ACSEE: Antagonistic Crowd Simulation Model With Emotional Contagion and Evolutionary Game Theory

人群心理 情绪传染 人群模拟 心理学 社会心理学 博弈论 计算机科学 电子游戏 认知心理学 计算机安全 人群 微观经济学 经济 多媒体
作者
Chaochao Li,Pei Lv,Dinesh Manocha,Hua Wang,Yafei Li,Bing Zhou,Mingliang Xu
出处
期刊:IEEE Transactions on Affective Computing [Institute of Electrical and Electronics Engineers]
卷期号:13 (2): 729-745 被引量:42
标识
DOI:10.1109/taffc.2019.2954394
摘要

Antagonistic crowd behaviors are often observed in cases of serious conflict. Antagonistic emotions, which is the typical psychological state of agents in different roles (i.e., cops, activists, and civilians) in crowd violence scenes, and the way they spread through contagion in a crowd are important causes of crowd antagonistic behaviors. Moreover, games, which refers to the interaction between opposing groups adopting different strategies to obtain higher benefits and less casualties, determine the level of crowd violence. We present an antagonistic crowd simulation model (ACSEE), which is integrated with antagonistic emotional contagion and evolutionary game theories. Our approach models the antagonistic emotions between agents in different roles using two components: mental emotion and external emotion. We combine enhanced susceptible-infectious-susceptible (SIS) and game approaches to evaluate the role of antagonistic emotional contagion in crowd violence. Our evolutionary game theoretic approach incorporates antagonistic emotional contagion through deterrent force, which is modelled by a mixture of emotional forces and physical forces defeating the opponents. Antagonistic emotional contagion and evolutionary game theories influence each other to determine antagonistic crowd behaviors. We evaluate our approach on real-world scenarios consisting of different kinds of agents. We also compare the simulated crowd behaviors with real-world crowd videos and use our approach to predict the trends of crowd movements in violence incidents. We investigate the impact of various factors (number of agents, emotion, strategy, etc.) on the outcome of crowd violence. We present results from user studies suggesting that our model can simulate antagonistic crowd behaviors similar to those seen in real-world scenarios.
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