Learning to Respond: The Use of Heuristics in Dynamic Games
AbstractWhile 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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Bibliographic InfoPaper provided by EconWPA in its series Game Theory and Information with number 0301001.
Length: 34 pages
Date of creation: 27 Jan 2003
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learning; limited information; dynamic games;
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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-02-03 (All new papers)
- NEP-CBE-2003-02-03 (Cognitive & Behavioural Economics)
- NEP-GTH-2003-02-03 (Game Theory)
- NEP-IND-2003-02-03 (Industrial Organization)
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