fire trails. This provided him with enough skill to engage in the problem-solving described above. But his learning was based largely on solo discovery, adapting skills from road riding and casual BMX-style riding in childhood. The kind of flaws in we’ve described in Wayne's technique, and the limitations in his problem solving ability, are common in individuals who attempt to teach themselves complex skills. Rich engagement with teachers and a skill community can scaffold skill development, allowing the individual to develop solutions and an expanded sense of what is possible (Aggerholm & Hølbjerre-Larsen, 2017 ).

One of the most important ways that a skill community can guide individual learning is by furnishing skill norms. As we described above, during the initial ride Wayne was aware that he was experiencing control problems but thought his riding was reasonable given his experience and the context. This is an example of a general phenomenon: learners tend to possess impoverished norms for the skill domain, and this hampers their ability to evaluate and improve their performance. In this case, Wayne did not have a good grasp of what kind of performance he should be able to achieve, given his base skill level. In fact, at that point he could relatively easily achieve a much higher level of performance. In general, it is difficult for learners to know what performance standards could be expected for their level and experience. This in turn limits their ability to diagnose problems in their methods.

Skill communities often have highly developed performance norms which orient individuals. In mountain biking, speed is a highly valued performance norm. Wayne was not especially concerned with speed at this point, and Kath found it amusing that Wayne’s initial reactions to the new bike were primarily focused on its improved low speed handling. Speed, though, is only one element of a set of norms for assessing quality of performance. Smoothness and efficiency are also valued. These attributes are integrated into an umbrella concept of flow , 17 which serves as a goal for riders and trail builders (Bicknell, 2016 ). Other norms concern safety and risk management. In this respect, a feature of the risk norms for mountain biking that is striking to Wayne is the acceptance that there is a fairly high level of risk that is ineliminable and must simply be accepted. Crashes and injuries are simply part of mountain biking. Thus, through social interaction learners acquire concepts for normatively characterising performance which help them to evaluate their own performances and set goals. Group riding, both social and competition, exposes the individual directly to the performance abilities and conventions of others, providing further information for self-evaluation and goal setting.

The ability to use social information is itself skill-dependent. For example, in riding with Kath, Wayne was aware that she was a more capable rider. However, from this he gained little information that was useful for his own riding. Because Kath was so much more advanced, her performance ability did not serve as a useful benchmark for him and he was unable to identify the structure of her methods in a way that would allow him to copy them. Individuals with more advanced skill are often much better at identifying the structure of methods used by others (Bicknell, 2010 , 2011 ). This can allow them to copy and adapt them for themselves, or identify problems which should be avoided or corrected. Teaching can scaffold learners through this limitation. The learner's limited action evaluation system is supplemented by the much more sophisticated action evaluation system of the teacher. In such situations, the causal control model of the more experienced rider can help to develop the causal control model of the learner.

The mountain bike community routinely scaffolds the experiences of the individual in other ways, too. The difficulty and type of experience that riders have is shaped by trail and bike design. Trail grading gives riders information about the difficulty of particular trails which allows riders to decide in advance whether a trail will be within their ability. Grading systems like this also provide individuals with benchmarks for assessing performance. If an individual finds an intermediate trail difficult they are able to locate themselves within the spectrum of abilities within the community. This can in turn guide goal setting for learning and performance. An individual having difficulties at a particular level can seek to identify technical limitations that are holding them back, and work on those, for example. At a more immediate level, signage on trails alerts riders to the required skill level needed to ride specific, upcoming obstacles. Double- or triple-downward arrows before a particularly steep section of trail that isn’t clearly visible on approach serve this function on some trails, alerting the rider to challenging terrain they typically cannot see until they are already riding it. In other trail communities a sign saying ‘warning’ may be used. An alternative convention is signs pointing to A-, B- or C-lines, indicating the technical difficulty of upcoming sections of trail and encouraging riders to make a decision about which line to take (Fig. ​ (Fig.1 1 ).

Trail sign, Ourimbah cross-country mountain bike track. This sign prompts riders to prepare for the jump line on the right, or to veer left to avoid it. Importantly, it is placed on an unremarkable section of track, at eye height, to catch the rider’s attention with enough time to make a decision about how to approach the upcoming section of trail, without having to stop riding in order to do so. Photo by Kath Bicknell.

Indeed, the mountain bike community has a rich set of community-specific caretaking practices which manage awareness, decision making and problem solving. Additional practices include:

• ‘Pre-riding’ (looking at a trail slowly, with an explorative mindset, before riding it at speed);
• ‘Sessioning an obstacle’ (stopping to look at and practice an obstacle before riding it at speed);
• ‘Riding and scoping’ (eg. riding around a jump while looking at it sideways during the first run of a trail and deciding whether it is safe to attempt on the next lap of that section of trail);
• Building up ‘reserve techniques’ (which can help to regain control if the speed, shape or pitch of the bike mid-obstacle indicates a crash is imminent).

Communication methods are also used to manage problem solving. For example, the question, ‘Is it rollable?’ is one that a skilled rider may ask another before attempting a trail for the first time. If a trail is ‘rollable’ it means there are no gaps that need to be jumped—as long as balance and momentum are maintained everything is ‘rideable’. The trail may well be frighteningly steep and require a high skillset in a number of other areas, but it will be manageable for a rider with a particular set of abilities.

To sum up, skill communities scaffold learning and performance in many ways, allowing higher levels of performance to be achieved and a better, safer quality of experience. The practices which achieve this do so to a significant degree by enhancing the decision-making and problem solving of the individual. Indeed, the engaging nature of sports like mountain biking rests on achieving a complex balance between approachability, challenge, and safety. Mountain biking has been very successful in this respect, and is a fast-growing sport (eg. Latz, 2020 ). This point is worth emphasising because it helps make the case that the phenomena we’ve been describing are not marginal or unimportant—they’re integral to many skills and can be crucial to their success.

The ability to formulate action strategies and control their execution is a central issue for understanding action and skill, yet there is very little work on it. Here we found that even an individual with relatively modest skill experience can be capable of fairly complex, fast-paced construction and control of action strategies. Our results need to be validated by further ecological and laboratory-based investigation but we are confident that the core phenomena we've described are real, and that the use of strategies and problem solving is very common in skilled action. The kind of problem solving we found, together with its flaws, is likely to be fairly typical for individuals in relatively early stages of skill learning. But in skills which require significant levels of flexibility—such as mountain biking and climbing—problem solving is also likely to be central to the most advanced levels of skill. It is consequently of high importance that we develop a better understanding of the mechanisms which support these abilities and the way they develop during skill learning. We've argued that causal representation, performance awareness, metacognitive awareness and action evaluation all play important roles and operate together in a complex, integrated action control system. Our account of these mechanisms goes beyond previous work and adds to the Mesh theory of skill a more detailed model of action control.

Acknowledgements

We thank John Sutton, John Michael, Josh Shepherd, Chiara Brozzo, Cynthia Siew, Gaye Camm, Tom McClelland, Doris McIlwain, and the generosity of colleagues and students in the Cognitive Ecologies and Microethnography Labs at Macquarie University, Sydney, Australia. An earlier version of this paper was presented as a keynote at the ‘Actions: The Mental and the Bodily’ conference at the University of Warwick, UK, and in a shorter form at the ‘Cognitive Futures in the Arts and Humanities: Paradigms of Understanding—Sharing Cognitive Worlds’ conference in Mainz, Germany. We thank the audiences for their questions, provocations and enthusiasm for this work.

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research was funded by European Research Council starting Grant 757698, awarded under the Horizon 2020 program for research and innovation, and the Australian Research Council Discovery Project grants DP130100756 ‘Mindful Bodies in Action: a philosophical study of skilled movement’, awarded to Doris McIlwain and John Sutton (2013–2015) and DP180100107 ‘The Cognitive Ecologies of Collaborative Embodied Skills’, awarded to John Sutton (2018–2020).

Declarations

The authors report no conflict of interest.

1 See Preston ( 2013 ) for a related discussion of Bratman’s account.

2 See Chisholm ( 2008 ) for an in-depth exploration of the relationship between bodily characteristics, technique and environment in climbing.

3 This video shows six different strategies used to solve a particular problem at the 2021 World Championships: https://www.instagram.com/p/CT9pAfTpMmm/ .

4 A video of squirrels engaging in this kind of problem solving can be seen here: https://theconversation.com/we-used-peanuts-and-a-climbing-wall-to-learn-how-squirrels-judge-their-leaps-so-successfully-and-how-their-skills-could-inspire-more-nimble-robots-165524 .

5 Nanay (2020) and Fridland ( 2021 ) argue that action control is based on imagistic representation, drawing on evidence that practice using mental imagery can be highly beneficial. Causal control models should be differentiated from mental images. Imagistic representations do not represent causal relations per se, although causal representation can be incorporated into imagistic representations. Nor is causal representation necessarily imagistic. It is the representation of causal relations specifically that plays a foundational enabling role in the intentional control of action.

6 https://www.youtube.com/watch?v=IqsNJv2VROs .

7 Not all parameter relations need be represented as causally related since the motor system can sometimes ‘fill-in’ some parameters when given others as explicit goals. Indeed, because of the limits of attention capacity learners must often rely on some degree of ‘filling-in’ because they can’t continuously attend to the full structure of the technique for a novel action while performing it. For instance, when learning a new chord the learner might initially attend primarily to landing finger locations then switch to correcting overall hand posture, while largely ignoring timing. But these kinds of cases illustrate limitations of automated ‘filling-in’ because the filled-in parameters will often not correspond to good technique. Focusing on finger locations while neglecting hand posture when learning a new chord is likely to result in poor posture. Once an individual has developed a high level of skill automated filling-in will tend to better correspond to good technique, but this is because the techniques have already been intentionally learned. We thank a referee for prompting this clarification.

8 We’re using the term ‘translation’ in a broad sense which involves the construction of alternative representations of the same, similar or related content (see Christensen, 2020). This includes translation between alternative expressions within a representational system such as a language. An alternative approach would be to restrict ‘translation’ to content re-representations and interpretations across representational systems with distinct formats and use an alternative concept such as ‘mapping’ for such content relating processes within a representational system, such as between levels of abstraction (we thank a reviewer for this suggestion). This raises a complex set of issues. We agree that there may be reasons to adopt a more restricted concept of translation but we think it is unclear how this should be done. In general, we think that philosophers and cognitive scientists have been overly focused on the idea that mental representational systems are discrete, static systems defined by a distinct, unchanging format Evidence from cognitive neuroscience suggests that the brain has an extremely flexible ability to construct representational systems, that representational systems with different formats can overlap (auditory, visual, and haptic representational systems use shared spatial representations), that multiple formats can be encompassed in an integrated representational system (working memory is an integrated workspace which includes auditory, visual and other subcomponents), and so on (see Author 2020 for an extended discussion). Consequently, we doubt that it will be possible to draw a clear distinction between within-system and between-system content-relating processes. More specifically, it’s likely that content-relating and construction processes that operate within cognitive representational systems also play a role in constructing new representational systems and building relations across existing systems when this becomes functionally beneficial. These points have strong implications for understanding skill learning, which, we suggest, involves the construction of multiple interlinked domain-specific representational systems. In future work we’ll develop the idea that the processes we described here as translating between levels of abstraction play a key role in the formation of new representational systems for skill control.

9 Recent philosophical discussion has been preoccupied with a narrower 'interface problem' formulated by Butterfill and Sinigaglia (2014). The idea is that there is a special difficulty understanding how cognitive and motor representations can interact which stems from the supposed impossibility of translation between their formats. However, Christensen (2020) argues that the theoretical rationale for rejecting translation is unsound and that there is extensive empirical evidence that the brain translates across many representational formats, including motor and cognitive.

10 We won't engage in a comprehensive analysis of the DPM model here. Brozzo (2021) criticises the distinction between present- and future-directed intentions. Christensen (2020) gives an alternative account of motor representations and their interface with higher level intentions.

11 Although it should be noted that Shepherd's account includes a discussion of the role of domain norms in action evaluation which brings it closer to the problem-based conception.

12 This example reflects common advice to, and complaints about, inexperienced musicians. The following article provides a fairly typical illustration: https://www.premierguitar.com/articles/print/28953-last-call-space-is-music-too .

13 Our use of the term ‘system’ here may suggest that we have in mind a Fodorian module. However, we are adopting a different, non-Fodorian conception of modules and systems which is based in a biological framework. By ‘system’ we mean a network of structures and processes that show a degree of somewhat specific functional integration and perform one or more somewhat specific functional roles (in this case, action evaluation). A cognitive system in this sense can integrate with, and overlap with, other cognitive systems. The AES will be a ‘system of systems’ which encompasses multiple lower and higher level evaluative systems, for instance including both reward and efficiency evaluation systems.

14 In this respect our account can be contrasted with that of the predictive processing framework, which claims that behaviour is governed by a single dominant goal, namely predictive error minimization. Our account thus avoids the ‘dark room’ problem facing the predictive coding approach, which is that predictive error minimization can be achieved by placing oneself in a highly predictable environment (Sun & Firestone, 2020). We thank a referee for this point.

15 Drawing on Kath’s experience of teaching this exercise to beginner riders at skills clinics, many riders think they are almost about the graze the tyre when they are still approximately 20 cm away from reaching it. This was also true of Wayne. Kath communicated this to him verbally during the exercise, showed the gap with a hand gesture, and demonstrated the correct vs incorrect technique. In response, Wayne exaggerated his attempt at the task and was able to graze the rear tyre. Both riders were delighted!

16 Also called a ‘front wheel washout’. There are some differences in the way that ‘washout’ is conceptualised in the mountain bike community. The version of the concept Wayne was using treats it as any case in which the front wheel loses traction and slides out from underneath the rider, causing a crash.

17 This folk concept of flow should be differentiated from the concept used in psychology (Csikszentmihalyi 1990 ).

S.I.: Minds in Skilled Performance.

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Contributor Information

Wayne Christensen, Email: [email protected] .

Kath Bicknell, Email: moc.llenkcibhtak@liame .

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