forecast-premortem

name	forecast-premortem
description	Use to stress-test predictions by assuming they failed and working backward to identify why. Invoke when confidence is high (>80% or <20%), need to identify tail risks and unknown unknowns, or want to widen overconfident intervals. Use when user mentions premortem, backcasting, what could go wrong, stress test, or black swans.

Forecast Pre-Mortem

Table of Contents

• What is a Forecast Pre-Mortem?
• When to Use This Skill
• Interactive Menu
• Quick Reference
• Resource Files

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What is a Forecast Pre-Mortem?

A forecast pre-mortem is a stress-testing technique where you assume your prediction has already failed and work backward to construct the history of how it failed. This reveals blind spots, tail risks, and overconfidence.

Core Principle: Invert the problem. Don't ask "Will this succeed?" Ask "It has failed - why?"

Why It Matters:

• 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"

Origin: Gary Klein's "premortem" technique, adapted for probabilistic forecasting

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When to Use This Skill

Use this skill when:

• 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

Do NOT use when:

• 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

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Interactive Menu

What would you like to do?

Core Workflows

1. Run a Failure Premortem - Assume prediction failed, explain why 2. Run a Success Premortem - For pessimistic predictions (<20%) 3. Dragonfly Eye Perspective - View failure through multiple lenses 4. Identify Tail Risks - Find black swans and unknown unknowns 5. Adjust Confidence Intervals - Quantify the adjustment 6. Learn the Framework - Deep dive into methodology 7. Exit - Return to main forecasting workflow

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1. Run a Failure Premortem

Let's stress-test your prediction by imagining it has failed.

Failure Premortem Progress:
- [ ] Step 1: State the prediction and current confidence
- [ ] Step 2: Time travel to failure
- [ ] Step 3: Write the history of failure
- [ ] Step 4: Identify concrete failure modes
- [ ] Step 5: Assess plausibility and adjust

Step 1: State the prediction and current confidence

Tell me:

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

Example: "This startup will reach $10M ARR within 2 years" - Probability: 75%, CI: 60-85%

Step 2: Time travel to failure

The Crystal Ball Exercise:

Jump forward to the resolution date. It is now [resolution date]. The event did NOT happen. This is a certainty. Do not argue with it.

How does it feel? Surprising? Expected? Shocking? This emotional response tells you about your true confidence.

Step 3: Write the history of failure

Backcasting Narrative: Starting from the failure point, work backward in time. Write the story of how we got here.

Prompts:

• "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..."

Frameworks to consider:

• 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

See Failure Mode Taxonomy for comprehensive categories.

Step 4: Identify concrete failure modes

Extract specific, actionable failure causes from your narrative.

For each failure mode: (1) What happened, (2) Why it caused failure, (3) How likely it is, (4) Early warning signals

Example:

Failure Mode	Mechanism	Likelihood	Warning Signals
Key engineer quit	Lost technical leadership, delayed product	15%	Declining code commits, complaints
Competitor launched free tier	Destroyed unit economics	20%	Hiring spree, beta leaks
Regulation passed	Made business model illegal	5%	Proposed legislation, lobbying

Step 5: Assess plausibility and adjust

The Plausibility Test:

Ask yourself:

• 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

Quantitative Method: Sum the probabilities of failure modes:

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

If this sum is greater than 1 - your_current_probability, your probability is too high.

Example: Current success: 75% (implied failure: 25%), Sum of failure modes: 40% Conclusion: Underestimating failure risk by 15%, Adjusted: 60% success

Next: Return to menu or document findings

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2. Run a Success Premortem

For pessimistic predictions - assume the unlikely success happened.

Success Premortem Progress:
- [ ] Step 1: State pessimistic prediction (<20%)
- [ ] Step 2: Time travel to success
- [ ] Step 3: Write the history of success
- [ ] Step 4: Identify how you could be wrong
- [ ] Step 5: Assess and adjust upward if needed

Step 1: State pessimistic prediction

Tell me: (1) What low-probability event are you predicting? (2) Why is your confidence so low?

Example: "Fusion energy will be commercialized by 2030" - Probability: 10%, Reasoning: Technical challenges too great

Step 2: Time travel to success

It is now 2030. Fusion energy is commercially available. This happened. It's real. How?

Step 3: Write the history of success

Backcasting the unlikely: What had to happen for this to occur?

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

Step 4: Identify how you could be wrong

Challenge your pessimism:

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

Step 5: Assess and adjust upward if needed

If success narrative was surprisingly plausible, increase probability.

Next: Return to menu

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3. Dragonfly Eye Perspective

View the failure through multiple conflicting perspectives.

The dragonfly has compound eyes that see from many angles simultaneously. We simulate this by adopting radically different viewpoints.

Dragonfly Eye Progress:
- [ ] Step 1: The Skeptic (why this will definitely fail)
- [ ] Step 2: The Fanatic (why failure is impossible)
- [ ] Step 3: The Disinterested Observer (neutral analysis)
- [ ] Step 4: Synthesize perspectives
- [ ] Step 5: Extract robust failure modes

Step 1: The Skeptic

Channel the harshest critic. You are a short-seller, a competitor, a pessimist. Why will this DEFINITELY fail?

Be extreme: Assume worst case, highlight every flaw, no charity, no benefit of doubt

Output: List of failure reasons from skeptical view

Step 2: The Fanatic

Channel the strongest believer. You are the founder's mother, a zealot, an optimist. Why is failure IMPOSSIBLE?

Be extreme: Assume best case, highlight every strength, maximum charity and optimism

Output: List of success reasons from optimistic view

Step 3: The Disinterested Observer

Channel a neutral analyst. You have no stake in the outcome. You're running a simulation, analyzing data dispassionately.

Be analytical: No emotional investment, pure statistical reasoning, reference class thinking

Output: Balanced probability estimate with reasoning

Step 4: Synthesize perspectives

Find the overlap: Which failure modes appeared in ALL THREE perspectives?

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

These are your robust failure modes - the ones most likely to actually happen.

Step 5: Extract robust failure modes

The synthesis:

Failure Mode	Skeptic	Fanatic	Observer	Robust?
Market too small	Definitely	Debatable	Base rate suggests yes	YES
Execution risk	Definitely	No way	50/50	Maybe
Tech won't scale	Definitely	Already solved	Unknown	Investigate

Focus adjustment on the robust failures that survived all perspectives.

Next: Return to menu

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4. Identify Tail Risks

Find the black swans and unknown unknowns.

Tail Risk Identification Progress:
- [ ] Step 1: Define what counts as "tail risk"
- [ ] Step 2: Systematic enumeration
- [ ] Step 3: Impact × Probability matrix
- [ ] Step 4: Set kill criteria
- [ ] Step 5: Monitor signposts

Step 1: Define what counts as "tail risk"

Criteria: Low probability (<5%), High impact (would completely change outcome), Outside normal planning, Often exogenous shocks

Examples: Pandemic, war, financial crisis, regulatory ban, key person death, natural disaster, technological disruption

Step 2: Systematic enumeration

Use the PESTLE framework for comprehensive coverage:

• 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

For each category, ask: "What low-probability event would kill this prediction?"

See Failure Mode Taxonomy for detailed categories.

Step 3: Impact × Probability matrix

Plot your tail risks:

High Impact
│
│  [Pandemic]        [Key Founder Dies]
│
│
│  [Recession]       [Competitor Emerges]
│
└─────────────────────────────────────→ Probability
  Low                              High

Focus on: High impact, even if very low probability

Step 4: Set kill criteria

For each major tail risk, define the "kill criterion":

Format: "If [event X] happens, probability drops to [Y]%"

Examples:

• "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%"

Why this matters: You now have clear indicators to watch

Step 5: Monitor signposts

For each kill criterion, identify early warning signals:

Kill Criterion	Warning Signals	Check Frequency
FDA rejection	Phase 2 trial results, FDA feedback	Monthly
Engineer quit	Code velocity, satisfaction surveys	Weekly
Competitor launch	Hiring spree, beta leaks, patents	Monthly
Regulation	Proposed bills, lobbying, hearings	Quarterly

Setup monitoring: Calendar reminders, news alerts, automated tracking

Next: Return to menu

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5. Adjust Confidence Intervals

Quantify how much the premortem should change your bounds.

Confidence Interval Adjustment Progress:
- [ ] Step 1: State current CI
- [ ] Step 2: Evaluate premortem findings
- [ ] Step 3: Calculate width adjustment
- [ ] Step 4: Set new bounds
- [ ] Step 5: Document reasoning

Step 1: State current CI

Current confidence interval: Lower bound: __%, Upper bound: __%, Width: ___ percentage points

Step 2: Evaluate premortem findings

Score your premortem on these dimensions (1-5 each):

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

Total score: __ / 20

Step 3: Calculate width adjustment

Adjustment formula:

Width multiplier = 1 + (Score / 20)

Examples:

• 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%

Current width: ___ points, Adjusted width: Current × Multiplier = ___ points

Step 4: Set new bounds

Method: Symmetric widening around current estimate

New lower = Current estimate - (Adjusted width / 2)
New upper = Current estimate + (Adjusted width / 2)

Example: Current: 70%, CI: 60-80% (width = 20), Score: 12/20, Multiplier: 1.6, New width: 32, New CI: 54-86%

Step 5: Document reasoning

Record: (1) What failure modes drove the adjustment, (2) Which perspective was most revealing, (3) What unknown unknowns were discovered, (4) What monitoring you'll do going forward

Next: Return to menu

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6. Learn the Framework

Deep dive into the methodology.

Resource Files

📄 Premortem Principles - Why humans are overconfident, hindsight bias and outcome bias, the power of inversion, research on premortem effectiveness

📄 Backcasting Method - Structured backcasting process, temporal reasoning techniques, causal chain construction, narrative vs quantitative backcasting

📄 Failure Mode Taxonomy - Comprehensive failure categories, internal vs external failures, preventable vs unpreventable, PESTLE framework for tail risks, kill criteria templates

Next: Return to menu

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Quick Reference

The Premortem Commandments

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

One-Sentence Summary

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

Integration with Other Skills

• 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

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Resource Files

📁 resources/

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

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