基于复杂网络演化博弈的传统产业集群低碳演化模型研究

中国管理科学 ›› 2014, Vol. 22 ›› Issue (12): 41-47.

基于复杂网络演化博弈的传统产业集群低碳演化模型研究

张宏娟, 范如国

武汉大学经济与管理学院, 湖北武汉 430072

收稿日期:2013-07-01 修回日期:2014-03-27 出版日期:2014-12-20 发布日期:2014-12-23
作者简介:张宏娟(1983-),女(汉族),湖北武汉人,武汉大学经济与管理学院博士生,研究方向:复杂网络、产业集群.
基金资助:
国家自然科学基金资助项目(71271159)

Research on Low-Carbon Evolutionary Model of Traditional Industrial Clusters Based on Evolutionary Games Theory on Complex Networks

ZHANG Hong-juan, FAN Ru-guo

Economics and Management School, Wuhan University, Wuhan 430072, China

Received:2013-07-01 Revised:2014-03-27 Online:2014-12-20 Published:2014-12-23

摘要/Abstract

摘要： 本文基于复杂网络演化博弈理论,从微观异质性主体的预期学习和自适应行为特征出发,综合运用随机博弈及博弈学习模型、多主体系统建模等方法,构建了传统产业集群低碳演化模型,并进行了产业低碳策略竞争、涌现和不同演化情景的仿真分析。研究表明,产业集群的复杂网络结构、主体的异质性预期和决策行为对集群低碳策略的涌现有着重要影响;要想在集群内推广应用新的低碳策略,集群异质性主体的低碳偏好、集群复杂网络的外部效应、低碳策略的采用成本和技术兼容性是重要影响因素。

关键词: 传统产业集群, 复杂网络, 演化博弈, 低碳演化

Abstract: Based on evolutionary games theory on complex networks, from the perspective of clusters' microscopic heterogeneous agents, considering the characteristics of agents' expected learning behavior and adaptive behavior, a traditional industrial low-carbon evolution model by using the random game, learning model and multi-agent system simulation methods are established. The scenario simulations about low-carbon strategy competition,emergence and evolution are carried out. The results show that structure of complex network, heterogeneous expectation and decision behavior of agents are significant to low-carbon strategy's emission. The key factors to spread the new strategy are heterogeneity agents' low-carbon preference, network' external effect, cost and technological compatibility of strategy.

Key words: traditional industrial cluster, complex networks, evolutionary game, low carbon evolution

中图分类号:

F069.9

张宏娟, 范如国. 基于复杂网络演化博弈的传统产业集群低碳演化模型研究[J]. 中国管理科学, 2014, 22(12): 41-47.

ZHANG Hong-juan, FAN Ru-guo. Research on Low-Carbon Evolutionary Model of Traditional Industrial Clusters Based on Evolutionary Games Theory on Complex Networks[J]. Chinese Journal of Management Science, 2014, 22(12): 41-47.

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