Social Physics, or the Interpretative Turn? Debating the Sciences of Society after AI
DOI:
https://doi.org/10.60923/issn.1971-8853/25832Keywords:
Philosophy and Methodology of Social Sciences, AI, Social Physics, ReflexivityAbstract
During the last few decades, new areas of research have emerged at the interface between the social sciences and computational sciences, such as computational social science and digital sociology. While there is significant willingness to engage across disciplines to establish these areas of inquiry, there equally exists widespread disagreement about what foundational concepts and methodological principles should guide their development. In the trading zone (Galison, 2010) between social and computational sciences, new possibilities, conflicts, and fundamental questions arise. Some argue that the combination of big data and machine learning will finally make possible the discovery of social laws (Lazer et al., 2021). Others claim that the AI revolution will unleash a new era of sociological theory and will enable the development of a qualitative computational science of society (Borch & Pardo-Guerra, 2024). In the 19th century, statistics and sociology found joint origins in the new methodology of social physics. In the wake of social media, attempts were made to reinvent this tradition, with mixed success (Watts, 2016). More recently, “AI” has been welcomed as an important opportunity for the renewal of interpretative social science (Friese, 2023; Törnberg & Uitermark, 2025). Can the wide uptake of machine-learning-based techniques across fields provide occasions to bring these debates and initiatives to maturity? Can we operationalize core questions and methodologies by combining social and computational theory and methods in new ways?
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