Brian Epstein has recently argued that a thoroughly microfoundationalist approach towards economics is unconvincing for metaphysical reasons. Generally, Epstein argues that for an improvement in the methodology of social science we must adopt social ontology as the foundation of social sciences; that is, the standing microfoundationalist debate could be solved by fixing economics’ ontology. However, as I show in this paper, fixing the social ontology prior to the process of model construction is optional instead of necessary and that metaphysical-ontological commitments (...) are often the outcome of model construction, not its starting point. By focusing on the practice of modeling in economics the paper provides a useful inroad into the debate about the role of metaphysics in the natural and social sciences more generally. (shrink)

Metaphysics has undergone two major innovations in recent decades. First, naturalistic metaphysicians have argued that our best science provides an important source of evidence for metaphysical theories. Second, social metaphysicians have begun to explore the nature of social entities such as groups, institutions, and social categories. Surprisingly, these projects have largely kept their distance from one another. Katherine Hawley has recently argued that, unlike the natural sciences, the social sciences are not sufficiently successful to provide evidence about the metaphysical nature (...) of social entities. By contrast, I defend an optimistic view of naturalistic social metaphysics. Drawing on a case study of research into contextual effects in social epidemiology, I show that social science can provide a valuable evidence for social metaphysicians. (shrink)

Machine learning (ML) models recently led to major breakthroughs in predictive tasks in the natural sciences. Yet their benefits for the social sciences are less evident, as even high-profile studies on the prediction of life trajectories have shown to be largely unsuccessful – at least when measured in traditional criteria of scientific success. This paper tries to shed light on this remarkable performance gap. Comparing two social science case studies to a paradigm example from the natural sciences, we argue that, (...) in addition to explanation, prediction is an important goal of social science – and we identify constraints that impede pure ML prediction from being successful in that field. As a remedy, we outline elements of an integrative modelling approach that combines explanatory models and predictive ML models. (shrink)