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)

The main aim of this article is to start a discussion of social pattern, a term that is commonly used in sociology but not specified or defined. The key question can be phrased as follows: Is it possible to transform the notion of social pattern from its current status in sociology as a proto-concept into a fully worked out concept? And if so, how can this be done? To provide material for the discussion we begin by introducing a few different (...) types of patterns that are currently being used (patterns in nature, cultural patterns, statistical patterns, and computationally generated patterns). This is followed by a suggestion for what a strictlysociologicalconcept of social pattern may look like. A useful and theoretically solid concept of social pattern can in our view be constructed by basing it on Weber’s concept of social action. This means thatboththe behavior of the actorsandthe meaning these invest their behavior with must be taken into account. The article ends with a brief discussion of how to use the concept of social patterns in an effective way and what may endanger such a use. (shrink)

In recent years, scholars in the social sciences and humanities have turned their attention to how the rise of digital technologies is reshaping political life in contemporary society. Here, we analyze this issue by distinguishing between two classification technologies typical of pre-digital and digital eras that differently constitute the relationship between individuals and groups. In class-based systems, characteristic of the pre-digital era, one’s status as an individual is gained through membership in a group in which salient social identities are shared (...) in common with other group members. In attribute-based systems, characteristic of the digital era, one’s status as an individual is determined by virtue of possession of a set of attributes that need not be shared with others. We argue that differences between these two types of classification technologies have important implications for how persons attach (or fail to attach) to groups, and therefore what kinds of political mobilization are possible. We illustrate this argument by examining contention over the use of gender as a variable in the pricing of risk in insurance and credit – two markets in which individuals directly encounter class-based and attribute-based systems of classification, respectively. (shrink)

This article examines what the rise in machine learning systems might mean for social theory. Focusing on financial markets, in which algorithmic securities trading founded on ML-based decision-making is gaining traction, I discuss the extent to which established sociological notions remain relevant or demand a reconsideration when applied to an ML context. I argue that ML systems have some capacity for agency and for engaging in forms of collective machine behaviour, in which ML systems interact with other machines. However, ML-based (...) collective machine behaviour is irreducible to human decision-making and thereby challenges established sociological notions of financial markets. I argue that such behaviour can nonetheless be analysed through an adaptation of sociological theories of interaction and collective behaviour. (shrink)