meta-decision-analysis
用決策矩陣和決策樹，把多個選項量化比較再下決定
中文翻譯
---
name: "meta-decision-analysis"
description: "用決策矩陣、決策樹、期望值和多準則決策分析（MCDA）做結構化的決策分析。使用者面對有多個選項和多項準則的複雜決策、需要客觀比較替代方案、量化風險與報酬，或要帶領團體做決定時使用，就算他們說的是「我們該選哪個」、「幫我決定」、「這些選項怎麼比較」或「預期結果是什麼」也一樣。"
metadata:
category: "WP-22 跨學科"
tags: ["meta-thinking", "decision-analysis", "decision-matrix"]
---
# 決策分析
## 框架
```
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.
```
### 決策矩陣（加權評分）
1. **列出替代方案**（3-6 個選項，包含「什麼都不做」）
2. **定義準則**（4-8 個重要的因素）
3. **幫準則加權**（加起來必須是 100%）
4. **每個選項在每項準則上評分**（1-5 或 1-10）
5. **計算加權總分** = Σ(分數 × 權重)
6. **敏感度檢查**：如果調整權重最高的那項準則，贏家會變嗎？
### 決策樹（不確定情況下的連續決策）
適用於有不確定性、而且要分步驟做的決策：
1. 畫出決策節點（方形）和機會節點（圓形）
2. 為每個機會結果指定機率（加起來必須是 1.0）
3. 為終端節點指定報酬
4. 計算期望值 = Σ(機率 × 報酬)
5. 選期望值最高的分支（或風險調整後結果最好的）
### 多準則決策分析（MCDA）
適用於利害關係人優先順序互相衝突的複雜決策：
1. 每個利害關係人獨立定義自己的準則和權重
2. 彙整成一個合併的加權矩陣
3. 找出利害關係人哪裡一致（容易的決定）、哪裡不一致（需要協商）
## 輸出格式
```markdown
# Decision Analysis: {Decision}
## Alternatives
1. {Option A}
2. {Option B}
3. {Option C}
## Decision Matrix
| Criterion | Weight | Option A | Option B | Option C |
|-----------|--------|----------|----------|----------|
| {criterion 1} | {X%} | {1-5} | {1-5} | {1-5} |
| **Weighted Total** | 100% | **{total}** | **{total}** | **{total}** |
## Sensitivity Analysis
- If {criterion} weight changes from X% to Y%, winner changes from {A} to {B}
## Recommendation
{Winner with rationale and key trade-offs acknowledged}
```
## 注意事項
- **「什麼都不做」永遠是個選項**：把它放進來當基準線。有時候最好的決定就是等。
- **分數是主觀的**：一個人給的「4」不等於另一個人給的「4」。評分之前先定義每個分數代表什麼，把標準校準好。
- **期望值忽略風險偏好**：$50（確定）的期望值和「50% 機率拿 $0、50% 機率拿 $100」的期望值，用 EV 算是一樣的，但感覺差很多。高風險的決策要用風險調整後的指標。
- **分析癱瘓**：決策分析應該是加快決策，不是拖慢。給分析設一個時間限制。
## 參考資料
- 決策樹軟體工具請看 `references/decision-tools.md`英文原文
---
name: "meta-decision-analysis"
description: "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'."
metadata:
category: "WP-22 跨學科"
tags: ["meta-thinking", "decision-analysis", "decision-matrix"]
---
# Decision Analysis
## Framework
```
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)
1. **List alternatives** (3-6 options including "do nothing")
2. **Define criteria** (4-8 factors that matter)
3. **Weight criteria** (must sum to 100%)
4. **Score each option** per criterion (1-5 or 1-10)
5. **Calculate weighted total** = Σ(score × weight)
6. **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:
1. Map decision nodes (squares) and chance nodes (circles)
2. Assign probabilities to chance outcomes (must sum to 1.0)
3. Assign payoffs to terminal nodes
4. Calculate Expected Value = Σ(probability × payoff)
5. Choose the branch with highest EV (or best risk-adjusted outcome)
### Multi-Criteria Decision Analysis (MCDA)
For complex decisions with competing stakeholder priorities:
1. Each stakeholder defines their criteria and weights independently
2. Aggregate into a combined weighted matrix
3. Identify where stakeholders agree (easy decisions) and disagree (requires negotiation)
## Output Format
```markdown
# Decision Analysis: {Decision}
## Alternatives
1. {Option A}
2. {Option B}
3. {Option C}
## Decision Matrix
| Criterion | Weight | Option A | Option B | Option C |
|-----------|--------|----------|----------|----------|
| {criterion 1} | {X%} | {1-5} | {1-5} | {1-5} |
| **Weighted Total** | 100% | **{total}** | **{total}** | **{total}** |
## Sensitivity Analysis
- If {criterion} weight changes from X% to Y%, winner changes from {A} to {B}
## Recommendation
{Winner with rationale and key trade-offs acknowledged}
```
## Gotchas
- **"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
- For decision tree software tools, see `references/decision-tools.md`