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Multinomial logit processes and preference discovery: outside and inside the black box

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  • Simone Cerreia-Vioglio
  • Fabio Maccheroni
  • Massimo Marinacci

Abstract

We provide two characterizations, one axiomatic and the other neuro-computational, of the dependence of choice probabilities on deadlines, within the widely used softmax representation, where pt (a; A) is the probability that alternative a is selected from the set A of feasible alternatives if t is the time available to decide, is a time dependent noise parameter measuring the unit cost of information, u is a time independent utility function, and is an alternative-specific bias that determines the initial choice probabilities and possibly reáects prior information. Our axiomatic analysis provides a behavioral foundation of softmax (also known as Multinomial Logit Model when is constant). Our neuro-computational derivation provides a biologically inspired algorithm that may explain the emergence of softmax in choice behavior. Jointly, the two approaches provide a thorough understanding of soft-maximization in terms of internal causes (neurophysiological mechanisms) and external e§ects (testable implications). Keywords: Discrete Choice Analysis, Drift Di§usion Model, Heteroscedastic Extreme Value Models, Luce Model, Metropolis Algorithm, Multinomial Logit Model, Quantal Response Equilib ium, Rational Inattention

Suggested Citation

  • Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci, 2020. "Multinomial logit processes and preference discovery: outside and inside the black box," Working Papers 663, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  • Handle: RePEc:igi:igierp:663
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    Keywords

    discrete choice analysis; drift di§usion model; heteroscedastic extreme value models; luce model; metropolis algorithm; multinomial logit model; quantal response equilib ium; rational inattention;
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