Abstract: In order to study the effects of manufacturers' information interaction structure on their strategies evolution in emissions permits auction, the method of evolutionary games on networks is utilized and a small-world network is introduced into the analysis of auction, and a small-world network is employed to portray manufacturers' information interaction structure. Meanwhile, learning speed is integrated into the strategy updating rule. Then, eclipse is used to simulate the effects of manufacturers' information interaction structure and their learning speed on the strategies evolution. The simulation results show that, manufacturers' strategies convergence speed has a positive correlation with their learning speed and degree, but has a first positive later negative correlation with clustering coefficient as well as the number of community structure. In addition, the strategies convergence speed of manufacturers in community is faster than that of manufacturers out community. There exits an optimal network clustering coefficient and an optimal number of community structure. At last, several suggestions are provided for government and manufacturers. The conclusion of this paper is helpful for emission rights licensors to induce the bidders to offer real price and can be regarded as a reference to improve the efficiency of decision-making on both sides.

Key words: information interaction structure, strategies updating rule, small-world networks, learning speed