Category 1: Structured Decision-Making Frameworks

1. Decision Trees
   • Description: A visual tool that maps sequential decisions and possible outcomes.
   • When/How to Use: Ideal when you have discrete choices and clear branches of outcomes—such as evaluating different feature development paths with anticipated customer responses.
2. Cost-Benefit Analysis
   • Description: A method to quantify and compare the expected costs and benefits of various options.
   • When/How to Use: Use when you have data or estimates to weigh trade-offs—for instance, deciding whether the potential revenue from a new feature justifies its development cost.
3. Multi-Criteria Decision Analysis (MCDA)
   • Description: A systematic approach that scores alternatives based on several weighted criteria.
   • When/How to Use: Effective when decisions involve multiple factors (like cost, time, and customer impact), such as prioritizing a product roadmap with competing feature requests.
4. Decision Matrix
   • Description: A table for comparing options against predefined criteria.
   • When/How to Use: Useful for evaluating multiple vendor solutions or product features where each option can be scored against factors like feasibility and impact.
5. Monte Carlo Simulation
   • Description: A probabilistic model that simulates a range of outcomes based on variability in key inputs.
   • When/How to Use: Best applied when there’s high uncertainty—like forecasting potential revenue impacts under different market conditions.
6. Pareto Analysis (80/20 Principle)
   • Description: The observation that 80% of results often come from 20% of causes.
   • When/How to Use: Use this to identify the critical few factors driving most outcomes, for example, focusing on key features that deliver the bulk of customer value.

Category 2: Foundational Mental Models & Decision Theories

1. Systems Thinking
   • Description: An approach that considers a decision as part of an interrelated whole.
   • When/How to Use: Particularly useful when a product decision impacts multiple areas (e.g., customer experience, technical infrastructure, and operations), helping identify potential ripple effects.
2. Second-Order Thinking
   • Description: A model that emphasizes anticipating the indirect, long-term consequences of decisions.
   • When/How to Use: Use when evaluating changes like a new pricing model, where immediate benefits might be offset by long-term shifts in customer behavior.
3. Inversion
   • Description: A method that involves considering the opposite of your planned approach to reveal potential pitfalls.
   • When/How to Use: Effective for challenging assumptions—ask what might go wrong if a product strategy is implemented, thereby uncovering hidden risks.
4. Probabilistic and Bayesian Reasoning
   • Description: Techniques for updating decisions as new evidence becomes available, weighing outcomes by their likelihood.
   • When/How to Use: Ideal when market conditions or user behavior data are in flux, allowing for dynamic recalibration of product strategies.
5. Occam's Razor
   • Description: The principle that the simplest explanation is usually the best.
   • When/How to Use: Helps avoid overcomplicating product hypotheses—choose the simplest solution that effectively addresses a user need.
6. Fermi Estimation
   • Description: A method for making rough estimates when data is scarce.
   • When/How to Use: Useful in early-stage product decisions where you need a ballpark figure (e.g., estimating market size for a new feature).
7. Expected Utility & Prospect Theories
   • Description: Theories that assess choices based on potential outcomes and the decision-maker's risk perception.
   • When/How to Use: Apply these when decisions involve risk and uncertainty, such as selecting between alternative market entry strategies.
8. Cognitive Bias Awareness
   • Description: Recognizing common biases (e.g., confirmation bias, anchoring) that affect decision-making.
   • When/How to Use: Always relevant—ensure that product decisions are based on objective data rather than skewed by biases.

Category 3: Decision Context & Complexity Frameworks

1. CYNEFIN Framework
   • Description: Categorizes decision contexts into domains—Obvious, Complicated, Complex, Chaotic, and Disorder—to guide the appropriate approach.
   • When/How to Use: Internally assess the nature of a decision. For example, if a product issue is complex or chaotic, consider adaptive, emergent methods such as iterative testing or scenario planning.