An Artificial Intelligence/Machine Learning Perspective on Social Simulation

This chapter reviews the current state of the art in data infrastructure and artificial intelligence approaches that could be valuable for social and behavioral modeling. Among the newer machine learning methods, adversarial training and fuzzy cognitive maps seem to have particular unrealized potential. The chapter then discusses the troublesome theory-data gap: the mismatch between measurable data streams and meaningful explanatory theories to frame the data. The chapter identifies this issue as a key barrier to meaningful social and behavioral modeling. It then discusses the need to move from purely data-driven work to theory-informed work and to tighten the iterative loop between theory and data analysis. Closing the theory-data gap is a general problem. The chapter illustrates three example models that attempt to integrate theory-informed and data-driven modeling: a network model, a factor-tree model, and a fuzzy cognitive map model. The first model addresses meme transmission. The last two address the public support for terrorism. The chapter identifies key questions and challenges along the way. These are questions that a notional social and behavioral modeling research community will need to tackle as it grows.