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. Our axiomatic analysis provides a behavioural foundation of softmax (also known as Multinomial Logit Model). Our neuro-computational derivation provides a biologically inspired algorithm that may explain the emergence of softmax in choice behaviour. Jointly, the two approaches provide a thorough understanding of softmaximization in terms of internal causes (neuro-physiological mechanisms) and external effects (testable implications).

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

Cerreia-Vioglio, Simone;Maccheroni, Fabio
;
Marinacci, Massimo;Rustichini, Aldo
In corso di stampa

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. Our axiomatic analysis provides a behavioural foundation of softmax (also known as Multinomial Logit Model). Our neuro-computational derivation provides a biologically inspired algorithm that may explain the emergence of softmax in choice behaviour. Jointly, the two approaches provide a thorough understanding of softmaximization in terms of internal causes (neuro-physiological mechanisms) and external effects (testable implications).
2022
Cerreia-Vioglio, Simone; Maccheroni, Fabio; Marinacci, Massimo; Rustichini, Aldo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/4052185
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