# Bayesian Evolution Literature Classification (Nanda & Lo Perspective) ## Based on πŸ—£οΈbayes_evol Framework - Finance/Risk Lens ### Core Framework Applied Through Finance - **Double Reparameterization**: Success probability β†’ Promise level (Ο†) β†’ Aspiration (ΞΌ,Ο„) - **Risk-Return Trade-off**: How promise level Ο† affects funding probability - **Information Asymmetry**: Gap between founder's Ο„ and investor's perception - **Portfolio Theory**: Diversification across different (n,Ο„) coordinates --- ## πŸ™ Ocean Food (ν•΄μ‹λŸ‰) Literature - Finance Perspective | Paper | Core Concept | 🟒 AGREE | πŸ”΄ DISAGREE | πŸ’° Finance Application | |-------|--------------|----------|-------------|------------------------| | **[[2세련지/πŸ“œpaper/πŸ™/ν•΄μ‹λŸ‰/πŸ“œπŸ™_yoo21_theorize(lean, operations)]]** | Build-test-learn reduces information asymmetry | Each iteration provides signals updating investor beliefs | - | Cost of capital changes as learning progresses | | **[[2세련지/πŸ“œpaper/πŸ™/ν•΄μ‹λŸ‰/πŸ“œπŸ™_corbett07_observe(entrepreneurs, newsvendor-behavior)]]** | Risk-seeking in loss domain explains unfavorable term acceptance | Prospect theory explains "irrational" financing decisions | - | High Ο„ when facing bankruptcy, low Ο„ when flush | | **[[πŸ“œπŸ™_Alvarez_Porac20_imagination_indetermincy_managerial_choice)]]** | Fundamental uncertainty needs new financial instruments | Convertible notes, SAFEs emerge as uncertainty responses | - | Instruments allow varying Ο„ over time as uncertainty resolves | | **[[2세련지/πŸ“œpaper/πŸ™/ν•΄μ‹λŸ‰/πŸ“œπŸ™_phanchambers18_integrate(entrepreneurship, operations)]]** | Unknown unknowns as purely exogenous | - | Markets price even "unknown unknowns" through volatility premiums | Founders actively manage Ο„ to optimize cost of capital | | **[[πŸ“œπŸ™_sterman00_model(business, dynamics)]]** | Deterministic feedback loops | - | Can't capture discrete financing events, needs stochastic shocks | Models must incorporate funding rounds, not continuous flows | | **[[πŸ“œπŸ™_terwiesch09_design(innovation, tournaments)]]** | Tournament model for innovation | - | Ignores winner's curse and adverse selection | Ο„ parameter captures self-selection into competitions | | **[[πŸ“œπŸ™_packard17_observe(opportunities, beliefs)]]** | Transform unmeasurable uncertainty | Staging investments allows sequential revelation | - | Each funding round recalibrates both n and Ο„ | | **[[πŸ“œπŸ™_yarkoni24_integrate(explanation, prediction)]]** | Explanation vs Prediction framework | Diversify across explanation-strong and prediction-strong ventures | - | High Ο„ ventures need different risk metrics than high n | --- ## πŸ… Prairie Food (μ΄ˆμ›μ‹λŸ‰) Literature - Finance Perspective | Paper | Core Concept | 🟒 AGREE | πŸ”΄ DISAGREE | πŸ’° Finance Application | |-------|--------------|----------|-------------|------------------------| | **[[πŸ“œπŸ…_kerr14_systematize(experimentation, entrepreneurship)]]** | Entrepreneurship = experimentation with unknowable probabilities | **Strongly agree**: Captures venture uncertainty perfectly | - | Each funding round = experiment with capital at risk | | **[[πŸ“œπŸ…_granovetter78_model(collective-behavior, thresholds)]]** | Individual thresholds β†’ collective outcomes | Threshold models explain herding behavior | Too deterministic for volatile markets | Explains funding cascades and market crashes | | **[[πŸ“œπŸ‘Ύ_bolton24_moral_hazard]]** | Entrepreneurs design uninformative experiments | **Perfect fit**: Explains adverse selection in funding | - | High Ο„ = manipulating investor signals strategically | | **[[πŸ“œπŸ…_loch02_optimize(portfolio, selection)]]** | Marginal analysis for portfolio optimization | Portfolio thinking essential for VCs | Misses active uncertainty management by founders | VCs must optimize across (n,Ο„) space | | **[[πŸ“œπŸ…_kavadias03_sequence(projects, optimization)]]** | cΞΌ rule for project sequencing | Sequencing matters for capital efficiency | Too rigid, assumes fixed parameters | Founders manipulate perceived urgency via Ο† | | **[[πŸ“œπŸ…_dada07_diversify(sourcing, suppliers)]]** | Diversification for risk management | Standard portfolio theory applies | Sometimes concentration optimal | High Ο„ justified when C > diversification benefit | | **[[πŸ“œhume_an_enquiry_concerning_human_understanding]]** | No necessary causation, instinct over reason | Philosophical foundation for market uncertainty | - | Justifies why markets price "unknown unknowns" | --- ## Nanda-Lo Specific Financial Mechanisms ### Capital Market Dynamics by Stage | Stage | Optimal Ο„ | Optimal n | Investor Type | Key Trade-off | |-------|-----------|-----------|---------------|---------------| | **Seed** | High | High | Angels | Protect idea vs attract capital | | **Series A** | Medium | Medium | Early VCs | Signal quality vs maintain flexibility | | **Growth** | Low | Low | Late VCs | Transparency vs competitive advantage | | **IPO** | Ο„β†’0 | Low | Public markets | Full disclosure requirement | ### Financial Interpretation of Model Parameters | Parameter | Financial Meaning | Market Mechanism | Example | |-----------|------------------|------------------|---------| | **Ο† (promise level)** | Implicit valuation claim | Term sheets, pitch decks | "We'll be a $1B company" | | **Ο„ (concentration)** | Information disclosure strategy | Due diligence resistance | Refusing certain data requests | | **n (complexity)** | Systematic/market risk | Beta, volatility measures | Industry-specific challenges | | **C (digestion cost)** | Due diligence expense | Legal, accounting fees | $500K Series A legal costs | | **Learning trap (Ο„β†’βˆž)** | Liquidation preference overhang | Down rounds, washouts | WeWork's valuation collapse | ### Portfolio Construction Strategies | Strategy | (n,Ο„) Coordinates | Expected Return | Risk Profile | Example Fund | |----------|-------------------|-----------------|--------------|--------------| | **Spray & Pray** | High n, Low Ο„ | Low | Diversified | 500 Startups | | **Conviction Bets** | Low n, High Ο„ | High | Concentrated | Founders Fund | | **Index Approach** | Medium n, Ο„β†’0 | Market | Systematic only | AngelList Syndicates | | **Smart Money** | Variable n, Optimal Ο„* | Superior | Actively managed | Sequoia Capital | --- ## Key Financial Insights 1. **Information Asymmetry**: Ο„ directly measures degree of strategic information withholding 2. **Staging Logic**: Each round recalibrates both n and Ο„ based on revealed information 3. **Valuation Premium**: High Ο„ commands premium from sophisticated investors, discount from others 4. **Exit Strategy**: IPO requires Ο„β†’0 (full transparency); M&A allows maintaining some Ο„ 5. **Market Cycles**: Bull markets reduce C β†’ encourage Ο„β†’0; Bear markets increase C β†’ justify higher Ο„ 6. **Adverse Selection**: Bolton's uninformative experiments = our high Ο„ manipulation ### Unique Finance Contribution - **Capital Structure as Ο„ Choice**: Debt (low Ο„) vs Equity (high Ο„) vs Convertibles (adaptive Ο„) - **Due Diligence as C**: Explains why some deals close quickly (low C) vs drag on (high C) - **Down Rounds as Learning Traps**: When Ο„ was too high relative to actual performance - **SAFE Notes**: Institutional innovation allowing Ο„ flexibility before pricing --- ## πŸŒ™ Financial Reframing Style Your model gains power through financial lens: - Promise level Ο† becomes implicit valuation claim - Digestion cost C becomes transaction costs - Learning trap becomes preference stack problems - Ο„ optimization becomes disclosure strategy