Categorization and coordination

Abstract

The use of coarse categories is prevalent in various situations and has been linked to biased economic outcomes, ranging from discrimination against minorities to empirical anomalies in financial markets. In this paper we study economic rationales for categorizing coarsely. We think of the way one categorizes one's past experiences as a model of the world that is used to make predictions about unobservable attributes in new situations. We first show that coarse categorization may be optimal for making predictions in stochastic environments in which an individual has a limited number of past experiences. Building on this result, and this is a key new insight from our paper, we show formally that cases in which people have a motive to coordinate their predictions with others may provide an economic rationale for categorizing coarsely. Our analysis explains the intuition behind this rationale.

The emergence of discrimination due to miscategorization

“…Azrieli (2009) presents a model where agents categorize others in a large game, while Azrieli (2010) builds a model where agents categorize others in a series of games where agents randomly match each other. While Mohlin (2014) studies the optimal categorization, Daskalova and Vriend (2020) studies how categorization strategies become shared. Learning how a category behaves on average also requires cognition costs.…”

The Benefits of Coarse Preferences

“…Steiner and Stewart (2015) study the price distortions that are induced when traders apply coarse reasoning in their forecasts. Daskalova and Vriend (2020) examine how attempts to coordinate predictions with others affects incentives for coarse categorization in different environments. A key difference between these models and ours is that in the models in the other papers that we mentioned, the categorization is determined exogenously, whereas in our model, the categorization are endogenously determined as part of the solution concept.…”