decision-matrix

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Decision Matrix

决策矩阵（Decision Matrix）

What Is It?

什么是决策矩阵？

A decision matrix is a structured tool for comparing multiple alternatives against weighted criteria to make transparent, defensible choices. It forces explicit trade-off analysis by scoring each option on each criterion, making subjective factors visible and comparable.

Quick example:

Option	Cost (30%)	Speed (25%)	Quality (45%)	Weighted Score
Option A	8 (2.4)	6 (1.5)	9 (4.05)	7.95 ← Winner
Option B	6 (1.8)	9 (2.25)	7 (3.15)	7.20
Option C	9 (2.7)	4 (1.0)	6 (2.7)	6.40

The numbers in parentheses show criterion score × weight. Option A wins despite not being fastest or cheapest because quality matters most (45% weight).

决策矩阵（Decision Matrix）是一种结构化工具，用于基于加权标准对比多个备选方案，从而做出透明、具备可辩护性的选择。它通过对每个选项在各标准上的打分，强制进行明确的权衡分析，让主观因素变得可见且可对比。

快速示例：

选项	成本（30%）	速度（25%）	质量（45%）	加权得分
选项A	8 (2.4)	6 (1.5)	9 (4.05)	7.95 ← 胜出者
选项B	6 (1.8)	9 (2.25)	7 (3.15)	7.20
选项C	9 (2.7)	4 (1.0)	6 (2.7)	6.40

括号中的数字为标准得分×权重。尽管选项A不是最快或最便宜的，但由于质量的权重最高（45%），因此它成为胜出者。

Workflow

工作流程

Copy this checklist and track your progress:

Decision Matrix Progress:
- [ ] Step 1: Frame the decision and list alternatives
- [ ] Step 2: Identify and weight criteria
- [ ] Step 3: Score each alternative on each criterion
- [ ] Step 4: Calculate weighted scores and analyze results
- [ ] Step 5: Validate quality and deliver recommendation

Step 1: Frame the decision and list alternatives

Ask user for decision context (what are we choosing and why), list of alternatives (specific named options, not generic categories), constraints or dealbreakers (must-have requirements), and stakeholders (who needs to agree). Understanding must-haves helps filter options before scoring. See Framing Questions for clarification prompts.

Step 2: Identify and weight criteria

Collaborate with user to identify criteria (what factors matter for this decision), determine weights (which criteria matter most, as percentages summing to 100%), and validate coverage (do criteria capture all important trade-offs). If user is unsure about weighting → Use resources/template.md for weighting techniques. See Criterion Types for common patterns.

Step 3: Score each alternative on each criterion

For each option, score on each criterion using consistent scale (typically 1-10 where 10 = best). Ask user for scores or research objective data (cost, speed metrics) where available. Document assumptions and data sources. For complex scoring → See resources/methodology.md for calibration techniques.

Step 4: Calculate weighted scores and analyze results

Calculate weighted score for each option (sum of criterion score × weight). Rank options by total score. Identify close calls (options within 5% of each other). Check for sensitivity (would changing one weight flip the decision). See Sensitivity Analysis for interpretation guidance.

Step 5: Validate quality and deliver recommendation

Self-assess using resources/evaluators/rubric_decision_matrix.json (minimum score ≥ 3.5). Present decision-matrix.md file with clear recommendation, highlight key trade-offs revealed by analysis, note sensitivity to assumptions, and suggest next steps (gather more data on close calls, validate with stakeholders).

复制以下检查清单并跟踪进度：

Decision Matrix 进度：
- [ ] 步骤1：明确决策框架并列出备选方案
- [ ] 步骤2：确定标准并分配权重
- [ ] 步骤3：为每个备选方案在各标准上打分
- [ ] 步骤4：计算加权得分并分析结果
- [ ] 步骤5：验证质量并交付建议

步骤1：明确决策框架并列出备选方案

向用户确认决策背景（我们要选什么，为什么选）、备选方案列表（具体的命名选项，而非通用类别）、约束条件或否决项（必须满足的要求），以及利益相关方（需要达成共识的人员）。了解必须满足的要求有助于在打分前筛选选项。如需澄清问题，请参阅【框架问题】部分。

步骤2：确定标准并分配权重

与用户协作确定决策标准（哪些因素对该决策重要）、分配权重（哪些标准最重要，权重以百分比表示，总和为100%），并验证标准的全面性（是否覆盖了所有重要的权衡因素）。如果用户不确定如何分配权重，请使用resources/template.md中的权重分配技巧。常见标准类型请参阅【标准类型】部分。

步骤3：为每个备选方案在各标准上打分

针对每个选项，使用统一的评分标准（通常为1-10分，10分表示最佳）在各标准上打分。向用户获取分数，或在有条件的情况下调研客观数据（如成本、速度指标）。记录假设条件和数据来源。如需复杂打分技巧，请参阅resources/methodology.md中的校准方法。

步骤4：计算加权得分并分析结果

计算每个选项的加权得分（各标准得分×权重的总和）。根据总得分对选项进行排名。识别得分接近的情况（选项得分差距在5%以内）。检查决策的敏感性（调整某一项权重是否会改变决策结果）。解读指导请参阅【敏感性分析】部分。

步骤5：验证质量并交付建议

使用resources/evaluators/rubric_decision_matrix.json进行自我评估（最低得分≥3.5）。提交

decision-matrix.md

文件，其中需包含明确的建议、分析揭示的关键权衡点、对假设条件的敏感性说明，以及下一步建议（针对得分接近的选项收集更多数据、与利益相关方验证）。

Framing Questions

框架问题

To clarify the decision:

What specific decision are we making? (Choose X from Y alternatives)
What happens if we don't decide or choose wrong?
When do we need to decide by?
Can we choose multiple options or only one?

To identify alternatives:

What are all the named options we're considering?
Are there other alternatives we're ruling out immediately? Why?
What's the "do nothing" or status quo option?

To surface must-haves:

Are there absolute dealbreakers? (Budget cap, timeline requirement, compliance need)
Which constraints are flexible vs rigid?

用于明确决策：

我们要做的具体决策是什么？（从Y个备选方案中选择X）
如果不做决策或决策错误会有什么后果？
我们需要在何时做出决策？
我们可以选择多个选项还是只能选一个？

用于识别备选方案：

我们正在考虑的所有具体命名选项有哪些？
有没有我们直接排除的其他备选方案？原因是什么？
“不采取行动”或维持现状的选项是什么？

用于明确必须满足的要求：

有没有绝对的否决项？（如预算上限、时间要求、合规需求）
哪些约束条件是灵活的，哪些是刚性的？

Criterion Types

标准类型

Common categories for criteria (adapt to your decision):

Financial Criteria:

Upfront cost, ongoing cost, ROI, payback period, budget impact
Typical weight: 20-40% (higher for cost-sensitive decisions)

Performance Criteria:

Speed, quality, reliability, scalability, capacity, throughput
Typical weight: 30-50% (higher for technical decisions)

Risk Criteria:

Implementation risk, reversibility, vendor lock-in, technical debt, compliance risk
Typical weight: 10-25% (higher for enterprise/regulated environments)

Strategic Criteria:

Alignment with goals, future flexibility, competitive advantage, market positioning
Typical weight: 15-30% (higher for long-term decisions)

Operational Criteria:

Ease of use, maintenance burden, training required, integration complexity
Typical weight: 10-20% (higher for internal tools)

Stakeholder Criteria:

Team preference, user satisfaction, executive alignment, customer impact
Typical weight: 5-15% (higher for change management contexts)

以下是常见的标准类别（可根据决策调整）：

财务标准：

前期成本、持续成本、投资回报率（ROI）、投资回收期、预算影响
典型权重：20-40%（对成本敏感的决策权重更高）

性能标准：

速度、质量、可靠性、可扩展性、容量、吞吐量
典型权重：30-50%（技术决策的权重更高）

风险标准：

实施风险、可逆性、供应商锁定、技术债务、合规风险
典型权重：10-25%（企业/受监管环境的权重更高）

战略标准：

与目标的一致性、未来灵活性、竞争优势、市场定位
典型权重：15-30%（长期决策的权重更高）

运营标准：

易用性、维护负担、培训需求、集成复杂度
典型权重：10-20%（内部工具的权重更高）

利益相关方标准：

团队偏好、用户满意度、管理层共识、客户影响
典型权重：5-15%（变更管理场景的权重更高）

Weighting Approaches

权重分配方法

Method 1: Direct Allocation (simplest) Stakeholders assign percentages totaling 100%. Quick but can be arbitrary.

Method 2: Pairwise Comparison (more rigorous) Compare each criterion pair: "Is cost more important than speed?" Build ranking, then assign weights.

Method 3: Must-Have vs Nice-to-Have (filters first) Separate absolute requirements (pass/fail) from weighted criteria. Only evaluate options that pass must-haves.

Method 4: Stakeholder Averaging (group decisions) Each stakeholder assigns weights independently, then average. Reveals divergence in priorities.

See resources/methodology.md for detailed facilitation techniques.

方法1：直接分配法（最简单） 由利益相关方分配权重，总和为100%。速度快但可能存在主观性。

方法2：两两比较法（更严谨） 对每一对标准进行比较：“成本比速度更重要吗？” 构建排名后再分配权重。

方法3：必备项vs期望项（先筛选） 将绝对要求（通过/不通过）与加权标准分开。仅评估满足必备项的选项。

方法4：利益相关方平均法（群体决策） 每个利益相关方独立分配权重，然后取平均值。可揭示优先级的差异。

如需详细的引导技巧，请参阅resources/methodology.md。

Sensitivity Analysis

敏感性分析

After calculating scores, check robustness:

1. Close calls: Options within 5-10% of winner → Need more data or second opinion 2. Dominant criteria: One criterion driving entire decision → Is weight too high? 3. Weight sensitivity: Would swapping two criterion weights flip the winner? → Decision is fragile 4. Score sensitivity: Would adjusting one score by ±1 point flip the winner? → Decision is sensitive to that data point

Red flags:

Winner changes with small weight adjustments → Need stakeholder alignment on priorities
One option wins every criterion → Matrix is overkill, choice is obvious
Scores are mostly guesses → Gather more data before deciding

计算得分后，检查决策的稳健性：

1. 得分接近： 与胜出者得分差距在5-10%以内的选项 → 需要更多数据或第二意见 2. 主导标准： 某一项标准主导整个决策 → 该标准的权重是否过高？ 3. 权重敏感性： 交换两项标准的权重是否会改变胜出者？→ 决策结果不稳定 4. 得分敏感性： 某一项得分调整±1分是否会改变胜出者？→ 决策对该数据点敏感

警示信号：

小幅调整权重就会改变胜出者 → 需要利益相关方就优先级达成共识
某一选项在所有标准上都胜出 → 决策矩阵过于冗余，选择显而易见
大部分得分都是猜测的 → 决策前需收集更多数据

Common Patterns

常见应用场景

Technology Selection:

Criteria: Cost, performance, ecosystem maturity, team familiarity, vendor support
Weight: Performance and maturity typically 50%+

Vendor Evaluation:

Criteria: Price, features, integration, support, reputation, contract terms
Weight: Features and integration typically 40-50%

Strategic Choices:

Criteria: Market opportunity, resource requirements, risk, alignment, timing
Weight: Market opportunity and alignment typically 50%+

Hiring Decisions:

Criteria: Experience, culture fit, growth potential, compensation expectations, availability
Weight: Experience and culture fit typically 50%+

Feature Prioritization:

Criteria: User impact, effort, strategic value, risk, dependencies
Weight: User impact and strategic value typically 50%+

技术选型：

标准：成本、性能、生态成熟度、团队熟悉度、供应商支持
权重：性能和成熟度通常占50%以上

供应商评估：

标准：价格、功能、集成能力、支持服务、声誉、合同条款
权重：功能和集成能力通常占40-50%

战略选择：

标准：市场机会、资源需求、风险、目标一致性、时机
权重：市场机会和目标一致性通常占50%以上

招聘决策：

标准：经验、文化契合度、成长潜力、薪酬预期、可入职时间
权重：经验和文化契合度通常占50%以上

功能优先级排序：

标准：用户影响、实施成本、战略价值、风险、依赖关系
权重：用户影响和战略价值通常占50%以上

When NOT to Use This Skill

不适用场景

Skip decision matrix if:

Only one viable option (no real alternatives to compare)
Decision is binary yes/no with single criterion (use simpler analysis)
Options differ on only one dimension (just compare that dimension)
Decision is urgent and stakes are low (analysis overhead not worth it)
Criteria are impossible to define objectively (purely emotional/aesthetic choice)
You already know the answer (using matrix to justify pre-made decision is waste)

Use instead:

Single criterion → Simple ranking or threshold check
Binary decision → Pro/con list or expected value calculation
Highly uncertain → Scenario planning or decision tree
Purely subjective → Gut check or user preference vote

以下情况请避免使用决策矩阵：

只有一个可行选项（没有真正的备选方案可对比）
基于单一标准的二元决策（是/否）（使用更简单的分析方法）
选项仅在一个维度上存在差异（直接对比该维度即可）
决策紧急且风险低（分析成本不值得）
标准无法客观定义（纯情感/审美选择）
你已经知道答案（用矩阵来证明预先做出的决策是浪费时间）

替代方法：

单一标准 → 简单排名或阈值检查
二元决策 → 优缺点列表或期望值计算
高度不确定性 → 场景规划或决策树
纯主观选择 → 直觉判断或用户偏好投票

Quick Reference

快速参考

Process:

Frame decision → List alternatives
Identify criteria → Assign weights (sum to 100%)
Score each option on each criterion (1-10 scale)
Calculate weighted scores → Rank options
Check sensitivity → Deliver recommendation

Resources:

resources/template.md - Structured matrix format and weighting techniques
resources/methodology.md - Advanced techniques (group facilitation, calibration, sensitivity analysis)
resources/evaluators/rubric_decision_matrix.json - Quality checklist before delivering

Deliverable:

decision-matrix.md

file with table, rationale, and recommendation

流程：

明确决策框架 → 列出备选方案
确定标准 → 分配权重（总和为100%）
为每个选项在各标准上打分（1-10分制）
计算加权得分 → 对选项排名
检查敏感性 → 交付建议

资源：

resources/template.md - 结构化矩阵格式和权重分配技巧
resources/methodology.md - 高级技巧（群体引导、校准、敏感性分析）
resources/evaluators/rubric_decision_matrix.json - 交付前的质量检查清单

交付物： 包含表格、依据和建议的

decision-matrix.md

文件