Learning to Respond: The Use of Heuristics in Dynamic Games

Mikhael Shor

Game Theory and Information from University Library of Munich, Germany

Abstract: 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.

Keywords: learning; limited information; dynamic games (search for similar items in EconPapers)
JEL-codes: C73 C91 D83 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2003-01-27
New Economics Papers: this item is included in nep-cbe, nep-gth and nep-ind
Note: Type of Document - Acrobat PDF; prepared on IBM PC; to print on HP; pages: 34 ; figures: included. 35 pages, Acrobat PDF, figures included
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpga:0301001

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