Learning in a Black Box
AbstractMany interactive environments can be represented as games, but they are so large and complex that individual players are in the dark about others' actions and the payoff structure.� This paper analyzes learning behavior in such 'black box' environments, where players' only source of information is their own history of actions taken and payoffs received.� Specifically we study voluntary contributions games.� We identify two robust features of the players' learning dynamics: search volatility and trend-following.� These features are clearly present when players have no information about the game; but also when players have full informaiton.� Convergence to Nash equilibrium occurs at about the same rate in both situations.
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Bibliographic InfoPaper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 653.
Date of creation: 23 Apr 2013
Date of revision:
learning; information; public goods games;
Find related papers by JEL classification:
- C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
- C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
- C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- H41 - Public Economics - - Publicly Provided Goods - - - Public Goods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-06-04 (All new papers)
- NEP-CBE-2013-06-04 (Cognitive & Behavioural Economics)
- NEP-CTA-2013-06-04 (Contract Theory & Applications)
- NEP-EVO-2013-06-04 (Evolutionary Economics)
- NEP-EXP-2013-06-04 (Experimental Economics)
- NEP-GTH-2013-06-04 (Game Theory)
- NEP-HPE-2013-06-04 (History & Philosophy of Economics)
- NEP-MIC-2013-06-04 (Microeconomics)
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.:
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