摘要
将过度反应理论引入多智能体仿真模型,设计个体采纳互联网产品的交互方式,从演化博弈视角构建在线社交网络中互联网产品扩散仿真模型。在仿真软件Anylogic6.4.1上进行多Agent模拟实验,模拟结果表明:(1)信息服务产品收益越高,产品扩散会在更早的时间点大幅波动,产品扩散的水平越高,但同时稳定性较差,有扩散大幅萎缩的风险;(2)当传播成本较大时,拒绝者在大多数时间占优,当惩罚成本较大时,采纳者会在大多数时间占优;(3)过度反应行为越强烈,扩散稳定性越差,但扩散机会越大;(4)网络中的信息噪声越小,扩散水平越高。通过观察个体对待互联网信息服务产品的采纳和拒绝行为,研究各项效用参数对于网络中产品扩散结果的影响,为互联网产品供应商进行广告营销提供一定的参考。
The overreaction theory is introduced into a multi-agent simulation model,individual adoption Internet product interaction method is designed,and an Internet product diffusion simulation model in online social networks is constructed from the perspective of evolutionary games.Multi-agent simulation experiments were performed on the simulation software Anylogic 6.4.1.The results show that:(1)The higher the income of information service products,the product diffusion will fluctuate significantly at an earlier time,and the higher the level of product diffusion,but at the same time stability is poor and diffusion may shrink significantly;(2)when the transmission cost is large,the rejector will be dominant most of the time;when the penalty cost is large,the receiver will be dominant most of the time;(3)the stronger the overreaction,the worse the diffusion stability,but the greater the chance of diffusion;(4)the smaller the information noise in the network,the higher the diffusion level.Through observing individual’s acceptance and rejection of Internet information service products and studying the effects of various utility parameters on the results of product diffusion in the network,certain reference for Internet product suppliers in advertising marketing is provided.
作者
危小超
宋琳
李锋
WEI Xiao-Chao;SONG Lin;LI Feng(School of Economics, Wuhan University of Technology, Wuhan 430000, China)
出处
《北京邮电大学学报（社会科学版）》
2020年第6期1-10,共10页
Journal of Beijing University of Posts and Telecommunications(Social Sciences Edition)
基金
国家自然科学基金(71601151)
中国博士后基金特别资助(2018T110814)
中国博士后基金面上资助(2014M552102)
教育部人文社科基金(16YJC630131)。
关键词
进化博弈
过度反应
多智能体仿真
互联网产品扩散
evolutionary game
overreaction
multi-agent simulation
Internet product diffusion
