Review of Economic Studies Vol. 90 No. 3 2023
Multinomial Logit Processes and Preference Discovery: Inside and Outside the Black Box
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 $$\beginalign* p_t\left( a,A\right) =\dfrace^\fracu\left( a\right) λ\left( t\right) +α\left( a\right) \sum_b\in Ae^\fracu\left( b\right) λ\left( t\right) +α\left( b\right) , \endalign*$$ 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, λ is a time-dependent noise parameter measuring the unit cost of information, u is a time-independent utility function, and α 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 α 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).
- DOI
- 10.1093/restud/rdac046
- Volume
- 90
- Issue
- 3
- Pages
- 1155-1194
- Language
- en
- Sources
- openalex bibtex:phds-export.bib crossref