Semantics meets attractiveness: Choice by salience

TL;DR

This paper introduces a context-sensitive choice model incorporating both attractiveness and semantic salience, explaining complex decision behaviors and the 'moodiness' of decision makers with a focus on linear salience structures.

Contribution

It presents a novel model combining semantics and attractiveness in choice, characterizes linear salience with a behavioral property, and provides empirical estimates demonstrating its high selectivity.

Findings

Choices can be explained by salience-based linear orders.

The model captures decision 'moodiness' and rationalizes observed behaviors.

Numerical estimates show the model's high testability and bounded rationality.

Abstract

We describe a context-sensitive model of choice, in which the selection process is shaped not only by the attractiveness of items but also by their semantics ('salience'). All items are ranked according to a relation of salience, and a linear order is associated to each item. The selection of a single element from a menu is justified by one of the linear orders indexed by the most salient items in the menu. The general model provides a structured explanation for any observed behavior, and allows us to to model the 'moodiness' of a decision maker, which is typical of choices requiring as many distinct rationales as items. Asymptotically all choices are moody. We single out a model of linear salience, in which the salience order is transitive and complete, and characterize it by a behavioral property, called WARP(S). Choices rationalizable by linear salience can only exhibit non-conflicting violations of WARP. We also provide numerical estimates, which show the high selectivity of this testable model of bounded rationality.