摘要
基于过度自信和演化博弈,构建多Agent模拟模型,研究网络舆情传播规律。从演化博弈视角构建网络舆情传播模型。结合过度自信理论,设计三种过度自信场景:收益过度自信、成本过度自信,以及收益和成本双重过度自信。进而设计综合考虑自身与邻居历史信息的学习规则,实现不同过度自信场景下舆情传播的多智能体仿真。结果表明:(1)收益过度自信越强,舆情传播收敛速度越快,用户群体更快达到稳定状态;(2)增强成本过度自信,采纳比例降低,群体策略从采纳占优转化为针锋相对;(3)相比收益,成本过度自信更有利于提高舆情扩散稳定性。本文尝试提出行为理论和仿真方法的集成框架,为网络舆情传播研究提供新思路。
Based on the overconfidence and evolutionary game,the multi-agent simulation model is constructed to study the public sentiment diffusion rules of online social networks.The online social network public sentiment diffusion model is constructed from the perspective of evolutionary game;combined with the theory of overconfidence,three overconfident scenarios are designed:benefit overconfidence,cost overconfidence,and dual overconfidence of cost and benefit.Then historical information learning rules of their own and neighbor is designed to achieve multi-agent simulation of public sentiment diffusion under different overconfident scenarios.The results show:1)The stronger the benefit overconfidence is,the faster the convergence speed of public sentiment diffusion is,and the faster the user group reaches a stable state.2)Increasing the cost overconfidence will result in the reduction of adoption proportion,and transformation of group strategy from adoption dominance to tit for tat.3)Compared with benefit overconfidence,cost overconfidence is more conducive to improving the stability of public sentiment diffusion.This research attempts to propose an integration framework for behavioral theory and simulation methods in order to provide new ideas for public sentiment diffusion research of online social networks.
作者
危小超
范玉瑶
WEI Xiao-chao;FAN Yu-yao(School of Economics, Wuhan University of Technology, Wuhan 430070, China)
出处
《北京邮电大学学报（社会科学版）》
2020年第4期1-8,47,共9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)
武汉理工大学研究生优秀学位论文培育项目(2017-YS-085)。
关键词
舆情传播
多智能体模拟
演化博弈
过度自信
public sentiment diffusion
multi-agent simulation
evolutionary game
overconfidence
