Procedural Complexity and Economic Behavior

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

Abstract Economists traditionally assume that humans are capable of using very complex decision rules to make optimal choices in markets and organizations. Economists also traditionally assume that humans are capable of complying with very complex rules set by governments and organizational bureaucracies, such as complex tax codes and auction rules. Several decades of research both in controlled laboratory experiments and using field data call these traditional assumptions into question. Economists understand that there exist bounds on the complexity of tasks humans are able to perform, but it has proved difficult to formally define and measure complexity and put the notion to use in making predictions and setting policy. This proposal uses conceptual and empirical tools from economics, psychology and computer science to carefully experimentally measure what features of rules make them complex in terms of difficulty and personal burden for humans to perform. This corpus of data will improve our ability to predict human behavior when faced with complexity, and allow us to develop effective, streamlined policies that realistically account for human capacities to cope with complexity. Both internal decision procedures and external rules can be modeled as algorithms, implemented not by machines but by humans. For instance, strategies in repeated games, and procedures in many decision problems, can be described as simple formal models of algorithms from theoretical computer science. This research consists of a series of experiments designed to evaluate various ways of classifying the algorithms underlying procedures and rules as more or less complex for humans, and to understand which measures of complexity accurately predict and describe human mistakes and subjective costs. The proposal considers distinct ways of modeling the complexity of algorithms and examines how well they predict subjects’ difficulty with implementing decision rules. The goal of the research is to develop an empirically grounded characterization of the determinants of procedural complexity for humans, which will be useful for building predictive models of human behavior, and for designing more effective policies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.