Simulation of Double Bargaining Mechanism with External Subsidy by Particle Swarm Optimization

Abstract

The equilibrium and efficiency of double sealed-bid bargaining mechanism were studied under the external subsidy of full-bonus, half-bonus and none-bonus. The buyer and seller of bounded rationality was hard to choose the equilibrium solution in one trade. To investigate the learning behaviours of the agents, a trading simulating system in which two populations of buyers and sellers were randomly matched to deal repeatedly was constructed, and the evolutionary learning process of the agents were modelled by particle swarm optimization (PSO) algorithm. The simulated results show that final bidding strategies of all agents in both populations are very close to the theoretical equilibrium solutions through an adaptive learning process, and external bonus markedly improve trading efficiency.