Abstract We propose and axiomatize the categorical thinking model (CTM) in which the framing of the decision problem affects how agents categorize alternatives, that in turn affects their evaluation of it. Prominent models of salience, status quo bias, loss-aversion, inequality aversion, and present bias all fit under the umbrella of CTM. This suggests categorization is an underlying mechanism of key departures from the neoclassical model of choice. We specialize CTM to provide a behavioural foundation for the salient thinking model of Bordalo et al. (2013, Journal of Political Economy, 121, 803–843) that highlights its strong predictions and distinctions from other models.

The standard revealed preference argument relies on an implicit assumption that a decision maker considers all feasible alternatives. The marketing and psychology literatures, however, provide well-established evidence that consumers do not consider all brands in a given market before making a purchase (Limited Attention). In this paper, we illustrate how one can deduce both the decision maker's preference and the alternatives to which she pays attention and inattention from the observed behavior. We illustrate how seemingly compelling welfare judgments without specifying the underlying choice procedure are misleading. Further, we provide a choice theoretical foundation for maximizing a single preference relation under limited attention. (JEL D11, D81)

This paper illustrates how one can deduce preference from observed choices when attention is both limited and random. We introduce a random attention model where we abstain from any particular attention formation and instead consider a large class of nonparametric random attention rules. Our intuitive condition, monotonic attention, captures the idea that each consideration set competes for the decision maker’s attention. We then develop a revealed preference theory and obtain testable implications. We propose econometric methods for identification, estimation, and inference for the revealed preferences. Finally, we provide a general-purpose software implementation of our estimation and inference results and simulation evidence.