Studying Cognition via Information Search in Game Experiments

University Of California-San Diego, La Jolla CA

Investigators

Abstract

Many unresolved questions about strategic behavior concern strategic sophistication, the extent to which players analyze their environment as a game, taking its structure and others' incentives into account. Sophistication is the main difference between the behavioral assumptions of traditional game theory, which take it to be unlimited, and adaptive learning models, which take it to be nonexistent or severely limited. It is also the main difference between the leading kinds of learning models, beliefs-based and reinforcement. Even when convergence to equilibrium is assured, sophistication can affect limiting outcomes through players' initial beliefs and the structure of their learning rules. The influence of sophistication on convergence, equilibrium selection, and the nature and timing of responses to changes in the environment give it a leading role in applications of game theory to economic, political, and social interactions. This project concerns experiments designed and conducted with Faculty Associate Miguel Costa-Gomes of the University of York, UK. Experiments allow the control needed to test theories of behavior in games, which are sensitive to environmental details. Experiments also allow us to study sophistication and other aspects of cognition more directly, by monitoring subjects' searches for hidden information. Under plausible assumptions about cognition and information search, leading theories of initial decisions and of learning both have sharply separated implications for search. Interpreting subjects' searches in the light of the cognitive implications of alternative theories of behavior help to understand their strategic thinking and predict their behavior. Some experiments adapt the MouseLab design used to study subjects' initial responses to two-person matrix games to two-person guessing games in which subjects have hidden, independently variable payoff parameters, monitoring their searches for their own and their partners' parameters. This new design better separates decision rules; and searching for payoff parameters within a known structure is a very different cognitive task than searching directly for payoffs in matrix games, which expands our view of subjects' strategic thinking. Another set of MouseLab experiments adapts our previous design to study initial responses to two-person matrix games with multiple equilibria mixed with other games designed to separate boundedly rational decision rules. The main goal is to separate traditional theories of equilibrium selection that apply notions like risk- or payoff-dominance to the set of equilibria from the view that responses to games with multiple equilibria are a by-product of the same kinds of boundedly rational rules that appear to predominate in other games. A third set of experiments monitor subjects' searches for hidden payoff parameters and the history of decisions and realized payoffs in repeated play of normal-form games with a variety of structures. The goal is to exploit sharp separation of information requirements to distinguish reinforcement and beliefs-based learning and hybrids such as experience-weighted attraction learning. These experiments require the development of a new interface with expanded search capabilities.