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

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).