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Updating stochastic choice


Alos-Ferrer, Carlos; Mihm, Maximilian (2021). Updating stochastic choice. Working paper series / Department of Economics 381, University of Zurich.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Economics
Working Paper Series > Department of Economics
Dewey Decimal Classification:330 Economics
JEL Classification:D01, D81
Uncontrolled Keywords:Stochastic preferences, Bayesian learning, logit choice, reinforcement, neuroeconomic theory
Language:English
Date:March 2021
Deposited On:24 Mar 2021 10:09
Last Modified:24 Mar 2021 10:09
Series Name:Working paper series / Department of Economics
Number of Pages:36
ISSN:1664-705X
OA Status:Green
Official URL:https://www.econ.uzh.ch/en/research/workingpapers.html?paper-id=1063

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