Inference From Random Restarts
Random-restart heuristics are widely used in nonconvex optimization and equilibrium computation: practitioners run a local algorithm from many initial conditions and interpret repeated convergence to the same output as evidence that the result is robust, dominant, or even unique. Despite its widespread use, this reasoning is usually informal. We provide a probabilistic framework for interpreting restart evidence. We give broad, easy-to-verify sufficient conditions under which repeated runs of a solver can be treated as independent draws from a categorical distribution induced by random initial conditions. Within this framework, we develop Bayesian inference from repeated identical outputs. We derive posterior concentration rates for basin size and uniqueness. These rates demonstrate that uniqueness is inherently harder than learning basin size: posterior concentration for uniqueness is polynomial, whereas basin size concentrates exponentially fast. We also provide a verification protocol for checking whether a given problem fits our framework. We demonstrate the protocol on a widely used equilibrium solver for mixed-logit demand with multi-product firms, and complement the verification exercise with posterior tables that apply to any restart experiment satisfying the protocol. We conclude by delineating limits of restart-based inference, including failures induced by solver--problem mismatch and limited visibility of alternative outcomes.