Decision Making and Problem Solving Courses: Critical Thinking and Effective Decisions
Decision making and problem solving training to develop critical thinking, structured decision analysis and ability to solve complex problems. Learn decision frameworks, cognitive bias management, decision trees and strategic problem solving techniques.
What you'll learn in Decision Making and Problem Solving courses
Knowing how to decide well is critical skill for leaders: wrong decisions are costly (time, money, opportunities), while effective decisions accelerate results. McKinsey shows that organizations with structured decision-making have 20% superior performance. Our paths cover critical thinking, decision frameworks, risk-benefit analysis, cognitive bias management, creative problem solving, decision trees, Eisenhower matrix.
You'll learn to apply structured decision frameworks: decision matrix (weighted pros/cons), decision trees (branched scenarios with probabilities), quantitative cost-benefit analysis, recognize and mitigate cognitive biases that distort decisions: confirmation bias (seeking confirmations), anchoring (anchoring to first data), sunk cost fallacy (irrecoverable costs), availability bias (recent events overestimated), use critical thinking: analyze assumptions, evaluate evidence, consider alternative perspectives, separate facts from opinions, apply problem solving techniques: 5 Whys (root cause analysis), fishbone diagram (Ishikawa), design thinking, TRIZ (inventive problem solving), manage decisions under uncertainty conditions: scenario analysis, decision trees with probabilities, real options, precautionary principle. Focus on decision fatigue: limit non-essential decisions, create routines, decide when energy is high.
Who this training path is for
- Managers and leaders: frequent strategic decisions with significant impact on team and business
- Entrepreneurs and CEOs: critical choices under uncertainty and limited resources
- Project managers: rapid decisions on priorities, resources, risks during complex projects
- Consultants and analysts: supporting clients in data-driven strategic decisions
- Growing professionals: developing critical thinking for managerial career
Benefits of Decision Making and Problem Solving training
Faster and more accurate decisions
Structured frameworks reduce decision time by 40% while maintaining or improving decision quality through systematic analysis.
Reduced costly errors
Awareness of cognitive biases and use of critical thinking prevent wrong instinctive decisions that cost time and resources.
Effective strategic problem solving
Structured techniques (5 Whys, fishbone, design thinking) identify deep causes and generate lasting innovative solutions.
How to choose the most suitable format for your team
Each format is designed to adapt to different decision-making skill development needs and business contexts.
In-person course → ideal for:
- C-level and senior managers facing complex strategic decisions with real business cases
- Problem solving workshops with intensive simulations and group debriefing on difficult cases
- Team decision making: shared decision techniques and constructive disagreement management
- Past decision analysis (post-mortem) to identify error patterns and improve process
Online course → ideal for:
- Critical thinking and decision framework fundamentals (matrix, trees) flexibly accessible
- Geographically dispersed managers needing scalable training on cognitive biases
- Microlearning specific techniques: 5 Whys, fishbone, Eisenhower matrix, SWOT analysis
- Critical thinking certifications with self-assessment exercises and automated feedback
Blended course → ideal for:
- Decision framework theory online + in-person workshop on real corporate strategic decisions
- Cognitive biases and critical thinking e-learning + live group difficult decision analysis sessions
- Problem solving tools platform + 1-to-1 coaching on specific decision challenges
- Certification path: on-demand content + assessment centre decisions under pressure
Frequently asked questions about Decision Making and Problem Solving
What are the main cognitive biases that influence decisions?
Cognitive biases are unconscious mental shortcuts that distort judgment. The main ones include: Confirmation bias (the tendency to seek information that confirms preexisting beliefs while ignoring contrary evidence), Anchoring (anchoring to the first number or information received, such as the first offer in a negotiation), Sunk cost fallacy (continuing an investment because of costs already incurred instead of rationally evaluating the future), Availability bias (overestimating the probability of recent or vivid events, like a plane crash just seen on TV), Overconfidence (overestimating one's own abilities and knowledge), and Groupthink (conforming to group decisions while avoiding dissent). To mitigate these biases, it's essential to develop awareness, use a devil's advocate, rely on objective data, and adopt pre-decision checklists.
How does the decision matrix work and when to use it?
The decision matrix is a tool for comparing multiple options based on weighted criteria. The process involves five steps: first, list the available options (for example suppliers A, B, C); second, define the relevant evaluation criteria (cost, quality, delivery times, support); third, assign weights to criteria based on their relative importance (totaling 100%); fourth, rate each option on each criterion using a scale (e.g., 1-10); fifth, calculate the weighted score by multiplying the rating by the weight of each criterion and summing the results. The option with the highest score is the one to choose. This tool is particularly useful for decisions with multiple criteria, for teams with different opinions (as it objectifies the process), and for non-intuitive choices. The limitations are that it requires time and that criteria and weights remain partially subjective.
What is a decision tree and how to build it?
A decision tree is a graphical representation that maps sequential decisions and possible scenarios with their associated probabilities and outcomes. The structure includes: decision nodes (represented by squares) indicating points where you choose between alternative options, probability nodes (circles) representing uncertain events with associated probabilities, branches connecting options or scenarios, and leaves showing final outcomes with their expected value. For calculation, you start from the leaves and work backward calculating the expected value using the formula Σ(probability × payoff). This tool is particularly useful for decisions with quantifiable uncertainty (e.g., product launch with 60% success and 40% failure), interdependent decision sequences, and complex cost-benefit analyses. Application examples: investment decisions, R&D projects, pricing strategies.
Which problem solving techniques are most effective?
The most effective techniques depend on the type of problem to address. The 5 Whys consist of asking "why?" five consecutive times to identify the root cause (particularly useful for operational problems). The Fishbone diagram (Ishikawa) is used to map cause categories (people, processes, materials, environment) for complex problems. Design Thinking follows a structured path: user empathy → problem definition → solution ideation → prototyping → testing (ideal for user-centered innovation). TRIZ applies 40 inventive principles to solve problems (such as segmentation, asymmetry, inversion). Six Thinking Hats by De Bono allow exploring the problem from six different perspectives (facts, emotions, benefits, risks, creativity, process). The key to success is defining the problem well before seeking solutions, formulating a clear "problem statement".
How to make decisions under uncertainty?
Uncertainty requires probabilistic and flexible approaches. The main strategies include: scenario analysis, which consists of building 3-4 plausible future scenarios (optimistic, pessimistic, and base) to identify a robust strategy valid in multiple contexts; decision trees with probabilities, where you assign probabilities to each scenario, calculate the expected value, and choose the option with the best EV; real options, which maintain flexibility (such as gradual investments instead of all at once, or exit options); the precautionary principle, which suggests acting cautiously when potential damage is irreversible, even if probability is low; small tests like pilots, MVPs, or A/B tests to reduce uncertainty before making major decisions. The key is avoiding analysis paralysis: make decisions with available information and adapt along the way.
Can decision making be improved or is it innate talent?
Decision making is a completely improvable skill through deliberate practice. Research demonstrates that decision quality improves significantly through the use of structured frameworks (which reduce improvisation), bias awareness (actively recognizing and mitigating them), decision post-mortems (analyzing what worked and what didn't to create a learning loop), perspective diversity (using devil's advocate and heterogeneous teams), and incremental decisions (preferring small reversible decisions to large irreversible choices). Innate talent helps (especially intuition) but method beats intuition in complex contexts. As Gary Klein highlighted, experts combine pattern recognition with rational deliberation. The key to success is being able to combine rigorous analysis with expert intuition, constantly reflecting on past decisions.