🐙👾charlie-vikash - amoon.world🌙

[[09-14|25-09-14]] - engineering version of [[🐢👾scott-josh]], branching from [[🌙simulated collaboration based on observed belief and goal of role model charlie, scott, vikash]] | | Charlie Fine’s School | Josh Tenenbaum’s School | | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Focus | • Operations: How ventures build and manage the capabilities to make and deliver things. • Evolution: How these operational systems and strategies must change as a venture grows from idea to maturity. | • Inference: How agents use a hierarchical learning framework to form strong, flexible prior beliefs from limited data. • Action: How agents use beliefs and desires to make decisions under uncertainty and resource constraints. | | Divergent Origins | Dynamic Process Improvement & Quality | Inference | | Key Papers | • Quality Improvement and Learning in Productive Systems (1986) • Dynamic Process Improvement (1988) • Optimal Investment in Product-Flexible Manufacturing Capacity (1990) | • Bayesian Concept Learning (1999) • Theory-Based Causal Induction (2007) • Learning Grounded Causal Models (2007) • Towards More Human-Like Concept Learning (2014) | | Convergent Pathways | Evolutionary Operations & Venture Dynamics | Resource-Rational Action | | Key Books/Papers | • Clockspeed (1998) • Rapid Response Capability in Value-Chain Design (2002) • Managing Operational Capabilities in Startup Companies (2018?) • Operations for Entrepreneurs (2022) | • ‘One and Done’ Optimal Decisions From Very Few Samples (2014) • Computational Rationality (2015) • The Naïve Utility Calculus (2016, 2020) • From World to Word Models (2023) | | Tools–Needs | • 🐢 Need: A framework for Stage‑Contingent Capability Management that guides founders on when and how to shift from flexibility to discipline. • 🐢 Tool: The “Nail–Scale–Sail” framework and the 10 Scaling Tools (e.g., Processification, Professionalization). | • 👾 Tool 1: Utility‑based frameworks. • 👾 Tool 2: Resource‑rational inference algorithms. • 👾 Tool 3: Probabilistic programming platforms. | ----- The Most Relevant Question for Future Research Based on the goal of synthesizing these frameworks, the most relevant question from the handout to include in your paper’s “Future Research” section is the one directed at Charlie Fine concerning the Precision of Probability Judgments. Why this bridges your work: 1) It directly addresses your core theme: the strategic tension between ambiguity and precision, asking how precision in probability judgments evolves. 2) It uses the exact operational stages: framed with “Nail‑it” and “Scale‑it,” linking belief updates to Fine’s real‑world stages. 3) It probes the dynamics your model prescribes: your model specifies how precision (τ) should evolve; this question invites empirical testing of that prescription. 4) It provides a cognitive link: contrasting stable “core knowledge” with flexible “intuitive theories” (Ullman & Tenenbaum, 2020) aligns with the Nail‑to‑Scale transition and motivates integrating Tenenbaum’s learning framework.