Discrete Public Goods: Contribution Levels and Learning as Outcomes of an Evolutionary Game
AbstractThis paper examines the learning dynamics of boundedly rational agents, who are asked to contribute to a discrete public good. In an incomplete information setting, we discuss contribution games and subscription games. The theoretical results on myopic best response dynamics implying striking differences between strategies played in the two games are confirmed by simulations, where the learning process is modeled by an Evolutionary Algorithm. We show that the contribution game even aggravates the selective pressure leading towards the non-contributing equilibrium, thereby supporting results from laboratory experiments. In contrast to this, the subscription game removes the 'fear incentive', implying a higher percentage of successful provisions over time.
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Bibliographic InfoPaper provided by Royal Economic Society in its series Royal Economic Society Annual Conference 2003 with number 45.
Date of creation: 04 Jun 2003
Date of revision:
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bounded rationality; evolutionary games; evolutionary algorithms; learning; public goods;
Find related papers by JEL classification:
- C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
- C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
- 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-2003-06-16 (All new papers)
- NEP-CBE-2003-06-16 (Cognitive & Behavioural Economics)
- NEP-GTH-2003-06-16 (Game Theory)
- NEP-PBE-2003-06-16 (Public Economics)
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