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作 者:谢丽 赵培忻[1] 丁海欣[2] Xie Li;Zhao Peixin;Ding Haixin(School of Management,Shandong University,Jinan 250100,China;School of Tourism Management,Zhengzhou University,Zhengzhou 450001,China)
机构地区:[1]山东大学管理学院,山东济南250100 [2]郑州大学旅游管理学院,河南郑州450001
出 处:《科技进步与对策》2021年第11期10-18,共9页Science & Technology Progress and Policy
基 金:国家自然科学基金青年项目(71802183);教育部人文社会科学研究青年基金项目(18YJC630200);河南省教育厅人文社会科学研究一般项目(2020-ZZJH-449)。
摘 要:创新扩散发生于社会系统中并通常与社会系统存在交互作用。网络是体现社会系统的重要手段,但已有研究中的扩散网络多为无向网络且具有静态或外生动态性质,上述处理方式忽视或无法体现扩散与以网络形式出现的社会系统的协同演化以及个体间的不对称影响。基于有向网络,提出能够体现创新扩散与网络协同演化的描述性框架,从信息搜寻与降低认知失调视角出发,基于信息熵与累积优势机制,在个体层面构造网络演化与创新采纳数学模型。在Repast Simphony 2.7开发平台下,利用基于Java的智能体建模方法展开系统仿真实验。基于实验数据的描述性结果揭示,动态网络与静态网络下的创新扩散存在显著不同,网络结构演化将强化节点出度的不均衡状况,更优的扩散结果往往与更不均衡的节点出度分布同时出现,其它因素包括大众传播与人际传播等也会影响扩散与网络的协同演化;交互作用存在于控制变量间。推断性结果确认了控制变量影响与交互作用的普遍存在。Innovation diffusion happens in social system;diffusion and social system always interact with each other.Network is an important way to depict this social factor;however,static or exogenously dynamic undirected innovation diffusion networks in literatures always either neglect or cannot reflect the coevolution of diffusion and social system embodied by network,and fail to capture the asymmetry of influence.Based on directed network,this study puts forward a conceptual frameworks to capture the coevolution of diffusion and network;following the perspectives of information searching and cognitive dissonance reduction,and based on information entropy and cumulative advantage,this study constructs mathematical models for network evolution and innovation adoption at the individual level.Based on the Repast Simphony 2.7 development platform,this study chooses a Java-based Agent-based Modeling approach to conduct comprehensive simulation experiments.Experiment data-based descriptive results reveal:innovation diffusions under dynamic and static network are significantly different,the evolution of network structure could increase the inequity of node out-degree,a better innovation result always comes with a more unequal node out-degree distribution;and other factors,including mass communication and personal communication,would also affect the coevolution of diffusion and network;and interactions exist among control variables.Referential results confirm the influences of control variables and the prevalence of interactions.
关 键 词:创新扩散 有向网络 信息熵 累积优势 智能体仿真
分 类 号:F091.354[经济管理—政治经济学]
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