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