Learning to Respond: The Use of Heuristics in Dynamic Games

While many learning models have been proposed in the game theoretic literature to track individuals' behavior, surprisingly little research has focused on how well these models describe human adaptation in changing dynamic environments. This paper evaluates several learning models in light of a laboratory experiment on responsiveness in a low-information dynamic game subject to changes in its underlying structure. While history-dependent reinforcement learning models track convergence of play well in repeated games, it is shown that they are ill suited to dynamic environments, in which sastisficing models accurately predict behavior. A further objective is to determine which heuristics, or "rules of thumb," when incorporated into learning models, are responsible for accurately capturing responsiveness. Reference points and a particular type of experimentation are found to be important in both describing and predicting play. Implications for the design of learning models for dynamic, low-information settings such as the Internet are discussed.