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)

Actions (Outputs)

Step 2: Build the Decision Table

Decision Table: [Name]

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

Actions

Rules

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

Step 3: Define and Weight Criteria

Criteria

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

Step 5: Calculate Weighted Scores

Decision Matrix

Calculation: Weighted Score = Σ(Score × Weight)

Step 6: Sensitivity Analysis

Test how results change if weights shift:

Sensitivity Analysis

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]

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

Chance Events

Step 3: Assign Values and Probabilities

Outcomes

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

Decision Logic

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
      rationale: "Team adoption is critical"
    - id: C2
      name: "Feature Set"
      weight: 0.25
      rationale: "Must handle complex workflows"
    - id: C3
      name: "Integration"
      weight: 0.25
      rationale: "Slack integration required"
    - id: C4
      name: "Cost"
      weight: 0.20
      rationale: "Within budget constraint"

  scores:
    - alternative: A
      scores: {C1: 3, C2: 5, C3: 4, C4: 3}
      weighted_total: 3.75
    - alternative: B
      scores: {C1: 5, C2: 4, C3: 5, C4: 4}
      weighted_total: 4.50
    - alternative: C
      scores: {C1: 4, C2: 4, C3: 3, C4: 5}
      weighted_total: 3.95

  ranking:
    - rank: 1
      alternative: B
      score: 4.50
    - rank: 2
      alternative: C
      score: 3.95
    - rank: 3
      alternative: A
      score: 3.75

  sensitivity:
    - scenario: "Cost weight +10%"
      winner: C
      stable: false
    - scenario: "Ease of Use weight +10%"
      winner: B
      stable: true

  recommendation:
    choice: B
    confidence: high
    rationale:
      - "Highest weighted score (4.50)"
      - "Stable across sensitivity scenarios"
      - "Best ease of use for team adoption"

  risks:
    - description: "Asana pricing may increase"
      likelihood: medium
      impact: low
      mitigation: "Negotiate annual contract"

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