We revisit Popper's falsifiability criterion. A tester hires a potential expert to produce a theory, offering payments contingent on the observed performance of the theory. In our model, instead of knowing the true data-generating process, the expert knows the state-of-the-art belief over data-generating processes. A non-expert does not. We argue that if the expert can, moreover, acquire additional information to refine this knowledge, falsifiability does have the power to distinguish between experts and non-experts and to identify valuable theories, capitalizing on experts' ability to acquire and refine knowledge.
Quantitative mode stability for the wave equation on the Kerr-Newman spacetime
Risk-Aware Objective-Based Forecasting in Inertia Management
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Simulation-based Bayesian inference with ameliorative learned summary statistics -- Part I