decision-analysis

name	decision-analysis
description	Decision modeling using decision tables, weighted scoring matrices, and decision trees. Structures complex decisions with clear criteria, alternatives evaluation, and outcome prediction.
allowed-tools	Read, Glob, Grep, Task, Skill

Decision Analysis

Model and analyze complex decisions using structured techniques: decision tables, weighted scoring matrices, and decision trees. Creates clear, defensible decision frameworks with traceable rationale.

What is Decision Analysis?

Decision Analysis is a systematic approach to evaluating complex choices by breaking them down into components: objectives, alternatives, criteria, and trade-offs. It transforms subjective judgment into structured, transparent reasoning.

Technique	Best For	Output
Decision Table	Rule-based logic, many conditions	Action based on condition combinations
Weighted Scoring Matrix	Multi-criteria comparison	Ranked alternatives with scores
Decision Tree	Sequential decisions, uncertainty	Optimal path with probabilities
Pugh Matrix	Concept selection, design choices	Best concept vs baseline

Technique 1: Decision Tables

What is a Decision Table?

A decision table captures complex conditional logic in a compact grid format. It lists all combinations of conditions and their corresponding actions.

Component	Description	Example
Conditions	Input variables/states	Customer type, Order value
Actions	Outcomes/responses	Apply discount, Require approval
Rules	Condition combinations	IF Premium AND >$1000 THEN 20% off

Decision Table Workflow

Step 1: Identify Conditions and Actions

## Decision Context

**Decision:** [What are we deciding?]
**Trigger:** [When is this decision made?]

### Conditions (Inputs)
| # | Condition | Possible Values |
|---|-----------|-----------------|
| C1 | [Condition 1] | [Value A / Value B / ...] |
| C2 | [Condition 2] | [Yes / No] |
| C3 | [Condition 3] | [Low / Medium / High] |

### Actions (Outputs)
| # | Action | Description |
|---|--------|-------------|
| A1 | [Action 1] | [What happens] |
| A2 | [Action 2] | [What happens] |

Step 2: Build the Decision Table

## Decision Table: [Name]

| Rule | C1 | C2 | C3 | A1 | A2 |
|------|----|----|----|----|----|
| R1 | Premium | Yes | High | X | - |
| R2 | Premium | Yes | Low | X | X |
| R3 | Standard | Yes | - | - | X |
| R4 | Standard | No | High | - | - |
| R5 | - | No | Low | - | X |

**Legend:** X = Execute action, - = Skip, [blank] = Any value

Step 3: Validate Completeness

Check	Question	Pass?
Completeness	All condition combinations covered?	☐
Consistency	No contradictory rules?	☐
Uniqueness	Each combination maps to one outcome?	☐
Simplification	Can rules be consolidated?	☐

Decision Table Template

## Decision Table: [Decision Name]

**Context:** [Business context]
**Owner:** [Decision owner]
**Last Updated:** [ISO date]

### Conditions
| ID | Condition | Values |
|----|-----------|--------|
| C1 | | |
| C2 | | |

### Actions
| ID | Action | Description |
|----|--------|-------------|
| A1 | | |
| A2 | | |

### Rules
| Rule | C1 | C2 | → | A1 | A2 | Notes |
|------|----|----|---|----|----|-------|
| R1 | | | | | | |
| R2 | | | | | | |

### Validation
- [ ] All combinations covered
- [ ] No contradictions
- [ ] Rules simplified

Technique 2: Weighted Scoring Matrix

What is a Weighted Scoring Matrix?

A weighted scoring matrix (also called decision matrix or Pugh matrix) evaluates multiple alternatives against weighted criteria to produce a ranked list.

Component	Description
Alternatives	Options being compared
Criteria	Factors for evaluation
Weights	Importance of each criterion (sum to 100%)
Scores	Rating of each alternative on each criterion
Weighted Score	Score × Weight, summed across criteria

Weighted Scoring Workflow

Step 1: Define the Decision

## Decision Context

**Decision:** [What are we choosing?]
**Objective:** [What outcome do we want?]
**Constraints:** [Non-negotiable requirements]
**Timeline:** [When must we decide?]

Step 2: Identify Alternatives

## Alternatives

| # | Alternative | Description | Source |
|---|-------------|-------------|--------|
| A | [Option A] | [Brief description] | [How identified] |
| B | [Option B] | [Brief description] | [How identified] |
| C | [Option C] | [Brief description] | [How identified] |

Step 3: Define and Weight Criteria

## Criteria

| # | Criterion | Description | Weight | Rationale |
|---|-----------|-------------|--------|-----------|
| 1 | [Criterion 1] | [What it measures] | 30% | [Why this weight] |
| 2 | [Criterion 2] | [What it measures] | 25% | [Why this weight] |
| 3 | [Criterion 3] | [What it measures] | 25% | [Why this weight] |
| 4 | [Criterion 4] | [What it measures] | 20% | [Why this weight] |
|   | **Total** | | **100%** | |

Weighting Methods:

Method	Description	When to Use
Direct Assignment	Stakeholders assign weights directly	Clear priorities, experienced team
Pairwise Comparison	Compare criteria pairs (AHP)	Unclear priorities, need consensus
Ranking	Rank criteria, convert to weights	Quick, approximate
Equal Weights	All criteria weighted equally	No clear priority, initial analysis

Step 4: Score Alternatives

## Scoring Scale

| Score | Meaning |
|-------|---------|
| 5 | Excellent - Fully meets/exceeds criterion |
| 4 | Good - Mostly meets criterion |
| 3 | Adequate - Partially meets criterion |
| 2 | Poor - Minimally meets criterion |
| 1 | Unacceptable - Does not meet criterion |

Step 5: Calculate Weighted Scores

## Decision Matrix

| Criterion | Weight | Alt A | Alt B | Alt C |
|-----------|--------|-------|-------|-------|
| Criterion 1 | 30% | 4 | 3 | 5 |
| Criterion 2 | 25% | 3 | 5 | 4 |
| Criterion 3 | 25% | 5 | 4 | 3 |
| Criterion 4 | 20% | 4 | 4 | 4 |
| **Weighted Score** | | **3.95** | **3.95** | **4.05** |
| **Rank** | | 2 | 3 | 1 |

**Calculation:** Weighted Score = Σ(Score × Weight)

Step 6: Sensitivity Analysis

Test how results change if weights shift:

## Sensitivity Analysis

| Scenario | Weight Change | Winner | Confidence |
|----------|---------------|--------|------------|
| Baseline | As defined | Alt C | - |
| Cost +10% | C1: 40%, others adjusted | Alt A | Low |
| Quality +10% | C2: 35%, others adjusted | Alt C | High |

**Robustness:** [Is the winner stable across scenarios?]

Pugh Matrix (Concept Selection)

A specialized scoring matrix comparing alternatives to a baseline:

## Pugh Matrix: [Decision]

**Baseline:** [Reference option - usually current state or simplest option]

| Criterion | Weight | Alt A vs Baseline | Alt B vs Baseline | Alt C vs Baseline |
|-----------|--------|-------------------|-------------------|-------------------|
| Criterion 1 | 30% | + | S | ++ |
| Criterion 2 | 25% | - | + | S |
| Criterion 3 | 25% | S | + | - |
| Criterion 4 | 20% | + | S | + |
| **Σ Plus** | | 2 | 2 | 2 |
| **Σ Minus** | | 1 | 0 | 1 |
| **Σ Same** | | 1 | 2 | 1 |
| **Net Score** | | +1 | +2 | +1 |

**Legend:** ++ Much better, + Better, S Same, - Worse, -- Much worse

Technique 3: Decision Trees

What is a Decision Tree?

A decision tree maps sequential decisions and uncertain events to visualize possible paths and outcomes. It's ideal for decisions with multiple stages or probabilistic outcomes.

Node Type	Symbol	Description
Decision Node	□	Choice point (you control)
Chance Node	○	Uncertain event (probabilities)
End Node	△	Final outcome (value)

Decision Tree Workflow

Step 1: Frame the Decision

## Decision Tree Context

**Decision:** [Primary decision]
**Objective:** [What we're optimizing - NPV, utility, etc.]
**Time Horizon:** [How far into future]
**Key Uncertainties:** [Major unknown factors]

Step 2: Identify Decision Points and Uncertainties

## Structure

### Decision Points
| # | Decision | Options |
|---|----------|---------|
| D1 | [First decision] | Option A, Option B |
| D2 | [Subsequent decision] | Option X, Option Y |

### Chance Events
| # | Event | Outcomes | Probabilities |
|---|-------|----------|---------------|
| E1 | [Uncertainty 1] | High, Low | 60%, 40% |
| E2 | [Uncertainty 2] | Success, Failure | 70%, 30% |

Step 3: Assign Values and Probabilities

## Outcomes

| Path | Sequence | Probability | Value | Expected Value |
|------|----------|-------------|-------|----------------|
| P1 | D1:A → E1:High → D2:X | 0.60 | $100K | $60K |
| P2 | D1:A → E1:High → D2:Y | 0.60 | $80K | $48K |
| P3 | D1:A → E1:Low | 0.40 | $20K | $8K |
| P4 | D1:B → E2:Success | 0.70 | $150K | $105K |
| P5 | D1:B → E2:Failure | 0.30 | -$50K | -$15K |

Step 4: Calculate Expected Values (Rollback)

Work backwards from end nodes:

## Rollback Analysis

### Chance Node E1 (after D1:A)
EV = (0.60 × max($100K, $80K)) + (0.40 × $20K)
EV = (0.60 × $100K) + $8K = $68K

### Chance Node E2 (after D1:B)
EV = (0.70 × $150K) + (0.30 × -$50K)
EV = $105K - $15K = $90K

### Decision Node D1
Choose B: EV = $90K > $68K

**Recommendation:** Choose Option B

Decision Tree Mermaid Diagram

flowchart TD
    D1{Decision 1<br/>Choose A or B?}

    D1 -->|A| E1((Event 1<br/>Market))
    D1 -->|B| E2((Event 2<br/>Tech))

    E1 -->|High 60%| D2{Decision 2}
    E1 -->|Low 40%| OUT1[/$20K/]

    D2 -->|X| OUT2[/$100K/]
    D2 -->|Y| OUT3[/$80K/]

    E2 -->|Success 70%| OUT4[/$150K/]
    E2 -->|Failure 30%| OUT5[/-$50K/]

    style D1 fill:#ffcc00
    style D2 fill:#ffcc00
    style E1 fill:#66ccff
    style E2 fill:#66ccff

DMN-Lite: Decision Model Notation

For simple, repeatable decisions, use a lightweight DMN approach:

## Decision: [Name]

**Decision ID:** DEC-001
**Business Context:** [When this decision is made]

### Input Data
| Input | Type | Source |
|-------|------|--------|
| Customer Segment | Text | CRM |
| Order Value | Currency | Order System |
| Credit Score | Number | Credit Bureau |

### Decision Logic

```text
IF Customer Segment = "Premium" AND Order Value > 1000
  THEN Discount = 20%
ELSE IF Customer Segment = "Premium"
  THEN Discount = 10%
ELSE IF Order Value > 5000
  THEN Discount = 15%
ELSE
  THEN Discount = 0%

Output

Output	Type	Range
Discount	Percentage	0% - 20%

Output Formats

Narrative Summary

## Decision Analysis Summary

**Decision:** [What was decided]
**Date:** [ISO date]
**Analyst:** decision-analyst

### Context
[2-3 sentences on why this decision was needed]

### Approach
- **Technique Used:** [Decision Table / Weighted Matrix / Decision Tree]
- **Alternatives Considered:** [Count and brief list]
- **Criteria Applied:** [Count and key criteria]

### Recommendation
**Recommended Option:** [Name]
**Rationale:** [Key reasons - 2-3 points]
**Confidence:** High / Medium / Low

### Key Trade-offs
| Factor | Recommended Option | Runner-up |
|--------|-------------------|-----------|
| [Factor 1] | [Assessment] | [Assessment] |
| [Factor 2] | [Assessment] | [Assessment] |

### Risks and Mitigations
| Risk | Likelihood | Impact | Mitigation |
|------|------------|--------|------------|
| [Risk 1] | H/M/L | H/M/L | [Action] |

### Next Steps
1. [Immediate action]
2. [Follow-up action]

Structured Data (YAML)

decision_analysis:
  version: "1.0"
  date: "2025-01-15"
  analyst: "decision-analyst"

  context:
    decision: "Select project management tool"
    objective: "Maximize team productivity while minimizing cost"
    constraints:
      - "Budget under $500/month"
      - "Must integrate with team messaging platform"
    timeline: "Decision by end of Q1"

  technique: "weighted_scoring_matrix"

  alternatives:
    - id: A
      name: "Tool A (Enterprise)"
      description: "Enterprise-grade, feature-rich work item tracking"
    - id: B
      name: "Tool B (Collaborative)"
      description: "User-friendly, good collaboration features"
    - id: C
      name: "Tool C (Developer-Focused)"
      description: "Modern, developer-focused interface"

  criteria:
    - id: C1
      name: "Ease of Use"
      weight: 0.30
       
      
       
       
       
      
       
       
       
      
       
       
       

  
      
       { ,  ,  ,  }
       
      
       { ,  ,  ,  }
       
      
       { ,  ,  ,  }
       

  
      
       
       
      
       
       
      
       
       

  
      
       
       
      
       
       

  
     
     
    
       
       
       

  
      
       
       
       

Mermaid Decision Matrix Visualization

quadrantChart
    title Decision Matrix - Tool Selection
    x-axis Low Cost --> High Cost
    y-axis Low Features --> High Features
    quadrant-1 Premium
    quadrant-2 Best Value
    quadrant-3 Budget
    quadrant-4 Expensive Limited
    "Tool A (Enterprise)": [0.7, 0.9]
    "Tool B (Collaborative)": [0.5, 0.7]
    "Tool C (Developer)": [0.3, 0.6]
    "Tool D (Basic)": [0.2, 0.3]

When to Use

Scenario	Technique
Rule-based logic with many conditions	Decision Table
Comparing multiple options on criteria	Weighted Scoring Matrix
Sequential decisions with uncertainty	Decision Tree
Concept selection vs baseline	Pugh Matrix
Simple repeatable business rules	DMN-Lite
Quick relative comparison	Pugh Matrix
Need stakeholder buy-in	Weighted Scoring (transparent)

Integration

Upstream

• stakeholder-analysis - Identify decision makers and criteria sources
• root-cause-analysis - Understand problem before deciding solution
• swot-pestle-analysis - Strategic context for decisions

Downstream

• Requirements - Decision drives requirement priorities
• Risk registers - Capture decision risks
• Implementation plans - Execute chosen alternative

Related Skills

• prioritization - MoSCoW, Kano for feature prioritization
• risk-analysis - Risk assessment for decision alternatives
• root-cause-analysis - Problem analysis before solution selection
• business-model-canvas - Strategic business decisions
• stakeholder-analysis - Decision maker identification

Version History

• v1.0.0 (2025-12-26): Initial release