🐢👾scott-josh - amoon.world🌙

- scientist version of [[🐙👾charlie-vikash]], branching from [[🌙simulated collaboration based on observed belief and goal of role model charlie, scott, vikash]] [[Infer Josh and Scott's Mind and Market.pdf]] on 2025 spring [[session5 - frontiers of bayesian decision making]] Below is the revised Markdown table. Emojis are used only in the Tools–Needs row: 🐢 for Scott Stern’s side and 👾 for Josh Tenenbaum’s side. | | Scott Stern’s School | Josh Tenenbaum’s School | |---|---|---| | Focus | • Market: How environments shape the choice/action set • Strategy: How strategy guides entrepreneurs to make experimental choices among multiple paths under uncertainty and resource constraints | • Inference: A hierarchical learning framework to form strong, flexible priors from limited data • Action: How agents use beliefs and desires to decide under uncertainty and resource constraints | | Divergent Origins | Market Dynamics | Inference | | Key Papers | • Incumbency and R&D Incentives (2000) • When Does Start-Up Innovation Spur the Gale of Creative Destruction? (2002) • Product Market and Market for Ideas (2003) • Essays on Metrics in Innovation (2022) | • Bayesian Concept Learning (1999) • Theory-Based Causal Induction (2007) • Learning Grounded Causal Models (2007) • Bayesian theory of mind: Modeling joint belief–desire attribution (2011) • Towards More Human-Like Concept Learning (2014) | | Convergent Pathways | Entrepreneur’s Strategic Choice | Resource-Rational Action | | Key Books/Papers | • Endogenous Appropriability (2018) • Foundations of Entrepreneurial Strategy (2020) • Choosing Technology: An Entrepreneurial Strategy Approach (2021) • Commercializing Contrarian Ideas (2024) • Bayesian Entrepreneurship (2024) • Entrepreneurship: Choice and Strategy (2024) | • ‘One and Done’ Optimal Decisions From Very Few Samples (2014) • Computational Rationality: A Converging Paradigm (2015) • The Naïve Utility Calculus (2016, 2020) • Bayesian Models of Conceptual Development (2020) • From World to Word Models (2023) • Bayesian Models of Cognition (2024) | | Tools–Needs | • 🐢 Need 1: Information-constrained opportunity valuation — A Bayesian model of how entrepreneurs allocate limited information-processing capacity when forming subjective beliefs about opportunities. (vs. behavioral-bias accounts) • 🐢 Need 2: Market-driven belief evolution — A resource-rational framework explaining how entrepreneurs update beliefs from limited market signals without full convergence to consensus. (vs. shared-prior assumptions in game theory) | • 👾 Tool 1: Utility-based frameworks — A language from control and decision theory (e.g., partially observable Markov decision processes) to formalize agents’ decisions assuming infinite computational capacity. • 👾 Tool 2: Resource-rational inference algorithms — Algorithms enabling optimal decisions under computational constraints using limited samples. • 👾 Tool 3: Probabilistic programming platforms — Language, algorithms, and community that integrate utility and resource-rational inference in unified computational architectures. |