Arbeitspapier

Updating stochastic choice

When an economic agent makes a choice, stochastic models predicting those choices can be updated. The structural assumptions embedded in the prior model condition the updated one, to the extent that the same evidence produces different predictions even when previous ones were identical. We provide a general framework for models of stochastic choice allowing for arbitrary forms of (structural) updating and show that different models can be sharply separated by their structural properties, leading to axiomatic characterizations. Our framework encompasses Bayesian updating given beliefs over deterministic preferences (as implied by popular random utility models) and standard neuroeconomic models of choice, which update decision values in the brain through reinforcement learning.

Sprache
Englisch

Erschienen in
Series: Working Paper ; No. 381

Klassifikation
Wirtschaft
Microeconomic Behavior: Underlying Principles
Criteria for Decision-Making under Risk and Uncertainty
Thema
Stochastic preferences
Bayesian learning
logit choice
reinforcement
neuroeconomic theory

Ereignis
Geistige Schöpfung
(wer)
Alós-Ferrer, Carlos
Mihm, Maximilian
Ereignis
Veröffentlichung
(wer)
University of Zurich, Department of Economics
(wo)
Zurich
(wann)
2021

DOI
doi:10.5167/uzh-201967
Handle
Letzte Aktualisierung
20.09.2024, 08:20 MESZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Alós-Ferrer, Carlos
  • Mihm, Maximilian
  • University of Zurich, Department of Economics

Entstanden

  • 2021

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