Making Decisions Under Model Misspecification.
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...
| Publicado en: | Review of Economic Studies Vol. 93; no. 2; pp. 892 - 926 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Mar2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192334041&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192334041 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00346527 REM jtl: Review of Economic Studies issn: 00346527 maglogo: N pubinfo: dt: Mar2026 vid: 93 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 192334041 10.1093/restud/rdaf046 ppf: 892 ppct: 34 formats: tig: atl: Making Decisions Under Model Misspecification. aug: au: Cerreia-Vioglio, Simone Hansen, Lars Peter Maccheroni, Fabio Marinacci, Massimo affil: Università Bocconi and Igier, Italy University of Chicago, USA su: Decision theory Approximation error Bayesian analysis Scientific models Robust optimization Uncertainty (Information theory) sug: subj: Decision theory Approximation error Bayesian analysis Scientific models Robust optimization Uncertainty (Information theory) keyword: Ambiguity copyrightHolder:Review of Economic Studies Ltd copyrightYear:2026 inLanguage:en Model misspecification publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/restud/rdaf046 Uncertainty Ambiguity copyrightHolder:Review of Economic Studies Ltd copyrightYear:2026 inLanguage:en Model misspecification publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/restud/rdaf046 Uncertainty ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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