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skills/forecast-premortem/resources/backcasting-method.md

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+# Backcasting Method
+
+## Temporal Reasoning from Future to Present
+
+**Backcasting** is the practice of starting from a future state and working backward to identify the path that led there.
+
+**Contrast with forecasting:**
+- **Forecasting:** Present → Future (What will happen?)
+- **Backcasting:** Future → Present (How did this happen?)
+
+---
+
+## The Structured Backcasting Process
+
+### Phase 1: Define the Future State
+
+**Step 1.1: Set the resolution date**
+- When will you know if the prediction came true?
+- Be specific: "December 31, 2025"
+
+**Step 1.2: State the outcome as a certainty**
+- Don't say "might fail" or "probably fails"
+- Say "HAS failed" or "DID fail"
+- Use past tense
+
+**Step 1.3: Emotional calibration**
+- How surprising is this outcome?
+  - Shocking → You were very overconfident
+  - Expected → Appropriate confidence
+  - Inevitable → You were underconfident
+
+---
+
+### Phase 2: Construct the Timeline
+
+**Step 2.1: Work backward in time chunks**
+
+Start at resolution date, work backward in intervals:
+
+**For 2-year prediction:**
+- Resolution date (final failure)
+- 6 months before (late-stage warning)
+- 1 year before (mid-stage problems)
+- 18 months before (early signs)
+- Start date (initial conditions)
+
+**For 6-month prediction:**
+- Resolution date
+- 1 month before
+- 3 months before
+- Start date
+
+---
+
+**Step 2.2: Fill in each time chunk**
+
+For each period, ask:
+- What was happening at this time?
+- What decisions were made?
+- What external events occurred?
+- What warning signs appeared?
+
+**Template:**
+```
+[Date]: [Event that occurred]
+Effect: [How this contributed to failure]
+Warning sign: [What would have indicated this was coming]
+```
+
+---
+
+### Phase 3: Identify Causal Chains
+
+**Step 3.1: Map the causal structure**
+
+```
+Initial condition → Trigger event → Cascade → Failure
+```
+
+**Example:**
+```
+Team overworked → Key engineer quit → Lost 3 months → Missed deadline → Funding fell through → Failure
+```
+
+---
+
+**Step 3.2: Classify causes**
+
+| Type | Description | Example |
+|------|-------------|---------|
+| **Necessary** | Without this, failure wouldn't happen | Regulatory ban |
+| **Sufficient** | This alone causes failure | Founder death |
+| **Contributing** | Makes failure more likely | Market downturn |
+| **Catalytic** | Speeds up inevitable failure | Competitor launch |
+
+---
+
+**Step 3.3: Find the "brittle point"**
+
+**Question:** Which single event, if prevented, would have avoided failure?
+
+This is your **critical dependency** and highest-priority monitoring target.
+
+---
+
+### Phase 4: Narrative Construction
+
+**Step 4.1: Write the headlines**
+
+Imagine you're a journalist covering this failure. What headlines mark the timeline?
+
+**Example:**
+- "Startup X raises $10M Series A" (12 months before)
+- "Startup X faces regulatory scrutiny" (9 months before)
+- "Key executive departs Startup X" (6 months before)
+- "Startup X misses Q3 targets" (3 months before)
+- "Startup X shuts down, cites regulatory pressure" (resolution)
+
+---
+
+**Step 4.2: Write the obituary**
+
+"Startup X failed because..."
+
+Complete this sentence with a single, clear causal narrative. Force yourself to be concise.
+
+**Good:**
+"Startup X failed because regulatory uncertainty froze customer adoption, leading to missed revenue targets and inability to raise Series B."
+
+**Bad (too vague):**
+"Startup X failed because of various challenges."
+
+---
+
+**Step 4.3: The insider vs outsider narrative**
+
+**Insider view:** What would the founders say?
+- "We underestimated regulatory risk"
+- "We hired too slowly"
+- "We ran out of runway"
+
+**Outsider view:** What would analysts say?
+- "82% of startups in this space fail due to regulation"
+- "Classic execution failure"
+- "Unit economics never made sense"
+
+**Compare:** Does your insider narrative match outsider base rates?
+
+---
+
+## Narrative vs Quantitative Backcasting
+
+### Narrative Backcasting
+
+**Strengths:**
+- Rich, detailed stories
+- Reveals unknown unknowns
+- Good for complex systems
+
+**Weaknesses:**
+- Subject to narrative fallacy
+- Can feel too "real" and bias you
+- Hard to quantify
+
+**Use when:**
+- Complex, multi-causal failures
+- Human/organizational factors dominate
+- Need to surface blind spots
+
+---
+
+### Quantitative Backcasting
+
+**Strengths:**
+- Precise probability estimates
+- Aggregates multiple failure modes
+- Less subject to bias
+
+**Weaknesses:**
+- Requires data
+- Can miss qualitative factors
+- May feel mechanical
+
+**Use when:**
+- Statistical models exist
+- Multiple independent failure modes
+- Need to calculate confidence intervals
+
+---
+
+## Advanced Technique: Multiple Backcast Paths
+
+### Generate 3-5 Different Failure Narratives
+
+Instead of one story, create multiple:
+
+**Path 1: Internal Execution Failure**
+- Team burned out
+- Product quality suffered
+- Customers churned
+- Revenue missed
+- Funding dried up
+
+**Path 2: External Market Shift**
+- Competitor launched free tier
+- Market commoditized
+- Margins compressed
+- Unit economics broke
+- Shutdown
+
+**Path 3: Regulatory Kill**
+- New law passed
+- Business model illegal
+- Forced shutdown
+
+**Path 4: Black Swan**
+- Pandemic
+- Supply chain collapse
+- Force majeure
+
+---
+
+### Aggregate the Paths
+
+**Calculate probability for each path:**
+- Path 1 (Internal): 40%
+- Path 2 (Market): 30%
+- Path 3 (Regulatory): 20%
+- Path 4 (Black Swan): 10%
+
+**Total failure probability:** 100% (since we assumed failure)
+
+**Insight:** But in reality, your prediction gives 25% failure. This means you're underestimating by 75 percentage points, OR these paths are not independent.
+
+**Adjustment:**
+If paths are partially overlapping (e.g., internal failure AND market shift), use:
+```
+P(A or B) = P(A) + P(B) - P(A and B)
+```
+
+---
+
+## Temporal Reasoning Techniques
+
+### The "Newspaper Test"
+
+**Method:**
+For each time period, imagine you're reading a newspaper from that date.
+
+**What headlines would you see?**
+- Macro news (economy, politics, technology)
+- Industry news (competitors, regulations, trends)
+- Company news (your specific case)
+
+**This forces you to think about:**
+- External context, not just internal execution
+- Leading indicators, not just lagging outcomes
+
+---
+
+### The "Retrospective Interview"
+
+**Method:**
+Imagine you're interviewing someone 1 year after failure.
+
+**Questions:**
+- "Looking back, when did you first know this was in trouble?"
+- "What was the moment of no return?"
+- "If you could go back, what would you change?"
+- "What signs did you ignore?"
+
+**This reveals:**
+- Early warning signals you should monitor
+- Critical decision points
+- Hindsight that can become foresight
+
+---
+
+### The "Parallel Universe" Technique
+
+**Method:**
+Create two timelines:
+
+**Timeline A: Success**
+What had to happen for success?
+
+**Timeline B: Failure**
+What happened instead?
+
+**Divergence point:**
+Where do the timelines split? That's your critical uncertainty.
+
+---
+
+## Common Backcasting Mistakes
+
+### Mistake 1: Being Too Vague
+
+**Bad:** "Things went wrong and it failed."
+**Good:** "Q3 2024: Competitor X launched free tier. Q4 2024: We lost 30% of customers. Q1 2025: Revenue dropped below runway. Q2 2025: Failed to raise Series B. Q3 2025: Shutdown."
+
+**Fix:** Force yourself to name specific events and dates.
+
+---
+
+### Mistake 2: Only Internal Causes
+
+**Bad:** "We executed poorly."
+**Good:** "We executed poorly AND market shifted AND regulation changed."
+
+**Fix:** Use PESTLE framework to ensure external factors are considered.
+
+---
+
+### Mistake 3: Hindsight Bias
+
+**Bad:** "It was always obvious this would fail."
+**Good:** "In retrospect, these warning signs were present, but at the time they were ambiguous."
+
+**Fix:** Acknowledge that foresight ≠ hindsight. Don't pretend everything was obvious.
+
+---
+
+### Mistake 4: Single-Cause Narratives
+
+**Bad:** "Failed because of regulation."
+**Good:** "Regulation was necessary but not sufficient. Also needed internal execution failure and market downturn to actually fail."
+
+**Fix:** Multi-causal explanations are almost always more accurate.
+
+---
+
+## Integration with Forecasting
+
+### How Backcasting Improves Forecasts
+
+**Before Backcasting:**
+- Forecast: 80% success
+- Reasoning: Strong team, good market, solid plan
+- Confidence interval: 70-90%
+
+**After Backcasting:**
+- Identified failure modes: Regulatory (20%), Execution (15%), Market (10%), Black Swan (5%)
+- Total failure probability from backcasting: 50%
+- **Realized:** Current 80% is too high
+- **Adjusted forecast:** 60% success
+- **Adjusted CI:** 45-75% (wider, reflecting uncertainty)
+
+---
+
+## Practical Workflow
+
+### Quick Backcast (15 minutes)
+
+1. **State outcome:** "It failed."
+2. **One-sentence cause:** "Failed because..."
+3. **Three key events:** Timeline points
+4. **Probability check:** Does failure narrative feel >20% likely?
+5. **Adjust:** If yes, lower confidence.
+
+---
+
+### Rigorous Backcast (60 minutes)
+
+1. Define future state and resolution date
+2. Create timeline working backward in chunks
+3. Write detailed narrative for each period
+4. Identify causal chains (necessary, sufficient, contributing)
+5. Generate 3-5 alternative failure paths
+6. Estimate probability of each path
+7. Aggregate and compare to current forecast
+8. Adjust probability and confidence intervals
+9. Set monitoring signposts
+10. Document assumptions
+
+---
+
+**Return to:** [Main Skill](../SKILL.md#interactive-menu)