Decision Analysis 決策分析

Released已發布

methodology methodology

Apply structured decision analysis using decision matrices, decision trees, expected value, and multi-criteria decision analysis (MCDA). Use this skill when the user faces a complex decision with multiple options and criteria, needs to compare alternatives objectively, quantify risk vs reward, or facilitate group decisions — even if they say 'which option should we choose', 'help me decide', 'how do we compare these options', or 'what's the expected outcome'.

思維框架技能：Decision Analysis 分析與應用。

View on GitHub在 GitHub 查看

Methodology 方法論

IRON LAW: Make Criteria and Weights Explicit BEFORE Evaluating Options

Choosing criteria after seeing the options lets bias sneak in — you
unconsciously weight criteria that favor your preferred option.
Define criteria, assign weights, THEN score options.

Decision Matrix (Weighted Scoring)

List alternatives (3-6 options including "do nothing")
Define criteria (4-8 factors that matter)
Weight criteria (must sum to 100%)
Score each option per criterion (1-5 or 1-10)
Calculate weighted total = Σ(score × weight)
Sensitivity check: Does the winner change if you adjust the top-weighted criterion?

Decision Tree (Sequential Decisions Under Uncertainty)

For decisions with uncertainty and sequential steps:

Map decision nodes (squares) and chance nodes (circles)
Assign probabilities to chance outcomes (must sum to 1.0)
Assign payoffs to terminal nodes
Calculate Expected Value = Σ(probability × payoff)
Choose the branch with highest EV (or best risk-adjusted outcome)

Multi-Criteria Decision Analysis (MCDA)

For complex decisions with competing stakeholder priorities:

Each stakeholder defines their criteria and weights independently
Aggregate into a combined weighted matrix
Identify where stakeholders agree (easy decisions) and disagree (requires negotiation)

Output Format輸出格式

# Decision Analysis: {Decision}

Gotchas注意事項

"Do nothing" is always an option: Include it as a baseline. Sometimes the best decision is to wait.
Scores are subjective: A score of "4" from one person ≠ "4" from another. Calibrate by defining what each score means before scoring.
Expected value ignores risk preference: EV of $50 (certain) vs EV of $50 (50% chance of $0, 50% chance of $100) are equal by EV but feel very different. For high-stakes decisions, use risk-adjusted metrics.
Analysis paralysis: Decision analysis should accelerate decisions, not delay them. Set a time limit for the analysis.

References參考資料

For decision tree software tools, see references/decision-tools.md

Tags標籤

meta-thinkingdecision-analysisdecision-matrix