• What is a Forecast Pre-Mortem?
• When to Use This Skill
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• Defeats overconfidence by forcing you to imagine failure
• Identifies specific failure modes you hadn't considered
• Transforms vague doubt into concrete risk variables
• Widens confidence intervals appropriately
• Surfaces "unknown unknowns"

• High confidence (>80% or <20%) - Most likely to be overconfident
• Feeling certain - Certainty is a red flag in forecasting
• Prediction is important - Stakes are high, need robustness
• After inside view analysis - Used specific details, might have missed big picture
• Before finalizing forecast - Last check before committing

• Confidence already low (~50%) - You're already uncertain
• Trivial low-stakes prediction - Not worth the time
• Pure base rate forecasting - Premortem is for inside view adjustments

1. What are you predicting?
2. What's your current probability?
3. What's your confidence interval?

• "The headlines that led to this were..."
• "The first sign of trouble was when..."
• "In retrospect, we should have known because..."
• "The critical mistake was..."

• Internal friction: Team burned out, co-founders fought, execution failed
• External shocks: Regulation changed, competitor launched, market shifted
• Structural flaws: Unit economics didn't work, market too small, tech didn't scale
• Black swans: Pandemic, war, financial crisis, unexpected disruption

• How easy was it to write the failure narrative?
  • Very easy → Drop confidence by 15-30%
  • Very hard, felt absurd → Confidence was appropriate
• How many plausible failure modes did you identify?
  • 5+ modes each >5% likely → Too much uncertainty for high confidence
  • 1-2 modes, low likelihood → Confidence can stay high
• Did you discover any "unknown unknowns"?
  • Yes, multiple → Widen confidence intervals by 20%
  • No, all known risks → Confidence appropriate

P(failure) = P(mode_1) + P(mode_2) + ... + P(mode_n)

1 - your_current_probability

• "The breakthrough came when..."
• "We were wrong about [assumption] because..."
• "The key enabler was..."
• "In retrospect, we underestimated..."

• Are you anchoring too heavily on current constraints?
• Are you underestimating exponential progress?
• Are you ignoring parallel approaches?
• Are you biased by past failures?

• Skeptic mentioned it
• Even fanatic couldn't dismiss it
• Observer identified it statistically

• Political: Elections, coups, policy changes, geopolitical shifts
• Economic: Recession, inflation, currency crisis, market crash
• Social: Cultural shifts, demographic changes, social movements
• Technological: Breakthrough inventions, disruptions, cyber attacks
• Legal: New regulations, lawsuits, IP challenges, compliance changes
• Environmental: Climate events, pandemics, natural disasters

• "If FDA rejects our drug, probability drops to 5%"
• "If key engineer quits, probability drops to 30%"
• "If competitor launches free tier, probability drops to 20%"
• "If regulation passes, probability drops to 0%"

1. Narrative plausibility - 1 = Failure felt absurd, 5 = Failure felt inevitable
2. Number of failure modes - 1 = Only 1-2 unlikely modes, 5 = 5+ plausible modes
3. Unknown unknowns discovered - 1 = No surprises, all known, 5 = Many blind spots revealed
4. Dragonfly synthesis - 1 = Perspectives diverged completely, 5 = All agreed on failure modes

Width multiplier = 1 + (Score / 20)

• Score = 4/20 → Multiplier = 1.2 → Widen CI by 20%
• Score = 10/20 → Multiplier = 1.5 → Widen CI by 50%
• Score = 16/20 → Multiplier = 1.8 → Widen CI by 80%

1. Assume failure is certain - Don't debate whether, debate why
2. Be specific - Vague risks don't help; concrete mechanisms do
3. Use multiple perspectives - Skeptic, fanatic, observer
4. Quantify failure modes - Estimate probability of each
5. Set kill criteria - Know what would change your mind
6. Monitor signposts - Track early warning signals
7. Widen CIs - If premortem was too easy, you're overconfident

Assume your prediction has failed, write the history of how, and use that to identify blind spots and adjust confidence.

• Before: Use after inside view analysis (you need something to stress-test)
• After: Use

  scout-mindset-bias-check

  to validate adjustments
• Companion: Works with

  bayesian-reasoning-calibration

  for quantitative updates
• Feeds into: Monitoring systems and adaptive forecasting

• premortem-principles.md - Theory and research
• backcasting-method.md - Temporal reasoning process
• failure-mode-taxonomy.md - Comprehensive failure categories