In this paper, we sketch a programme for AI-driven social theory. We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our specification for how AI-based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI-based models can (...) help synthesise knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI-driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI-systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful. These are (1) semanticisation, i.e., the ability to develop and operationalize verbal concepts to represent machine-manipulable knowledge; (2) transferability, i.e., the ability to transfer what has been learned in one context to another; and (3) generativity, i.e., the ability to independently create and improve on concepts and models. We argue that if the gaps identified here are addressed by further research, there is no reason why, in the future, the most advanced programme in social theory should not be led by AI-driven cumulative advances. (shrink)

The image of Max Weber as an “interpretivist” cultural theorist of webs of significance that people use to cope with a meaningless world reigns largely unquestioned today. This article presents a different image of Weber’s sociology, where meaning does not transport actors over an abyss of meaninglessness but rather helps them navigate a world of Chance. Retrieving this concept from Weber’s late writings, we argue that the fundamental basis of the orders sociologists seek to understand is not chaos. Action is (...) rendered interpretable, rather, to the extent persons orient themselves to possibilities and probabilities, which remain real but unknowable. In this framework, interpretation and probability are allies, not antagonists. Weber’s systematic use of the probabilistic notion of Chance as a central resource for concept formation and sociological explanation seriously challenges current understandings of probability as a purely statistical, atheoretical concern. We outline the conceptual difference it makes when basic categories of sociology are understood as rooted in Chance, and we point to the larger implications of Weber’s probabilism for contemporary debates around issues of prediction, action, and interpretation. (shrink)

Using a unique data set of causal usage drawn from research articles published between 2006–2008 in the American Journal of Sociology and American Sociological Review, this article offers an empirical assessment of causality in American sociology. Testing various aspects of what we consider the conventional wisdom on causality in the discipline, we find that “variablistic” or “covering law” models are not the dominant way of making causal claims, research methods affect but do not determine causal usage, and the use of (...) explicit causal language and the concept of “mechanisms” to make causal claims is limited. Instead, we find that metaphors and metaphoric reasoning are fundamental for causal claims-making in the discipline. On this basis, we define three dominant causal types used in sociology today, which we label the Probabilistic, Initiating and Conditioning types. We theorize this outcome as demonstrating the primary role that cognitive models play in providing inference-rich metaphors that allow sociologists to map causal relationships on to empirical processes. (shrink)