- [[🗄️(📝🪢🔴)]] # toc | this file | | 🗂️five files | 🗄️three tables | 🖼️two figures | llm instructions | first sentence table (paragraphs) | | --------------------- | -------------------------------- | ------------- | --------------- | -------------- | ---------------- | --------------------------------- | | [[# 1. Introduction]] | | | | | | 13 | | | [[## sec.1.1 literature review]] | | | | | | | | [[## sec.1.2 contribution]] | | | | | | # pcs |**Role**|**Cluster**|**Key Papers (placeholders)**|**Evangelist Papers (placeholders)**| |---|---|---|---| |**Problem Validation**|**P – Single Stakeholder = Low-Bar Experiment Trap**|[[📜Gans23_VisDisruption]]|Agree: [[📜Arora24_BayesianEntrepreneurship]] - _optimistic entrepreneurs favor low-bar experiments_| |**Cause-Nature**|**CN – Entrepreneurs Overconfident in Single Validation**|[[📜Arora24_UserPerspective]]|Agree: [[📜Modrak22_SBCParameterOnly]] - _single test quantities miss systematic failures_| |**Cause-Strategy**|**CS – Missing High-Bar Experiment Design**|[[📜Gans23_ExtremeExperiments]]|Agree: [[📜Camuffo24_ScientificEntrepreneurs]] - _training improves experiment choice_| |**Cause-Operations**|**CO – False Positive vs False Negative Trade-offs**|[[📜Talts20_SBCCalibration]]|Agree: [[📜Cook06_PosteriorQuantiles]] - _computational validation requires multiple tests_| |**Strategy-Solution Frontier**|**SS – Dual Acceptance as High-Bar Validation**|Agree: [[📜STRAP_DualAcceptance]]|Agree: [[📜InformationDesign_BergemannMorris]] - _skeptical audiences prefer high-bar experiments_| |**Operations-Solution Frontier**|**SO – Validation Completeness via Multi-Stakeholder Testing**|Agree: [[📜Theorem6_DensityRatio]]|Agree: [[📜Yu20_AcceleratorQuitRates]] - _systematic validation increases appropriate quit rates_| ## Key Theoretical Integration: **P-Cluster: Low-Bar Experiment Problem (Gans + Arora)** - **Gans**: Optimistic entrepreneurs choose low-bar experiments {λ₁, λ₀} = {1, Λ-1} - "easy to pass" - **Arora**: "Entrepreneurs are more optimistic... they ought to favor low-bar experiments" - **Connection**: Single stakeholder acceptance = low-bar experiment with high false positive rates **CN-Cluster: Entrepreneurial Overconfidence (Arora + Modrak)** - **Arora**: "Entrepreneurs are more optimistic about prospects of their venture, for otherwise they would not have started" - **Modrak**: Parameter-only validation misses data-ignoring models - **Connection**: Overconfident entrepreneurs accept single-stakeholder validation too easily **CS-Cluster: High-Bar Experiment Design Need (Gans + Camuffo)** - **Gans**: Skeptical investors prefer high-bar experiments {λ₁, λ₀} = {Λ-1, 1} - "hard to pass" - **Camuffo**: Scientific entrepreneurs trained to form theories have higher quit rates but better performance - **Connection**: Dual stakeholder acceptance = high-bar experiment design **SO-Cluster: Systematic Validation Training (Arora + Yu)** - **Arora**: "Scientific Entrepreneurs that persist outperform the control group entrepreneurs that persist" - **Yu**: "Startups in accelerators are more likely to quit than comparable firms" - **Connection**: Proper validation training increases appropriate abandonment of non-scalable ventures ## Updated Literature Integration: The validation theory now explicitly connects: 1. **Gans' experiment design theory** → Single vs dual stakeholder validation 2. **Arora's Bayesian entrepreneurship** → Optimism bias in validation choice 3. **Modrak's SBC theory** → Multi-dimensional testing completeness 4. **Camuffo's scientific entrepreneurship** → Training effects on validation quality This creates a cohesive theoretical foundation linking information design, Bayesian entrepreneurship, and computational validation to dual stakeholder acceptance theory.