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

• 成果类型: Article
• 署名作者: Cerreia-Vioglio, Simone; Maccheroni, Fabio; Marinacci, Massimo; Rustichini, Aldo
• 署名单位: Bocconi University; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdac046
• 发表日期: 2023
• 页码: 1155-1194
• 关键词: drift-diffusion model; visual working-memory; decision-making; rational inattention; stochastic choice; Time pressure; INFORMATION; DYNAMICS; performance; fixations

摘要

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 p(t) (a, A)= e(u(a)/lambda(t) +) (alpha(alpha))/Sigma(b is an element of A)e (u(b)/lambda(t) + alpha(b)), where p(t) (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, 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).