jkdkr2439/startup-risk-assessment-framework

# Startup Risk Assessment Framework

**A Mathematical Framework for Identifying and Mitigating Startup Failure Risks**

**Author:** Kevin T. Nguyen (jkdkr2439@gmail.com)

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## ⚠️ IMPORTANT

This is a **THEORETICAL FRAMEWORK ONLY** - not validated, no code, no data.

**If it works for you, say my name, bitches.**

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## 🎯 What This Is

A mathematical model that challenges the binary "10% success = 90% failure" thinking.

### The Real Distribution:

Success: ~11% (ALL factors must align) Early Failure: ~52% (ANY critical risk triggers it) Zombie Zone: ~37% (alive but never scales)

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## 📐 Core Math

**Success** (Series System):

P(success) = P(C₁) × P(C₂) × ... × P(Cₙ)

**Failure** (Parallel System):

P(failure) = 1 - ∏(1 - P(Rᵢ))

**Example:**
- Market fit (0.6) × Team (0.7) × Funding (0.8) × Timing (0.5) × Competition (0.65) = **10.9% success**
- Product wrong (0.25) OR Team breakdown (0.15) OR Cash crisis (0.20) OR Regulatory (0.10) OR Burnout (0.18) = **52.3% failure**

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## 🧠 Key Insight

**Reducing failure risk from 75% → 30% is more valuable than increasing success from 10% → 15%**

Focus on eliminating failure modes, not optimizing for success.

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## 📄 Full Theory

See attached PDF: `startup_probability_theory.pdf`

Based on:
- Reliability Engineering (series/parallel systems)
- Fault Tree Analysis (at-least-one-failure models)
- Survival Analysis (multi-state outcomes)
- Empirical startup data (CBInsights, Startup Genome)

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## 🎯 Primary Use Case

**NOT** for predicting winners.

**FOR** identifying your top 3 risks and focusing 80% effort on mitigating them before scaling.

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## 📬 Contact

Kevin T. Nguyen  
jkdkr2439@gmail.com

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**Status:** Theoretical framework awaiting community validation and implementation.

**License:** MIT - Do whatever you want. Just remember who created it when it prints money.