- using [subjective equivalence classes based on desire and belief cld](https://claude.ai/chat/55af53dc-bbc1-4785-a9ee-bdf451a6a460) abstract: The optimal stopping theory, formalized in the Secretary Problem, assumes three key conditions: (1) perfect relative ranking between observed candidates, (2) irrevocable sequential decisions, and (3) fixed candidate quality independent of observation order. Under these conditions, the optimal strategy achieves a 1/e (≈37%) success rate by rejecting the first n/e candidates and selecting the next candidate who exceeds all previous observations. While this framework has been applied to entrepreneurial decision-making, we demonstrate why these assumptions fundamentally break down in entrepreneurship. Our analysis shows that entrepreneurial decisions simultaneously create and discover the space of possible states and actions - the very act of observing changes both what can be observed and how future observations are evaluated. Using a four-step state-action evolution framework, we formalize how entrepreneurs' beliefs and desires actively shape their opportunity landscape, unlike the fixed-state space of the Secretary Problem. This reconceptualization contributes to understanding belief heterogeneity in entrepreneurship and suggests new approaches for designing both societal matching mechanisms and individual path-finding algorithms that account for the co-evolution of knowledge states and action possibilities. # 🗄️ secretary vs opportunity | name of assumption | Secretary searching | Opportunities searching | |-------------------|---------------------|------------------------| | Fixed State Space | Nature presents n candidates in random order; perfect ranking possible between seen candidates | Nature presents evolving opportunities; ranking depends on entrepreneur's accumulated knowledge and actions | | Fixed Exchangeability | All candidates are assumed exchangeable before seeing them; only their relative rank matters | Opportunities are not exchangeable - each exploration changes both the explorer and exploration space | | Known Uncertainty Structure | Only uncertainty is about future candidates' ranks; no latent variables considered | Multiple uncertainties: about opportunity value, about how exploration changes understanding, about what's possible | | Static Decision Rule | Simple stopping rule: reject first n/e candidates, then hire first best-so-far | Dynamic learning rule: each action both evaluates current opportunity and shapes ability to recognize future ones | # 🗄️🗄️ Second Table | Step | Entrepreneurs searching for opportunities | |------|------------------------------------------| | 1. Choose Random (🔵) | Start with seemingly conflicting goals/opportunities (like team's different perspectives) and treat them as random samples from possibility space | | 2. Verify Exchangeability (🔵➡️🔴) | Test if different goals/perspectives can be grouped into meaningful patterns; look for hidden connections between seemingly opposing views | | 3. Represent Ignorance (💚) | Acknowledge that conflicts might signal unexplored integration possibilities; represent uncertainty about how goals could complement rather than compete | | 4. Make Knowledge (🔴) | Transform conflicting perspectives into integrated knowledge; build shared understanding that combines multiple viewpoints into richer possibilities | | Aspect<br>from [🗣️TBV2024 conflict2integ](https://otter.ai/s/MV0uFkqqT32TZT2Cr6IS0w?snpt=true)<br>![[tradeoff2integration.png\|100]] | From Conflicting Goals | To Integrated Goals | | ------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | Team Dynamics | • Different perspectives cause tension<br>• Neurological "pain" from opposing views<br>• Conservative behavior in committees | • Collaborative interpretation<br>• Shared understanding<br>• Joint problem-solving | | Experimental Approach | • Individual agendas<br>• Separate research directions<br>• Competing methodologies | • Strategic experiment design<br>• Objective common ground<br>• Shared methodological framework | | Institutional Framework | • Siloed decision-making<br>• Fragmented governance<br>• Individual priorities | • Science at scale<br>• Aggregation mechanisms<br>• Collective judgment systems |