Computational Economics

, 34:399

Particle Swarm Optimization Algorithm for Agent-Based Artificial Markets

Article

DOI: 10.1007/s10614-009-9171-9

Cite this article as:
Zhang, T. & Brorsen, B.W. Comput Econ (2009) 34: 399. doi:10.1007/s10614-009-9171-9
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Abstract

Particle swarm optimization (PSO) is adapted to simulate dynamic economic games. The robustness and speed of the PSO algorithm is compared to a genetic algorithm (GA) in a Cournot oligopsony market. Artificial agents with the PSO learning algorithm find the optimal strategies that are predicted by theory. PSO is simpler and more robust to changes in algorithm parameters than GA. PSO also converges faster and gives more precise answers than the GA method which was used by some previous economic studies.

Keywords

Agent-based market Genetic algorithm Particle swarm optimization Simulation 

Copyright information

© Springer Science+Business Media, LLC. 2009

Authors and Affiliations

  1. 1.Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduPeople’s Republic of China
  2. 2.Department of Agricultural EconomicsOklahoma State UniversityStillwaterUSA

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