Here's a comprehensive summary table of our paper's structure with key contributions and tables: ## 📊 Paper Structure: Optimal Ignorance Level Framework |Section|Content|Core Message|Tables/Tools| |---|---|---|---| |**1. Introduction**|||| |1.1|Tesla earned precision, Better Place assumed it|🚗 Tesla survived through ambiguity; Better Place died defending specificity|| |1.2|Problem: Exogenous P and informativeness|⚠️ Existing theory treats success probability as given, not designed|| |1.3|Solution: τ as endogenous variable|🎯 Founders design learning capacity through precision choice|| |1.4|OIL Formula|📐 τ* = max{0, √(V/4i) - 1}|| |1.5|Why matters for founders|🪤 Precision trap + 🔧 Nail-to-scale toolkit|| |1.6|Why matters for scholars|🔬 Endogenizing P via τ bridges micro-macro|| |1.7|Three contributions|🚙 Endogeneity + 🐣 Formula/toolkit + 👨‍👩‍👧 Sectoral heterogeneity|| |**2. Theory**|||| |2.1|Part I: Endogeneity [Theorists]|🚙 τ → Δ(μ;τ) = μ/(1+τ) → P mechanism|🗄️MT: Terrain vs Map| |2.2|Part II: Formula [Practitioners]|🐣 When V<4i: τ=0; When V≥4i: earn precision|🗄️OT: Operational Tools| |2.3|Part III: Heterogeneity [Investors]|👨‍👩‍👧 R&D (epistemic) vs Consumer (aleatoric)|🗄️UT: Uncertainty Types| |**3. Model**|||| |3.1|M1: Complexity discipline|📉 φ* = 1/(c+1) - complexity constrains promises|| |3.2|M2: Empirical Bayes|📊 μ* = 1/(log c + γ) - calibrate aspiration|| |3.3|M2': OIL as decision variable|🎮 Full model with τ as choice variable|| |**4. Application**|||| |4.1|Tesla case|✅ Low τ → gradual increase → high τ|| |4.2|Better Place case|❌ High τ from start → learning trap|| |4.3|Comparative analysis|📈 3D evolution paths (τ, V, i)|| |**5. Discussion**|||| |5.1|Theoretical synthesis|🔗 Effectuation (τ=0) ↔ Causation (τ→∞)|| |5.2|Practical implications|💼 Stage-gate for R&D, portfolio for consumer|| |5.3|Future research|🔮 Dynamic τ trajectories, industry studies|| ### 🎯 Key Formulas & Concepts |Concept|Formula/Definition|Meaning| |---|---|---| |**Precision (τ)**|Prior concentration|Rigidity of promises| |**Update Capacity**|Δ(μ;τ) = μ/(1+τ)|Learning ability| |**OIL Formula**|τ* = max{0, √(V/4i) - 1}|Optimal precision rule| |**Phase Transition**|V = 4i boundary|Nail → Scale threshold| |**Success Probability**|P = φ(1-φ)^c|Sell × Deliver tension| ### 📑 Three Tables (MOUT) |Table|File Name|Purpose|Target Audience| |---|---|---|---| |🗄️MT|`table_mountain_terrain.tex`|Literature comparison: endogenous vs exogenous|Theorists| |🗄️OT|`table_operational_tool.tex`|V/i management tools by stage|Practitioners| |🗄️UT|`table_uncertainty_types.tex`|Epistemic/aleatoric × R&D/consumer|Investors| This structure shows how precision (τ) threads through the entire paper as the central mechanism connecting founder decisions to venture outcomes.