# 🐙calib(📝2) - Committee Expertise Integration Guide ## Committee Expertise Map ### 👾 **Alert (Scott Stern)**: Economic Theory & Entrepreneurial Strategy - **Core expertise**: Entrepreneurial alertness to market opportunities; endogenous uncertainty creation through promises; Bayesian equilibrium with heterogeneous beliefs; real options perspective on promise-making - **Paper refinement**: Position BMC within innovation economics; connect to entrepreneurial choice literature; strengthen game-theoretic foundations ### 🐢 **Dig (Moshe Ben-Akiva)**: Choice Modeling & Structural Decomposition - **Core expertise**: Decomposing promise decisions into structural components; discrete choice between promise levels; latent class models for entrepreneur types; threshold response functions P(Sell|φ) - **Paper refinement**: Formalize response functions; add discrete choice framework; incorporate heterogeneity modeling ### 🐅 **Gen (Vikash Mansinghka)**: Probabilistic Programming & AI - **Core expertise**: Generative world models for business scenarios; probabilistic programming implementation; ADEV optimization for promise calibration; human-AI collaboration in entrepreneurial reasoning - **Paper refinement**: Computational implementation; simulation methodology; AI-assisted decision support framing ### 🐙 **Calib (Charlie Fine)**: Operations Management & Dynamic Systems - **Core expertise**: Dynamic promise calibration across venture phases; operational timing optimization; clock speed matching between promise evolution and capability development; processification of entrepreneurial learning - **Paper refinement**: Operational dynamics; industry clock speed analysis; scaling framework ## Integration Strategy ### 1. **Introduction Enhancement** - Scott: Frame as entrepreneurial choice under endogenous uncertainty - Charlie: Connect to operations management literature on timing - Moshe: Position as discrete choice problem with structural parameters - Vikash: Emphasize computational tractability through conjugacy ### 2. **Model Development** - Moshe: Formalize P(Sell|φ), P(Deliver|φ) using random utility - Scott: Show how Beta(a,b) creates Bayesian equilibrium - Vikash: Demonstrate ADEV optimization for (a,b) selection - Charlie: Link to operational capability development ### 3. **Managerial Implications** - Charlie: Clock speed-dependent calibration strategies - Scott: Real options value of promise flexibility - Moshe: Segmentation by entrepreneur types - Vikash: AI tools for promise optimization ### 4. **Citation Strategy** Each committee member should see their work reflected: - Stern: Cite entrepreneurial strategy papers - Ben-Akiva: Reference discrete choice modeling - Mansinghka: Include probabilistic programming literature - Fine: Incorporate operations clock speed research ## Revision Checklist ### For Scott (Economic rigor): - [ ] Clarify welfare implications of promise equilibria - [ ] Add comparative statics on (a,b) parameters - [ ] Connect to real options valuation ### For Moshe (Choice structure): - [ ] Formalize utility functions for each outcome - [ ] Add heterogeneity in response functions - [ ] Include estimation discussion ### For Vikash (Computational): - [ ] Provide Gen/Stan implementation sketch - [ ] Show computational advantages of conjugacy - [ ] Discuss AI-human collaboration potential ### For Charlie (Operational): - [ ] Map (a,b) evolution across venture lifecycle - [ ] Connect to supply chain coordination - [ ] Industry-specific calibration examples ## Key Questions to Address 1. **Scott**: How does promise prior selection create market equilibria? 2. **Moshe**: What drives heterogeneity in optimal (a,b) across entrepreneurs? 3. **Vikash**: How can AI systems learn optimal promise calibration? 4. **Charlie**: How does industry clock speed affect recalibration frequency?