# 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)