# 🟥C1: Cast Stakeholder Prioritization as Unified Prediction-Prescription Model The failures of separated approaches—prediction being too slow for perishable commitment windows, prescription being too brittle for uncertain environments—motivate our unified framework that integrates both into a single prediction-prescription model. Rather than treating learning and optimization as sequential activities, this framework recognizes them as fundamentally coupled: every quality decision generates information about stakeholder responses, which refines future quality decisions in a continuous loop. Tesla's Roadster development exemplified this integration, simultaneously optimizing performance specifications while learning how luxury buyers and battery partners actually responded to different design choices. The mathematical innovation lies in jointly optimizing over both quality level q and response parameters (βr, βc), transforming the traditional either-or dilemma into a both-and solution. This enables entrepreneurs to act decisively while remaining adaptive—what we call "living 48 hours in a day" by making optimal decisions for current conditions while positioning for better decisions tomorrow. Tesla could launch with initial quality assumptions but build in mechanisms to rapidly update based on pre-order patterns, supplier feedback, and early customer experiences. The unified model addresses the fundamental tension between moving fast (before commitment expires) and making informed decisions (based on accurate parameters). By treating quality and learning as joint decisions, entrepreneurs escape the trap of either waiting too long to learn or betting everything on unvalidated assumptions. Each quality choice is evaluated not just for immediate expected value but also for its information value in refining parameter estimates—Tesla's early prototype demonstrations simultaneously tested customer willingness to pay while revealing battery partner capabilities. This integration proves particularly powerful in degenerate environments where high variable-to-constraint ratios make pure strategies inadequate. The framework provides mathematical rigor for balancing exploitation (optimizing based on current knowledge) with exploration (learning to improve future decisions), offering entrepreneurs a practical approach to navigating multi-stakeholder coordination when every moment counts. Rather than choosing between being a careful analyst or a bold actor, the unified model enables entrepreneurs to be both, dynamically adjusting the balance based on how quickly commitment windows are closing.