Multinomial Logit Processes and Preference Discovery: Inside and Outside the Black Box

Simone Cerreia-Vioglio, Fabio Maccheroni, Massimo Marinacci and Aldo Rustichini

The Review of Economic Studies, 2023, vol. 90, issue 3, 1155-1194

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$$\begin{align*}p_{t}\left( a,A\right) =\dfrac{e^{\frac{u\left( a\right) }{\lambda\left(t\right) }+\alpha\left( a\right) }}{\sum_{b\in A}e^{\frac{u\left(b\right) }{\lambda\left( t\right) }+\alpha\left( b\right) }},\end{align*}$$where $p_{t}\left( a,A\right)$ is the probability that alternative $a$ is selected from the set $A$ of feasible alternatives if $t$ is the time available to decide, $\lambda$ is a time-dependent noise parameter measuring the unit cost of information, $u$ is a time-independent utility function, and $\alpha$ is an alternative-specific bias that determines the initial choice probabilities (reflecting prior information and memory anchoring).Our axiomatic analysis provides a behavioural foundation of softmax (also known as Multinomial Logit Model when $\alpha$ is constant). 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).

Keywords: Discrete choice analysis; Drift Diffusion Model; Heteroscedastic extreme value models; Luce model; Metropolis algorithm; Multinomial Logit Model; Quantal response equilibrium; Rational inattention; D81 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:oup:restud:v:90:y:2023:i:3:p:1155-1194.

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