Strategic Learning in the Sealed-Bid Bargaining Mechanism by Particle Swarm Optimization Algorithm
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).
KeywordsParticle Swarm Optimization Particle Swarm Optimization Algorithm Bidding Strategy Learning Pattern Coordination Failure
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