6.6 KiB
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:
- Overweight unique details (availability bias)
- Construct plausible narratives (hindsight bias)
- Underweight statistical patterns (base rate neglect)
- 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
- Focus on success plan: Ignore failure modes
- Best-case scenario bias: Assume things go smoothly
- Neglect of base rates: "Our project is different"
- 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)
- Identify reference class
- Find base rate
- 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
- Always start with base rate - Non-negotiable
- Resist narrative seduction - Stories feel true but aren't predictive
- Expect regression to mean - Extreme outcomes are temporary
- Use inside view as update - Not replacement for outside view
- Trust frequencies over judgment - Especially when N is large
Return to: Main Skill