Computational Social Science and Rational Choice

Computational social science is a rapidly growing field in social science. It deals with various types of digital footprint data such as mobile data, social media data, or data gathered by online experiments, and often relies on computationally intensive methods such as natural language processing or large-scale network analysis. In analyzing such data, it has answered novel research questions, such as about the structure of population-level networks, the dynamics of cultural markets. However, it is unexplored how computational social science contributes to the advancement of rational choice theory. Crucially, digital footprint data as studied in CSS often provide information about entire social systems, but at the same time give detailed insight in actions and decisions of individuals. Such data sources thus provide unique opportunities to study the interdependence between micro- and macrolevel phenomena, and to study individual decision making in social contexts. For this session we invite contributions that apply CSS approaches to further our understanding of human decisions making in social contexts, or conversely, apply explicit modeling of individual decision making in empirical computational social science. Any abstracts ranging from theoretical discussions between rational choice theory and computational social science to concrete studies using them are welcome.