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

## TL;DR

This paper introduces ACSEE, a novel crowd simulation model that integrates emotional contagion and evolutionary game theory to realistically simulate antagonistic crowd behaviors in violent scenarios.

## Contribution

The paper presents a new crowd simulation model combining emotional contagion and game theory, capturing complex antagonistic interactions and predicting crowd violence trends.

## Key findings

- Model effectively simulates real-world antagonistic crowd behaviors.
- Emotional contagion significantly influences crowd violence outcomes.
- Simulation results align with real-world crowd videos and user studies.

## Abstract

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 violent 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.