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Evolutionary game analysis and simulation with system dynamics for behavioral strategies of participants in crowd logistics

ABSTRACT Crowd logistics is believed to be an innovative idea that can solve the dilemma lying in the scattered transportation resources and unstable demands of urban freight transport. However, the negative interactions among the participants have left crowd logistics an unstable development. In order to reveal the interactions, which occur under the bounded rationality and discover influencing factors to participants’ strategy selections, this article develops the system evolutionary game model and system dynamics model. Also, it investigates how tripartite participants respond to the governance policies under static and dynamic scenario. The simulation results show that dynamic scenario has a better performance in restraining strategy fluctuations among the participants. Under the dynamic scenario, the strategy combination of participants eventually converges to the evolutionarily stable strategy. The probability that shippers adopt “price-exploitation” strategy is positively correlated with extra profits while negatively correlated with the upper bond of the penalties. The profits are the key factor affecting the willingness of drivers joining crowd logistics platform (CLP).