Regulatory Learning or Precaution? Capital, Reporting, and Supervisory Architecture Under Ambiguity

Abstract

This study develops a two-period regulatory model in which precautionary capital, information-producing reporting, provider participation, and supervisory architecture are chosen jointly. Reporting may produce a verified signal before continuation capital is set, but it also entails direct, participation, and fixed setup costs. Under a known prior, an ignorable diagnostic has weakly nonnegative gross decision value, yet reporting is activated only if its optimized net surplus exceeds the no-reporting option. Under recursive maxmin, an admissible adverse-state-certainty model can eliminate learning and shift policy toward precaution; uniformly interior priors can preserve learning. Recursive smooth ambiguity converges only to the maxmin problem defined on the same finite model support. For arbitrary noisy histories, information dominance under ambiguity additionally requires projective consistency of experiments, conditional prior sets, and second-order weights; rectangularity alone is not sufficient. The resulting regime theorem separates information value, reporting intensity, activation, and architecture choice. Analytical proofs establish the claims, while deterministic code, independent grid calculations, and fresh-process reruns verify the registered examples.