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. Analysis of human behavior demonstrates that people are often remarkably responsive to changes in their environment, on time scales ranging from millennia (evolution) to milliseconds (reflex). The goal of this paper is to evaluate several prominent learning models in light of a laboratory experiment on responsiveness in a lowinformation 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 these 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.
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Length: 34 pages Date of creation: 27 Jan 2003 Date of revision: Handle: RePEc:wpa:wuwpga:0301001
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Find related papers by JEL classification: D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
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References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Itzhak Gilboa & David Schmeidler, 1993.
"Case-Based Optimization,"
Discussion Papers
1039, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
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