Computational Analysis of Collective Behaviors via Agent-Based Modeling

Agent-based modeling (ABM) is a common computational analysis tool to study system dynamics. In the framework of ABM, the system consists of multiple autonomous and interacting agents. We can explore emergent collective patterns by simulating the individual operations and interactions between agents. As a case study, we present an experiment using an agent-based model to study how competition for limited user attention in a social network results in collective patterns of meme popularity. The model is inspired by the long tradition that represents information spreading as an epidemic process, where infection is passed along the edges of the underlying social network. The model also builds upon empirical observations on how individual humans behave online. The combination of social network structure and finite agent attention is sufficient for the emergence of broad diversity in meme popularity and lifetime. The case study illustrates how one can analyze the kind of emergent human computation that makes some memes very popular.