ICES: Small: Decision Making with Bounded Categorization

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Classical economic theory models humans as acting rationally through the optimization of their expected utilities. The paradigm of bounded rationality takes a step toward greater realism by placing computational limitations on agents' abilities to determine optimal decisions. Behavioral and cognitive studies reveal that humans are also categorically bounded, meaning that they use a finite categorization of the set of decision problems that may be posed, with a small number of categories. This project will focus on the use of quantization theory and information theory to establish foundations for the interplay between categorization and decision making. The researchers aim to understand the impact of categorization on individual decision making, team decision making through majority voting, and sequential decision making. The theory developed in the project will include both analysis of situations in which the categorization is fixed and optimal design of categorizations. In addition to the behavioral justification for the study of categorization, informational limitations on learning suggest that categorization into classes of decision problems has ramifications for engineering design.This project has the potential to influence economic theory and the understanding of certain social and organizational phenomena. Specifically, the project offers a way to understand the decision making performance of teams, with the incorporation of certain human limitations and the potential for differing preferences (e.g., between Type I and Type II errors) among teammates. Differences in preferences lead to a quantifiable penalty of team discord, even when the team shares the common goal of making correct decisions. There is also a quantifiable advantage from team diversity in the sense of obtaining better performance when teammates apply different categorizations. These new concepts could contribute to principles for team formation and for how data gathering policies can be optimized with the goal of fair decision making.