Social Physics in the Age of Generative AI: How, Exactly, Is Society Complex?
DOI:
https://doi.org/10.60923/issn.1971-8853/24409Keywords:
Complexity theory, Modeling, Artificial Intelligence (AI)Abstract
The aspiration for a social physics — a science that would reveal universal laws of social life analogous to those governing the natural world—has haunted the social sciences since Comte. Each new wave of quantitative and computational methods breathes fresh life into this vision. The current moment is no exception: advances in artificial intelligence and large language models have reinvigorated attempts to model society through the methods of social physics. This essay argues that these ambitions rest on a confusion about the nature of social complexity. Drawing a distinction between systems complexity and anthropological complexity, we identify the limitations of analyzing society as a complex system akin to natural systems. One crucial obstacle for social physics is the analysis of meaning and interpretation. Generative artificial intelligence (GenAI) offers important opportunities to model language and reflexivity, inviting and perhaps compelling social physics to confront meaning and interpretation as constitutive features of the social world. At the same time, GenAI leaves untouched and may even aggravate social physics’ failure to grasp power, institutions, and inequality.References
Andersson, C., Törnberg, A., & Törnberg, P. (2014). Societal Systems — Complex or Worse? Futures, 63, 145–157. https://doi.org/10.1016/j.futures.2014.07.003
Ball, P. (2012). Why Society is a Complex Matter. Berlin: Springer.
Batty, M. (2014). The New Science of Cities. Cambridge, MA: MIT Press.
Byrne, D., & Callaghan, G. (2022). Complexity Theory and the Social Sciences: The State of the Art. London: Routledge.
Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. London, UK: Polity.
Castellani, B., & Gerrits, L. (2024). The Atlas of Social Complexity. Cheltenham, UK: Edward Elgar Publishing.
Castellano, C., Fortunato, S., & Loreto, V. (2009). Statistical Physics of Social Dynamics. Reviews of Modern Physics, 81(2), 591–646. https://doi.org/10.1103/RevModPhys.81.591
Christakis, N.A., & Fowler, J.H. (2013). Social Contagion Theory: Examining Dynamic Social Networks and Human Behavior. Statistics in Medicine, 32(4), 556–577. https://doi.org/10.1002/sim.5408
Collins, R. (2004). Interaction Ritual Chains. Princeton, NJ: Princeton University Press.
Connell, R. (2007). Southern Theory: The Global Dynamics of Knowledge in Social Science. Cambridge, UK: Polity. https://doi.org/10.4324/9781003117346
Eriksen, T.H. (2017). What is Anthropology? (2nded.). London: Pluto Press.
Geertz, C. (1973). The Interpretation of Cultures. New York: Basic Books.
Giddens, A. (1984). The Constitution of Society: Outline of the Theory of Structuration. London: Polity.
Haraway, D. (1988). Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective. Feminist Studies, 14(3), 575–599. https://doi.org/10.2307/3178066
Harding, S. (1986). The Science Question in Feminism. Ithaca, NY: Cornell University Press.
Hayles, N.K. (1999). How We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics. Chicago: University of Chicago Press.
Hui, Y. (2016). The Question Concerning Technology in China: An Essay in Cosmotechnics. Falmouth, UK: Urbanomic.
Larooij, M., & Törnberg, P. (2025). Validation is the Central Challenge for Generative Social Simulation: A Critical Review of LLMs in Agent-based Modeling. Artificial Intelligence Review, 59(1), 15. https://doi.org/10.1007/s10462-025-11412-6
Medina, E. (2011). Cybernetic Revolutionaries: Technology and Politics in Allende’s Chile. Cambridge, MA: MIT Press.
Noble, S.U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.
Park, J.S., O’Brien, J., Cai, C.J., Morris, M.R., Liang, P., & Bernstein, M.S. (2023). Generative Agents: Interactive Simulacra of Human Behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (pp. 1–22). Association for Computing Machinery, New York, NY. https://doi.org/10.1145/3586183.3606763
Pentland, A. (2014). Social Physics: How Good Ideas Spread — The Lessons from a New Science. New York: Penguin.
Santos, B.de S. (2014). Epistemologies of the South: Justice Against Epistemicide. London: Routledge.
Schelling, T.C. (1971). Dynamic Models of Segregation. Journal of Mathematical Sociology, 1(2), 143–186. https://doi.org/10.1080/0022250X.1971.9989794
Scott, J.C. (1998). Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. New Haven, CT: Yale University Press.
Törnberg, P., & Uitermark, J. (2021). For a Heterodox Computational Social Science. Big Data & Society, 8(2), 1–12. https://doi.org/10.1177/20539517211047725
Törnberg, P. & Uitermark, J. (2025). Seeing Like a Platform. An Inquiry into Digital Modernity. London: Routledge.
Törnberg, P., & Uitermark, J. (2026). From the Platform Society to the AI Society: Towards Critical Studies of Generative AI. https://osf.io/preprints/socarxiv/qahd3_v1
Törnberg, P., Valeeva, D., Uitermark, J., & Bail, C. (2023). Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms. arXiv. https://doi.org/10.48550/arXiv.2310.05984
Tufekci, Z. (2017). Twitter and Tear Gas: The Power and Fragility of Networked Protest. New Haven, CT: Yale University Press.
West, G. (2017). Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms, Cities, Economies, and Companies. London: Penguin.
Wigner, E.P. (1960). The Unreasonable Effectiveness of Mathematics in the Natural Sciences. Communications on Pure and Applied Mathematics, 13(1), 1–14. https://doi.org/10.1002/cpa.3160130102
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Petter Törnberg, Justus Uitermark
This work is licensed under a Creative Commons Attribution 4.0 International License.
