👁️🧠🤜BOSA 👁️Belief/Obs-understanding-🧠State-judging-🤜Action

[hyunjimoon]

The image illustrates a framework for analyzing investor decision-making through both discrete and continuous perspectives, with a focus on how investors understand and judge patent value differently. The framework breaks down into three key components: representation, perspective-taking, and algorithmic approaches. In the representation section, it shows how investors exhibit heterogeneity in both their understanding (how they interpret patents as signals of execution ability or idea quality) and their judging (how they value execution versus ideas in venture success). The framework then demonstrates two distinct perspectives: the society's perspective on matching (showing how different types of investors - "hate" or "love" of patents - interact with different types of ventures) and the founder's perspective on sampling (showing how founders can optimize their actions based on understanding investor types). The continuous decision model incorporates probabilistic reasoning through conditional probabilities p(state|observation) and p(action|state), allowing for more nuanced modeling of the decision process compared to the discrete model. This comprehensive framework helps explain why different investors might make different decisions when presented with the same patent information, and how founders can strategically navigate this heterogeneity.

scott and jeff

Scott17 Endogenous Appropriability.pdf shows that entrepreneurs must choose between two main strategies: control (delaying market entry to establish IP protection) versus execution (entering quickly to gain first-mover advantage). Rather than treating these as independent options, he demonstrates they are strategic substitutes and this choice interacts with other decisions like market segment selection and product development approach. The key insight is that the appropriability regime isn't just given by the environment but is actively shaped by entrepreneurs' strategic choices.

Jeff14 endogenous choice RBV.pdf
provides the methodological tools to properly test these strategic choices empirically. He shows that traditional statistical methods fail when studying such choices because managers make decisions with some knowledge of expected returns, and these returns vary across firms. His proposed Bayesian structural model, which directly incorporates the manager's decision process, offers a rigorous way to study how entrepreneurs actually choose between control and execution strategies while accounting for their knowledge and heterogeneous outcomes. Together, these papers provide both the theoretical framework and empirical tools needed to study how entrepreneurs make appropriability strategy choices in practice.

MoonZhang24_Endogenizing Quality Evaluation.pdf makes a key contribution by bridging Scott Stern's focus on strategic choice and Jeff Dotson's emphasis on endogenous evaluation through the lens of AI-augmented quality assessment. Specifically, the paper introduces a framework where the perception gap between internal actors (entrepreneurs) and external evaluators (VCs, mentors) is modeled as both a function of their optimism/conservatism biases (building on Dotson's structural endogeneity) and their different frames of reference (extending Stern's strategic choice model). By introducing an AI-augmented function d_aug(·) that helps narrow this perception gap while preserving the benefits of diverse perspectives, the paper offers a practical mechanism for how entrepreneurs and investors can make more informed strategic choices about quality evaluation in radical innovations. This synthesis demonstrates how AI can help resolve the fundamental tension identified in both papers - between an entrepreneur's need to make strategic choices under uncertainty and investors' need to properly evaluate those choices given endogenous selection effects.

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using analyzing endogenous appropriability and choice cld ,

[hyunjimoon]

preparing inquiry to Andrew:

#utility-belief-action set-prior (UBAP) framework

1. Angie's utility is monotone function of the strength of argument: "prior distribution as action (decision) oriented encoding of belief"

2. Angie's belief is:

• "distinguish between two phases of the iterative simulation. First, there is the initial mixing of the sequences, moving from their starting pointsto the target distribution.Second,the simulations move around within the target distribution." -- Parameterization and Bayesian Modeling

• "probabilities, and decisions, do not take place in a vacuum....Uncertainty in π (in O’Hagan’s terms, “epistemic uncertainty”) necessarily maps to potential information we could learn that would tell us something about π. So in this larger, potentially hierarchical, context, Bayesian inference can distinguish between aleatory uncertainty (randomness) and epistemic uncertainty (ignorance)." -- The Boxer, the Wrestler, and the Coin Flip: A Paradox of Robust Bayesian Inference and Belief Functions

3. Given 1,2, what are possible set of Angie's action (from which to compare utility)?

4. Given 1,2,3 what is Angie's prior distribution?

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useful answers from Andrew-Zizek dialogue gpt

1. 👀In Bayesian terms, if we “look awry,” we might discover that behind “epistemic uncertainty” lies not just insufficient knowledge but also structural illusions or repressed assumptions.

Initial mixing → “burn-in” or “convergence” phase of iterative simulation.
Target distribution → the stable distribution we want to sample from (often the “posterior” in Bayesian contexts).
Aleatory uncertainty (irreducible randomness) vs. epistemic uncertainty (ignorance that can be reduced with information).
Together, these remarks highlight that Angie is aware that beliefs get iteratively refined. She distinguishes situations where (a) the system hasn’t converged yet, and (b) the system has converged to a distribution from which she can keep sampling.