Abstract

In this chapter, we review research on computational approaches to scientific discovery, starting with early work on the induction of numeric laws before turning to the construction of models that explain observations in terms of domain knowledge. We focus especially on inductive process modeling, which involves finding a set of linked differential equations, organized into processes, that reproduce, predict, and explain multivariate time series. We review the notion of quantitative process models, present two approaches to their construction that search through a space of model structures and associated parameters, and report their successful application to the explanation of ecological data. After this, we explore the relevance of process models to the social sciences, including the reasons they seem appropriate and some challenges to discovering them. In closing, we discuss other causal frameworks, including structural equation models and agent-based accounts, that researchers have developed to construct models of social phenomena.