Constructing an Agent-Based Model of Networks in Social Movements
The social movement is an important part of the modern society of the United States. One question has persisted in social movement research: how can a movement succeed or fail? Past studies showed that "network" results in dynamic mobilization for collective action in social movement events, which is critical to the success or failure of a movement ((Diani 1992; Knoke et al. 2021)). Collective action is defined as "two or more persons engaged in one or more behaviors that can be judged common or convergent on one or more dimensions" (McPhail and Wohlstein, 1983). To illustrate the collective action, the researchers used computer simulation to animate the aggregate motion of bird flocks, showing that the interactions between the members of the network result in the following collective action of the bird flocks. Members of the social movement have personal networks that influence their participation in collective actions, which could further affect the outcomes of movement events (Petrov et al. 2021). Therefore, in this project, we constructed an Agent-Based model to simulate how a network recruits others to join the event through the connections/links of the network. An agent-based model is a computer simulation in which artificial agents are randomly assigned with a set of simple rules for behavioral responses to their environment. Agent actions are influenced by their interactions with other agents, generating unprogrammed patterns of collective action outcomes. Our model is able to vary the number of the actors, the chances the actors are recruited, and the chances the actors won't be recruited. Conducting the experiment repetitively could generate the statistical result to see how many agents are recruited and not recruited.