Decision modeling using decision tables, weighted scoring matrices, and decision trees. Structures complex decisions with clear criteria, alternatives evaluation, and outcome prediction.
Uses decision tables, weighted scoring matrices, and decision trees to structure complex choices with clear criteria and outcome prediction. Triggers when evaluating multi-criteria alternatives, modeling rule-based logic, or analyzing sequential decisions with uncertainty.
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Model and analyze complex decisions using structured techniques: decision tables, weighted scoring matrices, and decision trees. Creates clear, defensible decision frameworks with traceable rationale.
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 |
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 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] |
## 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
| 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: [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
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 |
## Decision Context
**Decision:** [What are we choosing?]
**Objective:** [What outcome do we want?]
**Constraints:** [Non-negotiable requirements]
**Timeline:** [When must we decide?]
## 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] |
## 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 |
## 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 |
## 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)
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?]
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
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 Context
**Decision:** [Primary decision]
**Objective:** [What we're optimizing - NPV, utility, etc.]
**Time Horizon:** [How far into future]
**Key Uncertainties:** [Major unknown factors]
## 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% |
## 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 |
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
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
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 | Type | Range |
|---|---|---|
| Discount | Percentage | 0% - 20% |
## 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]
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"
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]
| 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) |
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