gh-lyndonkl-claude/skills/chain-estimation-decision-storytelling/resources/methodology.md

Advanced Chain Estimation → Decision → Storytelling Methodology

Workflow

Copy this checklist and track your progress:

Advanced Analysis Progress:
- [ ] Step 1: Select appropriate advanced technique for complexity
- [ ] Step 2: Build model (decision tree, Monte Carlo, real options)
- [ ] Step 3: Run analysis and interpret results
- [ ] Step 4: Validate robustness across scenarios
- [ ] Step 5: Translate technical findings into narrative

Step 1: Select appropriate advanced technique for complexity

Choose technique based on decision characteristics: decision trees for sequential choices, Monte Carlo for multiple interacting uncertainties, real options for flexibility value, multi-criteria analysis for qualitative + quantitative factors. See Technique Selection Guide for decision flowchart.

Step 2: Build model

Structure problem using chosen technique: define states and branches for decision trees, specify probability distributions for Monte Carlo, identify options and decision points for real options analysis, establish criteria and weights for multi-criteria. See technique-specific sections below for modeling guidance.

Step 3: Run analysis and interpret results

Execute calculations (manually for small trees, with tools for complex simulations), interpret output distributions or decision paths, identify dominant strategies or highest-value options, and quantify value of information or flexibility where applicable.

Step 4: Validate robustness across scenarios

Test assumptions with stress testing, vary key parameters to check sensitivity, compare results across different modeling approaches, and identify conditions where conclusion changes. See Sensitivity and Robustness Testing.

Step 5: Translate technical findings into narrative

Convert technical analysis into business language, highlight key insights without overwhelming with methodology, explain "so what" for decision-makers, and provide clear recommendation with confidence bounds. See Communicating Complex Analysis.

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Technique Selection Guide

Decision Trees → Sequential decisions with discrete outcomes and known probabilities

• Use when: Clear sequence of choices, branching scenarios, need optimal path
• Example: Build vs buy with adoption uncertainty

Monte Carlo Simulation → Multiple interacting uncertainties with continuous distributions

• Use when: Many uncertain variables, complex interactions, need probability distributions
• Example: Project NPV with uncertain cost, revenue, timeline

Real Options Analysis → Decisions with flexibility value (defer, expand, abandon)

• Use when: Uncertainty resolves over time, value of waiting, staged commitments
• Example: Pilot before full launch, expand if successful

Multi-Criteria Decision Analysis (MCDA) → Mix of quantitative and qualitative factors

• Use when: Multiple objectives, stakeholder tradeoffs, subjective criteria
• Example: Vendor selection (cost + quality + relationship)

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Decision Trees

Structure

• Decision node (□): Your choice
• Chance node (○): Uncertain outcome with probabilities
• Terminal node: Final payoff

Method

1. Map all decisions and chance events
2. Assign probabilities to chance events
3. Work backward: calculate EV at chance nodes, choose best at decision nodes
4. Identify optimal path

Example

□ Build vs Buy
├─ Build → ○ Success (60%) → $500k
│         └─ Fail (40%) → $100k
└─ Buy → ○ Fits (70%) → $400k
         └─ Doesn't (30%) → $150k

Build EV = (500 × 0.6) + (100 × 0.4) = $340k
Buy EV = (400 × 0.7) + (150 × 0.3) = $325k
Decision: Build (higher EV)

Value of Information

• EVPI = EV with perfect info - EV without info
• Tells you how much to spend on reducing uncertainty

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Monte Carlo Simulation

When to Use

• Multiple uncertain variables (>3)
• Complex interactions between variables
• Need full probability distribution of outcomes
• Continuous ranges (not discrete scenarios)

Method

1. Identify uncertain variables: cost, revenue, timeline, adoption rate, etc.
2. Define distributions: normal, log-normal, triangular, uniform
3. Specify correlations: if variables move together
4. Run simulation: 10,000+ iterations
5. Analyze output: mean, median, percentiles, probability of success

Distribution Types

• Normal: μ ± σ (height, measurement error)
• Log-normal: positively skewed (project duration, costs)
• Triangular: min/most likely/max (quick estimation)
• Uniform: all values equally likely (no information)

Interpretation

• P50 (median): 50% chance of exceeding
• P10/P90: 80% confidence interval
• Probability of target: P(NPV > $0), P(ROI > 20%)

Tools

• Excel: =NORM.INV(RAND(), mean, stdev)
• Python: numpy.random.normal(mean, stdev, size=10000)
• @RISK, Crystal Ball: Monte Carlo add-ins

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Real Options Analysis

Concept

Flexibility has value. Option to defer, expand, contract, or abandon is worth more than committing upfront.

When to Use

• Uncertainty resolves over time (can learn before committing)
• Irreversible investments (can't easily reverse)
• Staged decisions (pilot → scale)

Types of Options

• Defer: Wait for more information before committing
• Expand: Scale up if successful
• Contract/Abandon: Scale down or exit if unsuccessful
• Switch: Change approach mid-course

Valuation Approach

Simple NPV (no flexibility):

• Commit now: EV = Σ(outcome × probability)

With real option:

• Value = NPV of commitment + Value of flexibility
• Flexibility value = Expected payoff from optimal future decision - Expected payoff from committing now

Example

• Commit to full launch now: $1M investment, 60% success → $3M, 40% fail → $0

  • EV = (3M × 0.6) + (0 × 0.4) - 1M = $800K

• Pilot first ($200K), then decide:

  • Good pilot (60%) → full launch → EV $1.8M (0.6 × 3M - 1M)
  • Bad pilot (40%) → abandon → lose $200K
  • EV = (1.8M × 0.6) + (-0.2M × 0.4) = $1.0M

• Real option value = $1.0M - $800K = $200K (value of flexibility to learn first)

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Multi-Criteria Decision Analysis (MCDA)

When to Use

• Multiple objectives that can't be reduced to single metric (not just NPV)
• Qualitative + quantitative factors
• Stakeholder tradeoffs (different groups value different things)

Method

1. Identify criteria (from stakeholder perspectives)

• Cost, speed, quality, risk, strategic fit, customer impact, etc.

2. Weight criteria (based on priorities)

• Sum to 100%
• Finance might weight cost 40%, Product weights customer impact 30%

3. Score alternatives (1-5 or 1-10 scale on each criterion)

• Alternative A: Cost=4, Speed=2, Quality=5
• Alternative B: Cost=2, Speed=5, Quality=3

4. Calculate weighted scores

• A = (4 × 0.3) + (2 × 0.4) + (5 × 0.3) = 3.5
• B = (2 × 0.3) + (5 × 0.4) + (3 × 0.3) = 3.5

5. Sensitivity analysis on weights

• How much would weights need to change to flip the decision?

Handling Qualitative Criteria

• Scoring rubric: Define what 1, 3, 5 means for "strategic fit"
• Pairwise comparison: Compare alternatives head-to-head on each criterion
• Range: Use min-max scaling to normalize disparate units

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Sensitivity and Robustness Testing

One-Way Sensitivity

• Vary one parameter at a time (e.g., cost ±20%)
• Check if conclusion changes
• Identify which parameters matter most

Two-Way Sensitivity

• Vary two parameters simultaneously
• Create sensitivity matrix or contour plot
• Example: Cost (rows) × Revenue (columns) → NPV

Tornado Diagram

• Bar chart showing impact of each parameter
• Longest bars = most sensitive parameters
• Focus analysis on top 2-3 drivers

Scenario Analysis

• Define coherent scenarios (pessimistic, base, optimistic)
• Not just parameter ranges, but plausible futures
• Calculate outcome for each complete scenario

Break-Even Analysis

• At what value does conclusion change?
• "Need revenue >$500K to beat alternative"
• "If cost exceeds $300K, pivot to Plan B"

Stress Testing

• Extreme scenarios (worst case everything goes wrong)
• Identify fragility: "Works unless X and Y both fail"
• Build contingency plans for stress scenarios

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Communicating Complex Analysis

For Executives

Focus: Bottom line, confidence, risks

• Recommendation (1 sentence)
• Key numbers (EV, NPV, ROI)
• Confidence level (P10-P90 range)
• Top 2 risks + mitigations
• Decision criteria: "Proceed if X, pivot if Y"

For Technical Teams

Focus: Methodology, assumptions, sensitivity

• Modeling approach and rationale
• Key assumptions with justification
• Sensitivity analysis results
• Robustness checks performed
• Limitations of analysis

For Finance

Focus: Numbers, assumptions, financial metrics

• Cash flow timing
• Discount rate and rationale
• NPV, IRR, payback period
• Risk-adjusted returns
• Comparison to hurdle rate

General Principles

• Lead with conclusion, then support with analysis
• Show confidence bounds, not just point estimates
• Explain "so what", not just "what"
• Use visuals: probability distributions, decision trees, tornado charts
• Be honest about limitations: "Assumes X, sensitive to Y"

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Common Pitfalls in Advanced Analysis

False Precision

• Problem: Reporting $1,234,567 when uncertainty is ±50%
• Fix: Round appropriately. Use ranges, not points.

Ignoring Correlations

• Problem: Modeling all uncertainties as independent when they're linked
• Fix: Specify correlations in Monte Carlo (costs move together, revenue and volume linked)

Overfit ting Models

• Problem: Building complex models with 20 parameters when data is thin
• Fix: Keep models simple. Complexity doesn't equal accuracy.

Anchoring on Base Case

• Problem: Treating "most likely" as "expected value"
• Fix: Calculate probability-weighted EV. Assymetric distributions matter.

Analysis Paralysis

• Problem: Endless modeling instead of deciding
• Fix: Set time limits. "Good enough" threshold. Decide with available info.

Confirmation Bias

• Problem: Modeling to justify predetermined conclusion
• Fix: Model alternatives fairly. Seek disconfirming evidence. External review.

Ignoring Soft Factors

• Problem: Optimizing NPV while ignoring strategic fit, team morale, brand impact
• Fix: Use MCDA for mixed quantitative + qualitative. Make tradeoffs explicit.

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Advanced Tools and Resources

Spreadsheet Tools

• Excel: Data tables, Scenario Manager, Goal Seek
• Google Sheets: Same capabilities, collaborative

Specialized Software

• @RISK (Palisade): Monte Carlo simulation add-in for Excel
• Crystal Ball (Oracle): Similar Monte Carlo tool
• Python: numpy, scipy, simpy for custom simulations
• R: Statistical analysis and simulation

When to Use Tools vs. Manual

• Manual (small decision trees): < 10 branches, quick calculation
• Spreadsheet (medium complexity): Decision trees, simple Monte Carlo (< 5 variables)
• Specialized tools (high complexity): 10+ uncertain variables, complex correlations, sensitivity analysis

Learning Resources

• Decision analysis: "Decision Analysis for the Professional" - Skinner
• Monte Carlo: "Risk Analysis in Engineering" - Modarres
• Real options: "Real Options" - Copeland & Antikarov
• MCDA: "Multi-Criteria Decision Analysis" - Belton & Stewart

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Summary

Choose technique based on problem structure:

• Sequential choices → Decision trees
• Multiple uncertainties → Monte Carlo
• Flexibility value → Real options
• Mixed criteria → MCDA

Focus on:

• Robust conclusions (stress test assumptions)
• Clear communication (translate technical to business language)
• Actionable insights (not just numbers)
• Honest limits (acknowledge what analysis can't tell you)

Remember:

• Models inform decisions, don't make them
• Simple model well-executed beats complex model poorly-executed
• Transparency about assumptions matters more than sophistication
• "All models are wrong, some are useful" - George Box