### On Creating a Simulation-Based Proposition ***Vikash***: "What if we create one? Something like: 'Entrepreneurs can preserve learning capacity by simulating market feedback distributions ex-ante, selecting (μ,τ) pairs that maintain sufficient posterior variance across likely scenarios.'" ***Charlie***: "That's not a proposition, Vikash. That's a methodology description. What's the testable insight?" ***Scott***: "Exactly. We're conflating tool with theory. Simulation-based calibration is HOW entrepreneurs might implement our insights, not the insight itself." ***Vikash***: "You're right. I was getting carried away with the computational elegance. The real insight is already embedded in Proposition 5—the precision threshold. That's what simulation would help entrepreneurs discover." ### Identifying the Fourth Key Proposition ***Moshe***: "So if not simulation, what's our fourth home run? We have three solid ones: operational complexity determining promise levels, precision thresholds creating learning traps, and joint optimization of ambition and precision." ***Charlie***: "Looking at the paper, there's a critical insight we're undervaluing. Go back to Proposition 2—when promises linearly affect resources, maximum promises become optimal. That's the fundamental tension that drives everything else." ***Scott***: "Charlie's right. The sequence tells a story: Proposition 2 shows why entrepreneurs race toward bold promises, Proposition 3 shows why physics constrains them, Proposition 5 shows why precision traps them, and Proposition 7 shows the optimal balance. It's a complete theoretical arc." ***Vikash***: "But Proposition 2 feels too simple compared to the others. It's just ∂U/∂φ = αV > 0, therefore φ* = 1." ***Moshe***: "Simple isn't bad if it's foundational. Think about it—this proposition explains Silicon Valley's 'fake it till you make it' culture. I