On Modeling And Dynamics Analysis Of Social Crowd Behavior

Since crowd behavior was observed by Gustav LeBon in 1896, the mass behaviors and motion of crowds have been modeled and simulated with different application fields recently, such as computer game, public safety management, architecture, computer graphics, robotics, training system, and sociology so on. However, we apply crowd model and simulation technology to describing social crowd and their behaviors for the management and training of emergency response in military for specially.As we know, the society including large-scale components and large-scale rela-tionship is usually researched as the complex systems. Moreover complex networks theory, evolutionary game theory and Agent-based modeling theory has been adopted as the tools on complex system practically.The research on dynamics of collective be-havior is combined with evolutionary game theory on network, generalized synchro-nization on network and Agent-based modeling method. The microscopic interactions between goal-oriented individuals can result in the macroscopic-behavioral dynamics of crowd behaviors. By the tools above, spatial and temporal dynamics reveal fasci-nating global emergence phenomena and interesting patterns of group movement and autonomous behavioral development.The main content and contribution of this dissertation are summarized as follows:1. A novel definition of generalized synchronization on complex networks is intro-duced to understand the dynamics of crowd behavior. With two usual methods detecting generalized synchronization, two criteria of generalized synchronization on networks are advanced.2. The social behavior selection network (BSN), as an improved social behaviors, is modeled to understand the origin of dominant behaviors in social networks that gen-erally lead to emergence of crowd. For behavioral selection networks with complex networks topology, we demonstrate the existence of phase transition curves relating from diversity of behaviors to crowd behaviors. By virtue of changing the topological structure, the behavioral networks behave affluent dynamical phenomena, including the emergency of crowd behaviors. 3. The nonlinear dynamics of the behaviors selection networks model, by virtue of which we can understand the origin of flocking behaviors in social networks, is researched. For behavioral networks with different complex networks topology, the nonlinear dynamics including the chaotic dynamics by the numerical simulation tools are illuminated. With changing the structure of networks topology and the mutation matrix, the behavioral networks behave affluent dynamical phenomena.4. The Large-Scale Crowd Behavior Simulation prototype system platform for emergency response adopting an agent-based modeling approach is developed. The Agent model perceives the human being as a psychosomatic, autonomous acting crea-ture with emotion evolutionary capabilities that is embedded in a virtual dynamic social environment. The layered architecture of the prototype system involving in human be-havior in crisis situations is explained in detail. The model holds different internal states of human beings to support the decision making, which is composed of physi-cal, emotional, and social aspects. The emotion contagion model decides the modeling solutions for common environmental, socio-psychological and behavioral phenomena in the context of crisis, defines their interrelation and impact on an individual's in-ternal state and integrates them into a comprehensive modeling approach. The LSCS prototype system platform can be used to analyze, forecast and control the complex situations under emergent crisis.5. The relationship between Agent-based modeling (ABM) tools and Equation-based modeling (EBM) methods are clarified through modeling and simulating the crowd behavior. ABM emphasis the detail components of complex system using the bottom-up approach. Adversely, EBM can mainly model the total dynamics of the complex system using the top-down method. Fortunately we can integrate the two tools above to model the system by virtue of the complementarity of ABM and EBM.