The Price of Relearning: Ambiguity Provenance in Dynamic Decisions

Abstract

Dynamic robust systems often rebuild ambiguity sets as data arrive. Relearning can then replace the evaluator rather than merely update its beliefs; we call this dependence on an uncertainty set's date of origin ambiguity provenance. Under weak-evidence richness, a likelihood quotient exactly characterizes continuous compact-valued reconstruction rules that preserve Bayesian provenance. When compatibility fails and the discrepancy is decision-visible, sophisticated behavior admits an exact triangular evaluator-vintage representation before operationally redundant vintages are quotiented out. The directed welfare loss from later reconstruction is the Price of Relearning. We characterize when vintage state can be compressed, the divide between polynomial evaluation of specified finite models and NP-hard universal certification, and a compatible reconstruction that repairs the protocol. A Gaussian pricing benchmark closes the reconstruction-action-information loop; scanner data calibrate its scale without a causal protocol claim.