考虑学习速度的小世界网络上排污权拍卖策略演化

发布时间：2018-05-03 14:17

本文选题：信息交互结构 + 策略更新规则　；参考：《中国管理科学》2017年03期

【摘要】：针对排污权拍卖中厂商信息交互结构对其策略演化的影响,运用网络演化博弈方法,将小世界网络引入到排污权统一价格拍卖的博弈分析之中。采用小世界网络刻画厂商的信息交互结构,同时将厂商的学习速度引入到博弈参与人的博弈策略更新规则中,并运用eclipse仿真研究厂商的策略演化与其学习速度及信息交互结构的关系。研究结果表明:厂商的策略收敛速度与学习速度、度正相关;与网络聚类系数、社团结构数目先正相关、后反相关;社团内部厂商的策略收敛速度快于社团外部厂商,存在最优网络聚类系数与最优社团结构数目。研究结果为排污权出让方如何有效诱导竞买方真实报价,提高双方的决策效率提供了建议参考。
[Abstract]:Aiming at the influence of the information interaction structure on the evolution of pollution emission rights auction, the small world network is introduced into the game analysis of the unified price auction of emission rights by using the network evolution game method. The small world network is used to describe the information exchange structure of the firm, and the learning speed of the firm is introduced into the game strategy updating rules of the game participants. Eclipse simulation is used to study the relationship between vendor's strategy evolution and learning speed and information interaction structure. The results show that: the firm's strategy convergence speed is positively correlated with the learning speed and degree, and it is positively correlated with the network clustering coefficient, the number of community structure is positively correlated with the number of the community structure, and the strategy convergence rate of the firms within the community is faster than that of the firms outside the community. There exists an optimal network clustering coefficient and an optimal number of community structures. The results provide a reference for how to effectively induce the real offer of the buyer and improve the decision-making efficiency of both parties.
【作者单位】：武汉大学经济与管理学院;
【基金】：国家自然科学基金资助项目(71371147)
【分类号】：X196;F724.59

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