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