PARTICLE SWARM OPTIMIZATION IN AGENT-BASED ECONOMIC SIMULATIONS OF THE COURNOT MARKET MODEL

摘要

The numerous variations of the particle swarm optimization (PSO) algorithm originally proposed by Kennedy and Eberhart (1995. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks IV. IEEE: Piscataway, NJ; 1942-1948) have proven to be powerful optimization methods that rely on exploiting simple analogues of social interaction. In this study, PSO is adopted in lieu of the social or individual evolutionary learning algorithms as a model of individual adaptation in an agent-based computational model. In this examination of the simple Cournot market framework, each agent's individual strategy evolves according to the PSO algorithm. The model is one in which agents' strategies must adapt interdependently. That is, a change in one particle may not only affect its performance but also other particles within the same swarm simultaneously. The dynamics and convergence properties associated with this model are compared with those where evolutionary learning algorithms are employed. Similar to evolutionary learning, convergence to equilibrium is dependent on the scope of learning, social or individual. While convergence is dependent on some of the algorithm parameters, prices resulting from the individual PSO are nearest the Cournot equilibrium and those from social PSO are nearest the Walrasian equilibrium in all cases. For particular parameterizations, certain advantages over evolutionary algorithms exist: in the main, decreasing volatility in market prices does not require an election operator or the addition of free parameters through two-level learning.