#1. based on below, re-organize # Fake It Till You Approximate It: Simplify to Aspire, Sync to Focus ## Abstract Bayesian entrepreneur's success stems not from innate traits but from how founders translate environmental challenges into adaptive capabilities. I develop a framework where entrepreneurs act as modelers who continuously update their venture designs based on environmental feedback. Two principles emerge: ventures must limit operational complexity to maintain bold aspirations, and must reduce information-integration costs to earn strategic precision. Simplify to aspire, acculturate to concentrate. These principles explain why some ventures adapt while others fail. Tesla succeeded by starting flexible and earning precision through systematic experimentation; Better Place failed by committing to rigid precision despite high complexity. Our model shows that success probability isn't given—it's constructed through deliberate choices about how much to know and when. This transforms entrepreneurship from a game of prediction to one of calibrated adaptation, where the ability to manage uncertainty becomes the core capability. ***Keywords**: Bayesian and Evolutionary foundership, Hierarchical Model, Calibration, Flexibility* *It appears to be a general principle that, whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought. \- E.T. Jaynes.* 🚨todo1: imagine you're 공자 who knows english. translate 견리사의 in english so that its meaning is not lost in translation 🚨🚨todo2: syntax and semantics ## 1\. Introduction Tesla and Better Place shared a vision to electrify the automobile, yet their fates diverged dramatically because they managed promise precision differently. Both promised a sustainable transportation future, but their strategies sat at opposite poles of the confidence spectrum. Better Place committed to a tightly integrated, highly precise system of battery-swap stations—a high-τ promise requiring the entire ecosystem to conform at once. Tesla, by contrast, began with a low-τ promise—a premium sports car for a niche market—and increased precision gradually while learning from the market, expanding both its product line and charging infrastructure. I argue that the capability to dynamically manage promise's 🚨🚨syntax and semantics🚨🚨 explains this fork in outcomes. ### 1.2. Three Meanings of τ In my model, precision τ carries three analytically grounded meanings: 1. Promise precision. High τ denotes a narrow, specific commitment; low τ denotes a broad, flexible commitment. 2. Pseudo–sample size. High τ behaves as if the founder holds substantial prior evidence. 3. Width of open adaptive space. Low τ preserves a set of latent functions not yet specified, maximizing the real option value of exaptation. As organizations mature and face greater information-integration costs and environmental complexity, managing τ dynamically—by deliberately controlling i (information-integration cost) and c (complexity)—becomes a central task. The structural metaphor of DNA helical tension and the strategic metaphor of firebreak width illuminate why flexibility (low τ) and efficiency (high τ) succeed under different conditions: Table 1. Bayesian–Evolutionary Metaphors for the Flexibility–Efficiency Tension | | Low τ (Flexibility) | High τ (Efficiency) | | ----- | ----- | ----- | | DNA tension | Loose | Tight | | Firebreak width | Wide | Narrow | | Best-fit environment | High complexity, high information cost | Low complexity, low information cost | Tightly wound DNA strands (high τ) replicate with high fidelity and are efficient in stable environments, but they generate little variation and thus adapt poorly to shocks. Loosely wound strands (low τ) admit variation, enabling pivots essential for survival in changing conditions. Firebreak width reflects the founder’s prior quality: a founder with a very high-quality prior (e.g., Robert Langer at MIT) can set a narrow firebreak (high τ) and pursue maximum efficiency; a less certain founder exploring novel terrain should widen the firebreak (low τ) to preserve room and time to maneuver when reality defies the initial thesis. ### 1.4. Methodological Innovation: Separating founder and Venture I implement hierarchical Bayesian modeling to separate the founder (prior) from the venture (likelihood). This enables simulation and calibration of a business model to raise its success probability. The separation clarifies how environmental complexity c shapes the likelihood of success and how the strategic choice of ambiguity τ shapes the prior—allowing us to model learning as belief-updating in response to venture-generated evidence (Gelman et al., 2020). ### 1.5. Bridging Schools of Thought The framework bridges the false dichotomy between the action school, which values flexibility and emergence, and the planning school, which values detailed prediction and commitment. In our model, the pure action school corresponds to the limit τ → 0 (maximal openness), and the pure planning school corresponds to τ → ∞ (absolute confidence in a single plan). These are endpoints of a continuum. Rather than “decision-making under uncertainty,” I model decision-making about uncertainty itself, offering a new perspective for strategy. ### 1.6. Roadmap I proceed with a “what–why–how–so what” logic. Section 2 develops the theory of τ, its structural and strategic metaphors grounded in the mathematical formalism of Bayesian and Evolutionary entrepreneur's logic. Section 3 applies the framework to Tesla and Better Place, showing how it explains real-world success and failure. Section 4 envisions future works in both theory and practice. ----