Making Decisions under Model Misspecification

TL;DR

This paper develops a decision-theoretic framework that explicitly accounts for model misspecification by integrating the uncertainty from using simplified models as approximations, providing a formal criterion for decision-making under such conditions.

Contribution

It introduces an axiomatic approach to decision-making that incorporates concerns about model misspecification, extending max-min analysis to more realistic scenarios.

Findings

Provides a formal decision criterion considering model misspecification

Extends max-min analysis to include model approximation uncertainty

Offers an axiomatic foundation for decisions under model simplifications

Abstract

We use decision theory to confront uncertainty that is sufficiently broad to incorporate "models as approximations." We presume the existence of a featured collection of what we call "structured models" that have explicit substantive motivations. The decision maker confronts uncertainty through the lens of these models, but also views these models as simplifications, and hence, as misspecified. We extend the max-min analysis under model ambiguity to incorporate the uncertainty induced by acknowledging that the models used in decision-making are simplified approximations. Formally, we provide an axiomatic rationale for a decision criterion that incorporates model misspecification concerns.