| [[🐢stats]] | 🐢0 | **기: 의사결정의 4단계** 고전적 결정분석이 신념 도출과 효용 명세를 깔끔하게 분리하지만, 창업 현실은 이보다 복잡—신념과 욕망이 공진화 | p(x\|d)와 U(x)는 독립적이지 않음 | | ----------- | ---- | ------------------------------------------------------------------------------ | --------------------------------- | | | 🐢→ | **승: 베이지안 창업가정신** 정량적 렌즈를 제공하지만 규범적/실증적 접근의 혼란으로 모델의 유용성이 의문시되는 현 상황 | 데이터 기반 의사결정이 데이터 없는 미래 창조에는 무력 | | | 🐢← | **전: 인공적 분리 붕괴** 새로운 가능성을 상상하는 행위(신념 조정)가 가치있는 것(효용 재형성)을 동시에 변화시키는 얽힌 현실 | iPhone은 기술적 가능성과 새로운 가치 차원을 동시 창조 | | | 🐢→← | **결: 공동 최적화** 순차적 단계가 아닌 신념 구조(a,b)와 효용의 동시 설계로 창업가의 의사결정 재정의 | 신념과 욕망이 함께 춤추는 수학 | ### 2.2 🐢 Statistics and Decision Science: From Data Analysis to Uncertainty Design **TL;DR**: Classical decision analysis separates belief elicitation from utility specification in four neat steps. But entrepreneurial reality is messier: beliefs about what's possible and desires for what's valuable co-evolve. We collapse this artificial separation, recognizing that imagining new possibilities (beliefs) simultaneously creates new dimensions of value (utility). The marriage of operations and statistics promised to eliminate guesswork through data-driven decision making. But traditional decision analysis, with its neat four-step process, reveals precisely why entrepreneurship demands a different approach. **Common Ground**: Classical decision analysis provides a rigorous framework: (1) Define possible outcomes X and decisions D; (2) Define probability distributions p(x|d) for outcomes given decisions; (3) Define utility function U:X→ℝ; (4) Choose d* = argmax E[U(x)|d]. This works beautifully when you know what game you're playing. Bayesian approaches add sophistication by incorporating uncertainty through prior beliefs, and random utility models reveal preferences from observed choices (Ben-Akiva & Lerman, 1985; Train, 2009). **Critical Gap**: But here's where the framework breaks down for entrepreneurs. As Gelman et al. (2021) identify in their catalog of Bayesian "holes," traditional approaches assume you can separately elicit priors (step 2) and utilities (step 3). Entrepreneurs face a more tangled reality: their beliefs about what's possible and their goals for what's desirable co-evolve. Bayesian entrepreneurship offers a quantitative lens (Agrawal et al., 2024; Camuffo et al., 2024), but unsettled confusion between normative and positive approaches makes the usefulness of models questionable. The entrepreneur's utility function isn't fixed—it's discovered through the very process of imagining new realities. **Our Extension**: We collapse the artificial separation between belief elicitation and utility specification. Instead of sequential steps, entrepreneurs face a joint optimization problem: simultaneously designing belief structures (a,b) and discovering utilities through market interaction. Our framework recognizes that p(x|d) and U(x) aren't independent—the act of imagining new possibilities (adjusting beliefs) changes what's valuable (reshaping utility). This isn't a bug in entrepreneurial decision-making; it's the core feature. When Steve Jobs imagined the iPhone, he wasn't just updating beliefs about technical feasibility; he was creating new dimensions of value that didn't previously exist in anyone's utility function. The mathematics must reflect this reality where belief and desire dance together. [[noubar_afeyan]]