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 (Equation Presented) 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, 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).
Original language | English (US) |
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Pages (from-to) | 1155-1194 |
Number of pages | 40 |
Journal | Review of Economic Studies |
Volume | 90 |
Issue number | 3 |
DOIs | |
State | Published - May 1 2023 |
Bibliographical note
Publisher Copyright:© 2022 The Author(s).
Keywords
- Discrete choice analysis
- Drift Diffusion Model
- Heteroscedastic extreme value models
- Luce model
- Metropolis algorithm
- Multinomial Logit Model
- Quantal response equilibrium
- Rational inattention