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skills/reference-class-forecasting/resources/outside-view-principles.md

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+# Outside View Principles
+
+## Theory and Foundation
+
+### What is the Outside View?
+
+The **Outside View** is a forecasting method that relies on statistical baselines from similar historical cases rather than detailed analysis of the specific case at hand.
+
+**Coined by:** Daniel Kahneman and Amos Tversky
+**Alternative names:** Reference class forecasting, actuarial prediction, statistical prediction
+
+---
+
+## The Two Views Framework
+
+### Inside View (The Trap)
+- Focuses on unique details of the specific case
+- Constructs causal narratives
+- Emphasizes what makes "this time different"
+- Relies on expert intuition and judgment
+- Feels more satisfying and controllable
+
+**Example:** "Our startup will succeed because we have a great team, unique technology, strong market timing, and passionate founders."
+
+### Outside View (The Discipline)
+- Focuses on statistical patterns from similar cases
+- Ignores unique narratives initially
+- Emphasizes what usually happens to things like this
+- Relies on base rates and frequencies
+- Feels cold and impersonal
+
+**Example:** "Seed-stage B2B SaaS startups have a 10% success rate. We start at 10%."
+
+---
+
+## Why the Outside View Wins
+
+### Research Evidence
+
+**The Planning Fallacy Study (Kahneman)**
+- Students asked to predict thesis completion time
+- Inside view: Average prediction = 33 days
+- Actual average: 55 days
+- Outside view (based on past students): 48 days
+- **Result:** Outside view was 7× more accurate than inside view
+
+**Expert Predictions vs Base Rates**
+- Expert forecasters using inside view: 60% accuracy
+- Simple base rate models: 70% accuracy
+- **Result:** Ignoring expert judgment improves predictions
+
+**Why Experts Fail:**
+1. **Overweight unique details** (availability bias)
+2. **Construct plausible narratives** (hindsight bias)
+3. **Underweight statistical patterns** (base rate neglect)
+4. **Overconfident in causal understanding** (illusion of control)
+
+---
+
+## When Outside View Works Best
+
+### High-Signal Situations
+✓ Large historical datasets exist
+✓ Cases are reasonably similar
+✓ Outcomes are measurable
+✓ No major structural changes
+✓ Randomness plays a significant role
+
+**Examples:**
+- Startup success rates
+- Construction project delays
+- Drug approval timelines
+- Movie box office performance
+- Sports team performance
+
+---
+
+## When Outside View Fails
+
+### Low-Signal Situations
+✗ Truly novel events (no reference class)
+✗ Structural regime changes (e.g., new technology disrupts all patterns)
+✗ Extremely heterogeneous reference class
+✗ Small sample sizes (N < 20)
+✗ Deterministic physics-based systems
+
+**Examples:**
+- First moon landing (no reference class)
+- Pandemic with novel pathogen (limited reference class)
+- Cryptocurrency regulation (regime change)
+- Your friend's personality (N = 1)
+
+**What to do:** Use outside view as starting point, then heavily weight specific evidence
+
+---
+
+## Statistical Thinking vs Narrative Thinking
+
+### Narrative Thinking (Human Default)
+- Brain constructs causal stories
+- Connects dots into coherent explanations
+- Feels satisfying and convincing
+- **Problem:** Narratives are selected for coherence, not accuracy
+
+**Example narrative:** "Startup X will fail because the CEO is inexperienced, the market is crowded, and they're burning cash."
+
+This might be true, but:
+- Experienced CEOs also fail
+- Crowded markets have winners
+- Cash burn is normal for startups
+
+The narrative cherry-picks evidence.
+
+### Statistical Thinking (Discipline Required)
+- Brain resists cold numbers
+- Requires active effort to override intuition
+- Feels unsatisfying and reductive
+- **Benefit:** Statistics aggregate all past evidence, not just confirming cases
+
+**Example statistical:** "80% of startups with this profile fail within 3 years. Start at 80% failure probability."
+
+---
+
+## The Planning Fallacy in Depth
+
+### What It Is
+Systematic tendency to underestimate time, costs, and risks while overestimating benefits.
+
+### Why It Happens
+1. **Focus on success plan:** Ignore failure modes
+2. **Best-case scenario bias:** Assume things go smoothly
+3. **Neglect of base rates:** "Our project is different"
+4. **Anchoring on ideal conditions:** Forget reality intrudes
+
+### The Fix: Outside View
+Instead of asking "How long will our project take?" ask:
+- "How long did similar projects take?"
+- "What was the distribution of outcomes?"
+- "What percentage ran late? By how much?"
+
+**Rule:** Assume your project is **average** for its class until proven otherwise.
+
+---
+
+## Regression to the Mean
+
+### The Phenomenon
+Extreme outcomes tend to be followed by more average outcomes.
+
+**Examples:**
+- Hot hand in basketball → Returns to average
+- Stellar quarterly earnings → Next quarter closer to mean
+- Brilliant startup idea → Execution regresses to mean
+
+### Implication for Forecasting
+If you're predicting based on an extreme observation:
+- **Adjust toward the mean** unless you have evidence the extreme is sustainable
+- Extreme outcomes are often luck + skill; luck doesn't persist
+
+**Formula:**
+```
+Predicted = Mean + r × (Observed - Mean)
+```
+Where `r` = correlation (skill component)
+
+If 50% skill, 50% luck → r = 0.5 → Expect halfway between observed and mean
+
+---
+
+## Integration with Inside View
+
+### The Proper Sequence
+
+**Phase 1: Outside View (Base Rate)**
+1. Identify reference class
+2. Find base rate
+3. Set starting probability = base rate
+
+**Phase 2: Inside View (Adjustment)**
+4. Identify specific evidence
+5. Calculate how much evidence shifts probability
+6. Apply Bayesian update
+
+**Phase 3: Calibration**
+7. Check confidence intervals
+8. Stress test with premortem
+9. Remove biases
+
+**Never skip Phase 1.** Even if you plan to heavily adjust, the base rate is your anchor.
+
+---
+
+## Common Objections (And Rebuttals)
+
+### "But my case really IS different!"
+**Response:** Maybe. But 90% of people say this, and 90% are wrong. Prove it with evidence, not narrative.
+
+### "Base rates are too pessimistic!"
+**Response:** Optimism doesn't change reality. If the base rate is 10%, being optimistic doesn't make it 50%.
+
+### "I have insider knowledge!"
+**Response:** Great! Use Bayesian updating to adjust from the base rate. But start with the base rate.
+
+### "This feels too mechanical!"
+**Response:** Good forecasting should feel mechanical. Intuition is for generating hypotheses, not estimating probabilities.
+
+---
+
+## Practical Takeaways
+
+1. **Always start with base rate** - Non-negotiable
+2. **Resist narrative seduction** - Stories feel true but aren't predictive
+3. **Expect regression to mean** - Extreme outcomes are temporary
+4. **Use inside view as update** - Not replacement for outside view
+5. **Trust frequencies over judgment** - Especially when N is large
+
+---
+
+**Return to:** [Main Skill](../SKILL.md#interactive-menu)