Strategic Learning in Multiple Equilibria for Double Bargaining Mechanism by PSO

Xiao Bo Zhu

doi:10.4028/www.scientific.net/AMM.571-572.258

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 571-572Strategic Learning in Multiple Equilibria for...

Strategic Learning in Multiple Equilibria for Double Bargaining Mechanism by PSO

Abstract:

The learning behaviours of buyers and sellers with the assumption of bounded rationality were studied in the double sealed-bid bargaining mechanism. A multi-agent simulation trading system was constructed to observe the process of equilibrium approach when exist the multiple equilibria. The bidding choices of the agents were modelled by particle swarm optimization (PSO) algorithm. In our proposed model, two populations of buyers and sellers were randomly matched to deal repeatedly until the iteration stop, and each agent would update his bidding strategy in each round by imitating the successful member in his population and by private experience. Results show that the final biddings of the agents in both populations commonly approach a Nash equilibrium which is reasonable for the market principle.

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Periodical:

Applied Mechanics and Materials (Volumes 571-572)

Pages:

258-261

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.571-572.258

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Online since:

June 2014

Authors:

Xiao Bo Zhu*

Keywords:

Multi-Agent System (MAS), Multiple Equilibria, Particle Swarm Optimization (PSO), Swarm Intelligence

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