# Bayesian Evolution Assessment: Andrew & Josh Policy Lens ## 👾 Space Food Literature Review [[09-27|25-09-27]] evol ent x baye ent [[lamarkian]] 인지에서 의지로 (라마르크) VS 의지에서 인지로 (쇼펜하우어) "확률"구조 위에 인지와 의지 기능의 중첩 (본 논문) 1.  1. import from bayesian modeling community: 1. effective sample size (autocorrelation of samples per industry, which shares spirit with process noise in system dynamics \citep{sterman2002system}), 2. mcmc vs smc 2. export to bayesian modeling community: 1. self-imposed uncertainty (alea inside as buffer to neutralize \citep{brinkerink2025negative} organizational entropy increase by being permeable to developing order of one's environment. this may help explain stein's paradox on universal robustness of hierarchical bayesian model. 3. import from evolutionary science community 1. understand evolutionary entrepreneur's behavior using bayesian models. Specifically, venture's somatic adaptability, from operations such as processification and automation, can be understood as founder's "simulation of Lamarckian inheritance creating survival value under multiple stress" \citep{bateson2000steps}). As noted in the modeling section, simulation is a main driving force for venture's quality improvement. 2. size of exaptation's open space can also be parameterized with tau (inversely proportionate), as the wider open space is, the faster future opportunity can adhere to) \citep{gould1982exaptation} which has implication on e.g. organizational space design 3. **sorting and selection are different \citep{vrba1986hierarchical}** 4. export to evolutionary science community 1. rich cultures such as moderna's parallel entrepreneurship and flagship pioneering's "killer experiment", iteration of variation and selection of ideas, logic of restricting probability space (\citep{gans2019foundations, CamuffoGambardellaPignataro2024}, startup lifecycle \citep{}, and progressive precision 2. as venture increase market size and capability in parallel, how does the availably possiblities evolve? To be specific, what could Betterplace do to better balance replicating its success in Denmarket to Tel Aviv, while it automated battery swapping process? Understanding the multiplicative structure of flexibility where successive demand fractionates the set of available possibilities \citep{bateson2000steps} can help us understand flexibility under parallel growth. # evol ent - The Adjacent Possible: Harnessing Functional Excess, Experimentation, and Protoscience as Tool's "ubiquitously available but dormant “functional excess” in the adjacent possible provides the raw material for evolutionary disruptions." - [[lamarkian]] # bayes ent ## Table: Future Research - Bridging Bayesian and Evolutionary Entrepreneurship |Direction|Community|Concept|Application to OIL Framework|Target Scholar Interest| |---|---|---|---|---| |**Import from Bayesian** 🟢||||**Andrew Gelman**| ||Modeling|Effective Sample Size|Industry-specific autocorrelation of samples parallels process noise in system dynamics. Different industries have different τ persistence.|Hierarchical modeling of industry effects| |||MCMC vs SMC|Sequential Monte Carlo better captures venture's path-dependent τ evolution vs MCMC's equilibrium assumption|Dynamic vs static inference| |**Export to Bayesian** 🟢||||| ||Theory|Self-imposed Uncertainty|Ventures create internal aleatory buffer (low τ) to neutralize organizational entropy, explaining Stein's paradox on hierarchical model robustness|Universal shrinkage explanation| |||Endogenous Priors|Founders actively shape their prior precision, not just update it - agency in Bayesian modeling|Agent-based Bayesian models| |**Import from Evolution** 🟣||||**Steven Pinker**| ||Mechanism|Simulated Lamarckian|Venture's somatic adaptability (processification/automation) = founder's "simulation creating survival value under stress" (Bateson)|Cultural evolution of technology| |||Exaptation Space|τ inversely proportional to exaptation openness - wider space → faster opportunity adhesion (Gould)|Innovation through repurposing| |||Sorting ≠ Selection|Vrba's distinction: market sorts ventures by τ trajectory, then selects survivors|Multi-level selection| |**Export to Evolution** 🟣||||| ||Empirics|Killer Experiments|Moderna/Flagship's parallel entrepreneurship = controlled variation-selection iterations within τ constraints|Designed evolution examples| |||Multiplicative Flexibility|As V and i evolve in parallel, successive demands fractionate possibilities (Bateson). Better Place failed this balance.|Flexibility under constraints| ## Integration Opportunities ### 🟢🟣 Joint Framework: "Bayesian Evolution of Ventures" |Synthesis Area|Bayesian Contribution|Evolutionary Contribution|Novel Insight| |---|---|---|---| |**Learning Mechanism**|Posterior updating via Beta-Binomial|Baldwin effect: learned traits guide selection|τ evolution = cultural ratchet| |**Uncertainty Management**|Prior precision as design variable|Bet-hedging in fluctuating environments|Optimal ignorance = adaptive bet-hedging| |**Multi-level Structure**|Hierarchical models (venture within industry)|Group selection (founder-venture-market)|τ operates across levels simultaneously| |**Time Dynamics**|Sequential updating|Evolutionary trajectories|Path-dependent τ* differs from equilibrium τ*| ### Provocative Questions for Both Communities **For Gelman**: "What if agents could choose their prior precision strategically, not just update passively? How does this change hierarchical modeling?" **For Pinker**: "What if cultural evolution in startups follows Bayesian updating rules with designed (not random) variation? Is this still evolution?" **Bridging Question**: "Can we model entrepreneurship as Bayesian agents conducting evolutionary experiments, where τ mediates between individual learning (Bayesian) and population selection (Evolutionary)?" --- ![[bayes_evol(andrew_josh) 2025-09-12-21.svg]] %%[[bayes_evol(andrew_josh) 2025-09-12-21.md|🖋 Edit in Excalidraw]]%% | Paper | Core Concept | 🟢 AGREE | 🔴 DISAGREE | 🔵 Our Bayesian Extension | ⚡️manual | | -------------------------------------------------------------- | ----------------------------------------------------- | -------------------------------------------------------- | --------------------------------- | ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | | **[[📜👾_gans23_choose(entrepreneurship, experimentation)]]** | High-bar vs low-bar experiments based on priors | **Strongly agree**: Experimental design reflects beliefs | - | Maps directly to our τ choice mechanism | | | **[[📜👾_stern24_model(beliefs, experimentation)]]** | Sequential testing of strategies with fixed core idea | **Core alignment**: Iterative updating | Lacks uncertainty choice | Our τ allows strategic opacity during updates | | | **[[📜👾_meehl67_test(theory, method)]]** | Physics vs psychology testing asymmetry | **Philosophical foundation**: Everything correlates | - | Justifies different τ for atom vs bit ventures | | | [[📜👾_tenanbaum11_grow(minds, cognition)]] | Bayesian cognitive development | **Deep resonance**: Learning as inference | Too deterministic | We add founder's agency in learning (τ) | scientific approach of inferring upwards, but exaptation or structure or form created for convenience gaining meaning later cannot be explained. | | **[[📜👾_arrow69_classify(production, knowledge)]]** | Knowledge production classification | Foundation for n parameter | Static categories | We make categories dynamic through PRHC | | | **[[📜👾_nejad22_model(mentorship, accelerators)]]** | Mentorship's direct + screening effects | **Perfect fit**: Dual uncertainty reduction | - | Mentors help optimize both n and τ | | | **[[📜👾_busenitz97_recognize(entrepreneurs, biases)]]** | Entrepreneurial cognitive biases | Biases exist | Not biases but rational τ choices | Reframe "overconfidence" as high prior + low τ | | | **[[📜🐅_mansinghka25_automate(formalization, programming)]]** | Autoformalization of knowledge | Computational Bayesian methods useful | - | Could automate PRHC calibration | | | **[[📜🐅_bhui21_optimize(decisions, resources)]]** | Resource allocation under uncertainty | Standard optimization applies | Missing strategic uncertainty | Add τ to resource allocation models | | | **[[📜🐢_xuan24_plan(instruction, cooperation)]]** | Planning with instruction | - | - | *Needs review* | | | **[[📜_arora25_behavior(users, entrepreneurs)]]** | User behavior modeling | - | - | *Needs review* | | | **[[📜👾_johnston02_caution(startups, scaling)]]** | Cautious scaling approach | - | - | *Needs review* | | | **[[📜👾_peng21_overload(information, decisions)]]** | Information overload in decisions | Supports high digestion cost C | - | Justifies τ→0 under info overload | | | enhancing social science thru automation | | | | | | ## Bayesian Statistical Methods Applied (Andrew's Focus) | Method | Application to Our Model | Implementation | |--------|-------------------------|----------------| | **Prior Predictive Checks** | Test if initial φ distributions are reasonable | Generate synthetic ventures, check outcomes | | **Posterior Predictive Checks** | Validate updated beliefs match observed data | Compare predicted vs actual pivot rates | | **Simulation-Based Calibration** | Ensure inference pipeline recovers true parameters | Simulate founders with known (n,τ), recover via MCMC | | **Hierarchical Modeling** | Industry → Founder → Venture structure | Partial pooling of parameters across levels | | **Model Comparison** | Test PRHC against simpler models | WAIC, LOO-CV for model selection | ## Policy Intervention Points (Josh's Focus) | Stage | Policy Tool | Effect on Parameters | Example | |-------|------------|---------------------|---------| | **Pre-Launch** | Incubators | Calibrate initial φ | Y Combinator advice on MVP | | **Seed** | Grants | Reduce n uncertainty | SBIR validation | | **Growth** | Accelerators | Optimize τ trajectory | Techstars mentorship | | **Scale** | Regulations | Force τ→0 | SEC disclosure requirements | | **Exit** | Public markets | Require full transparency | IPO prospectus | ## Computational Implementation Framework ```python class BayesianEvolution: """Andrew's computational framework""" def __init__(self): self.prior_n = Beta(2, 2) # Nature's uncertainty prior self.prior_tau = Gamma(2, 1) # Founder's concentration prior def reparameterize(self, success_prob): """First reparameterization: P(s) → φ""" return self.promise_level(success_prob) def regularize(self, promise, complexity): """Add nature's constraint""" return promise * (1 - promise)**complexity def hierarchize(self, promise, aspiration, concentration): """Second reparameterization: φ → (μ, τ)""" return Beta(aspiration * concentration, (1 - aspiration) * concentration) def calibrate(self, observed_data): """Update beliefs given data""" return self.posterior_update(observed_data) ``` ## Policy Design Principles | Principle | Mechanism | Our Model's Insight | |-----------|-----------|-------------------| | **Reduce n selectively** | Target high-impact uncertainties | Government can't reduce all n efficiently | | **Allow τ flexibility** | Don't force transparency too early | High τ valuable in early stages | | **Lower C systemically** | Standardize information formats | Reduces barriers to τ adjustment | | **Signal separation** | Different funding sources for different (n,τ) | Angels for high τ, VCs for medium, public for low | ## Key Synthesis Points ### Andrew's Statistical Contributions 1. **Rigorous inference**: MCMC for parameter estimation 2. **Model validation**: Prior/posterior predictive checks 3. **Hierarchical structure**: Industry-founder-venture levels 4. **Causal identification**: Using policy shocks as instruments ### Josh's Policy Applications 1. **Stage-appropriate interventions**: Different tools for different phases 2. **Market failure diagnosis**: High n → under-investment 3. **Institutional design**: Accelerators as (n,τ) optimizers 4. **Regulatory calibration**: Transparency requirements by stage ### Our Bridge Innovation - **Statistical rigor meets policy reality**: Falsifiable predictions about intervention effects - **Computational tractability**: PRHC sequence enables practical implementation - **Dynamic optimization**: τ as strategic variable, not fixed bias - **Heterogeneous effects**: Same policy affects different (n,τ) types differently ## Research Agenda 1. **Empirical**: Measure (n,τ) distributions across industries 2. **Theoretical**: Prove convergence of PRHC sequence 3. **Computational**: Develop efficient τ optimization algorithms 4. **Policy**: Design experiments to test intervention effects ----