This paper explores the question whether boundedly rational agents learn to behave optimally when asked to voluntarily contribute to a public good. The decision process of individuals is described by an Evolutionary Algorithm. We find that the contribution level converges towards the Nash...

This paper tries to connect the theory of genetic algorithm (GA) learning to evolutionary game theory. It is shown that economic learning via genetic algorithms can be described as a specific form of evolutionary game. It will be pointed out that GA learning results in a series of near Nash...

This paper tries to connect the theory of genetic algorithm (GA) learning to evolutionary game theory. It is shown that economic learning via genetic algorithms can be described as a specific form of evolutionary game. It will be pointed out that GA learning results in a series of near Nash...

This paper tries to connect the theory of genetic algorithm (GA) learning to evolutionary game theory. It is shown that economic learning via genetic algorithms can be described as a specific form of evolutionary game. It will be pointed out that GA learning results in a series of near Nash...

The paper considers the application of Genetic Algorithms (GA) on coordination games with non-point pollution controlling systems including collective and random fines. During the GA simulations populations continually switch between the two symmetric Nash equilibria of the game. Coordination of...

Mathematically oriented microeconomic research has contributed enormously to the understanding of economic behavior and the functioning of markets and institutions. However, theoretical as well as applied microeconomic studies may be driven too much by mathematical feasibility. An illustrative...