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Strategic Learning in the Sealed-Bid Bargaining Mechanism by Particle Swarm Optimization Algorithm

  • Xiaobo Zhu
  • Qian Yu
  • Xianjia Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

The learning behaviours of buyers and sellers in the sealed-bid Bargaining Mechanism were studied under the assumption of bounded rationality. The learning process of the agents is modelled by particle swarm optimization (PSO) algorithm. In the proposed model, there are two populations of buyers and sellers with limited computation ability and they were randomly matched to deal repeatedly. The agent’s bidding strategy is assumed to be a linear function of his value of trading item and each agent adjusts his strategy in repeated deals by imitating the most successful member in his population and by own past experience. Such learning pattern by PSO is closer to the behaviours of human beings in real life. Finally, the simulated results show that the bidding strategies of the agents in both populations will converge near the theoretical linear equilibrium solutions (LES).

Keywords

Particle Swarm Optimization Particle Swarm Optimization Algorithm Bidding Strategy Learning Pattern Coordination Failure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaobo Zhu
    • 1
  • Qian Yu
    • 1
  • Xianjia Wang
    • 1
  1. 1.Institute of Systems Engineering, Wuhan University, Wuhan 430072China

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