decision-makinglisted

# Decision Making A decision is a commitment to one of several possible actions in the face of uncertainty about their consequences. Good decision-making is not the same as getting good outcomes — luck intervenes — but consistently good decisions produce better outcomes over time. This skill covers the structured methods decision scientists use to bring rigor to choices that matter. **Agent affinity:** kahneman-ct (System 1 / System 2 allocation), tversky (expected value and biases), paul (integration with elements of reasoning) **Concept IDs:** crit-decision-frameworks, crit-calibrated-confidence, crit-intellectual-humility ## The Decision Toolbox at a Glance | # | Method | Purpose | When to use | |---|---|---|---| | 1 | Expected value calculation | Weigh probabilities and payoffs | Repeatable decisions with quantifiable outcomes | | 2 | Decision trees | Map sequential choices and chance nodes | Multi-stage decisions with contingencies | | 3 | Multi-criteria decision analysis (MCDA) | Weigh multiple incommensurable criteria | Choices involving trade-offs across dimensions | | 4 | Pros and cons with weights | Simple MCDA for everyday decisions | Personal choices, not enough data for formal analysis | | 5 | Pre-mortem | Imagine failure to surface risks | Before committing to a major plan | | 6 | Reversibility check | Decide how much deliberation is needed | Every decision | | 7 | Two-way door vs. one-way door | Distinguish easily-undoable from locked-in | Speed decisions