Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Coordination via genetic learning
Computational Economics - Special issue: genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computational Economics
Robust Evolutionary Algorithm Design for Socio-economic Simulation
Computational Economics
Relative risk aversion and wealth dynamics
Information Sciences: an International Journal
Particle Swarm Optimization Algorithm for Agent-Based Artificial Markets
Computational Economics
On the role of risk preference in survivability
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Paradox Lost: The Evolution of Strategies in Selten's Chain Store Game
Computational Economics
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We study a general equilibrium system where agents haveheterogeneous beliefs concerning realizations of possible outcomes.The actual outcomes feed back into beliefs thus creating acomplicated nonlinear system. Beliefs are updated via a geneticalgorithm learning process which we interpret as representingcommunication among agents in the economy. We are able toillustrate a simple principle: genetic algorithms can beimplemented so that they represent pure learning effects (i.e.,beliefs updating based on realizations of endogenous variables inan environment with heterogeneous beliefs). Agents optimally solvetheir maximization problem at each date given their beliefs at eachdate. We report the results of a set of computational experimentsin which we find that our population of artificial adaptive agentsis usually able to coordinate their beliefs so as to achieve thePareto superior rational expectations equilibrium of the model.