Making Decisions Under Model Misspecification

• 成果类型: Article; Early Access
• 署名作者: Cerreia-Vioglio, Simone; Hansen, Lars Peter; Maccheroni, Fabio; Marinacci, Massimo
• 署名单位: Bocconi University; Bocconi University; University of Chicago
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdaf046
• 发表日期: 2025
• 关键词: Maxmin expected utility; ambiguity aversion; uncertainty; equilibrium; beliefs

摘要

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. We then extend our analysis beyond the max-min case allowing for a more general criterion that encompasses a Bayesian formulation.