reference-class-forecasting

Reference Class Forecasting

Table of Contents

• Interactive Menu
• Quick Reference
• Resource Files

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Interactive Menu

What would you like to do?

Core Workflows

1. Find My Base Rate - Identify reference class and get statistical baseline

• Guided process to select correct reference class
• Search strategies for finding historical frequencies
• Validation that you have the right anchor

2. Test "This Time Is Different" - Challenge uniqueness claims

• Reversal test for uniqueness bias
• Similarity matching framework
• Burden of proof calculator

3. Calculate Funnel Base Rates - Multi-stage probability chains

• When no single base rate exists
• Sequential probability modeling
• Product rule for compound events

4. Validate My Reference Class - Ensure you chose the right comparison set

• Too broad vs too narrow test
• Homogeneity check
• Sample size evaluation

5. Learn the Framework - Deep dive into methodology

• Read Outside View Principles
• Read Reference Class Selection Guide
• Read Common Pitfalls

6. Exit - Return to main forecasting workflow

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1. Find My Base Rate

Let's establish your statistical baseline.

Step 1: What are you forecasting?

Tell me the specific event or outcome you're predicting.

Example prompts:

• "Will this startup succeed?"
• "Will this bill pass Congress?"
• "Will this project launch on time?"

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Step 2: Identify the Reference Class

I'll help you identify what bucket this belongs to.

Framework:

• Too broad: "All companies" → meaningless
• Just right: "Seed-stage B2B SaaS startups in fintech"
• Too narrow: "Companies founded by people named Steve in 2024" → no data

Key Questions:

1. What type of entity is this? (company, bill, project, person, etc.)
2. What stage/size/category?
3. What industry/domain?
4. What time period is relevant?

I'll work with you to refine this until we have a specific, searchable class.

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Step 3: Search for Historical Data

I'll help you find the base rate using:

• Web search for published statistics
• Academic studies on success rates
• Government/industry reports
• Proxy metrics if direct data unavailable

Search Strategy:

"historical success rate of [reference class]"
"[reference class] failure statistics"
"[reference class] survival rate"
"what percentage of [reference class]"

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Step 4: Set Your Anchor

Once we find the base rate, that becomes your starting probability.

The Rule:

Treat this base rate as your starting point. Adjust only when you have specific, evidence-based reasons from your "inside view" analysis.

Default anchors if no data found:

• Novel innovation: 10-20% (most innovations fail)
• Established industry: 50% (uncertain)
• Regulated/proven process: 70-80% (systems work)

Next: Return to menu or proceed to inside view analysis.

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2. Test "This Time Is Different"

Challenge uniqueness bias.

When someone (including yourself) believes "this case is special," we need to stress-test that belief.

The Uniqueness Audit

Question 1: Similarity Matching

• What are 5 historical cases that are most similar to this one?
• For each, what was the outcome?
• How is your case materially different from these?

Question 2: The Reversal Test

• If someone claimed a different case was "unique" for the same reasons you're claiming, would you accept it?
• Are you applying special pleading?

Question 3: Burden of Proof The base rate says [X]%. You claim it should be [Y]%.

Calculate the gap: |Y - X|

Required evidence strength:

• Gap < 10%: Minimal evidence needed
• Gap 10-30%: Moderate evidence needed (2-3 specific factors)
• Gap > 30%: Extraordinary evidence needed (multiple independent strong signals)

Output

I'll tell you:

1. Whether "this time is different" is justified
2. How much you can reasonably adjust from the base rate
3. What evidence would be needed to justify larger moves

Next: Return to menu

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3. Calculate Funnel Base Rates

For multi-stage processes without a single base rate.

When to Use

• No direct statistic exists (e.g., "success rate of X")
• Event requires multiple sequential steps
• Each stage has independent probabilities

The Funnel Method

Example: "Will Bill X become law?"

No direct data on "Bill X success rate," but we can model the funnel:

1. Stage 1: Bills introduced → Bills that reach committee

   • P(committee | introduced) = ?

2. Stage 2: Bills in committee → Bills that reach floor vote

   • P(floor | committee) = ?

3. Stage 3: Bills voted on → Bills that pass

   • P(pass | floor vote) = ?

Final Base Rate:

P(law) = P(committee) × P(floor) × P(pass)

Process

I'll help you:

1. Decompose the event into sequential stages
2. Search for statistics on each stage
3. Multiply probabilities using the product rule
4. Validate the model (are stages truly independent?)

Common Funnels

• Startup success: Seed → Series A → Profitability → Exit
• Drug approval: Discovery → Trials → FDA → Market
• Project delivery: Planning → Development → Testing → Launch

Next: Return to menu

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4. Validate My Reference Class

Ensure you chose the right comparison set.

The Three Tests

Test 1: Homogeneity

• Are the members of this class actually similar enough?
• Is there high variance in outcomes?
• Should you subdivide further?

Example: "Tech startups" is too broad (consumer vs B2B vs hardware are very different). Subdivide.

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Test 2: Sample Size

• Do you have enough historical cases?
• Minimum: 20-30 cases for meaningful statistics
• If N < 20: Widen the class or acknowledge high uncertainty

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Test 3: Relevance

• Have conditions changed since the historical data?
• Are there structural differences (regulation, technology, market)?
• Time decay: Data from >10 years ago may be stale

Validation Checklist

I'll walk you through:

• Class has 20+ historical examples
• Members are reasonably homogeneous
• Data is from relevant time period
• No major structural changes since data collection
• Class is specific enough to be meaningful
• Class is broad enough to have data

Output: Confidence level in your reference class (High/Medium/Low)

Next: Return to menu

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5. Learn the Framework

Deep dive into the methodology.

Resource Files

📄 Outside View Principles

• Statistical thinking vs narrative thinking
• Why the outside view beats experts
• Kahneman's planning fallacy research
• When outside view fails

📄 Reference Class Selection Guide

• Systematic method for choosing comparison sets
• Balancing specificity vs data availability
• Similarity metrics and matching
• Edge cases and judgment calls

📄 Common Pitfalls

• Base rate neglect examples
• "This time is different" bias
• Overfitting to small samples
• Ignoring regression to the mean
• Availability bias in class selection

Next: Return to menu

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Quick Reference

The Outside View Commandments

1. Base Rate First: Establish statistical baseline BEFORE analyzing specifics
2. Assume Average: Treat case as typical until proven otherwise
3. Burden of Proof: Large deviations from base rate require strong evidence
4. Class Precision: Reference class should be specific but data-rich
5. No Narratives: Resist compelling stories; trust frequencies

One-Sentence Summary

Find what usually happens to things like this, start there, and only move with evidence.

Integration with Other Skills

• Before: Use estimation-fermi if you need to calculate base rate from components
• After: Use bayesian-reasoning-calibration to update from base rate with new evidence
• Companion: Use scout-mindset-bias-check to validate you're not cherry-picking the reference class

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Resource Files

📁 resources/

• outside-view-principles.md - Theory and research
• reference-class-selection.md - Systematic selection method
• common-pitfalls.md - What to avoid

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