Social Physics: A manifesto
Abstract
Social Physics seeks quantitative, empirically testable explanations of collective human behavior. Its name has a long and contested history, but its contemporary program is neither the claim that society is literally a physical system nor an attempt to replace the social sciences with physics. It is an interdisciplinary practice: observation and experimentation, model construction, mathematical and computational analysis, and repeated confrontation with data. The field has been transformed by analyzing digital traces, network science, agent-based modeling, large-scale experiments, and the capacity to study coupled dynamics across economic, social, technological, biological, and physical layers. We review advances in financial analysis and modeling; economic complexity; the structure and evolution of social networks; spreading, opinion, and information dynamics; inequality, cooperation, conflict, mobility, cities, culture, science, and collective behavior; and the emerging study of human-AI ecosystems. Across these domains, common features recur - heterogeneity, interaction, feedback, adaptation, non-equilibrium dynamics, multiscale organization, and emergent collective outcomes. We also confront the limitations that have arisen in this field: convenience samples, platform dependence, weak causal identification, over-universal claims, insufficient engagement with meaning and institutions, privacy risks, and the danger that predictive models become instruments of manipulation. We propose a future Social Physics organized around theory - data reciprocity; causal and generative explanation; measurement validity and cross-context generalization; responsible, participatory data governance; genuinely interdisciplinary collaboration; and reflexive study of interventions and algorithms that alter the systems being observed.
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BibTeX
@misc{kertesz2026social,
title = {{Social Physics: A manifesto}},
author = {János Kertész and Marc Barthelemy and Guido Caldarelli and Bikas K. Chakrabarti and Tiziana Di Matteo and Serge Galam and Marta Gonzales and Yurij Holovatch and Janusz A. Holyst and Sarika Jalan and Hawoong Jeong and Neil F. Johnson and Renaud Lambiotte and Linyuan Lü and Thomas Lux and Rosario N. Mantegna and Matjaz Perc and Jari Saramäki and Michael Small and Misako Takayasu and Taha Yasseri and Wei-Xing Zhou},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.08117},
eprint = {2610.08117},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.08117},
}
RIS
TY - UNPB
TI - Social Physics: A manifesto
AU - János Kertész
AU - Marc Barthelemy
AU - Guido Caldarelli
AU - Bikas K. Chakrabarti
AU - Tiziana Di Matteo
AU - Serge Galam
AU - Marta Gonzales
AU - Yurij Holovatch
AU - Janusz A. Holyst
AU - Sarika Jalan
AU - Hawoong Jeong
AU - Neil F. Johnson
AU - Renaud Lambiotte
AU - Linyuan Lü
AU - Thomas Lux
AU - Rosario N. Mantegna
AU - Matjaz Perc
AU - Jari Saramäki
AU - Michael Small
AU - Misako Takayasu
AU - Taha Yasseri
AU - Wei-Xing Zhou
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.08117
UR - https://arxiv.org/abs/2610.08117
AB - Social Physics seeks quantitative, empirically testable explanations of collective human behavior. Its name has a long and contested history, but its contemporary program is neither the claim that society is literally a physical system nor an attempt to replace the social sciences with physics. It is an interdisciplinary practice: observation and experimentation, model construction, mathematical and computational analysis, and repeated confrontation with data. The field has been transformed by analyzing digital traces, network science, agent-based modeling, large-scale experiments, and the capacity to study coupled dynamics across economic, social, technological, biological, and physical layers. We review advances in financial analysis and modeling; economic complexity; the structure and evolution of social networks; spreading, opinion, and information dynamics; inequality, cooperation, conflict, mobility, cities, culture, science, and collective behavior; and the emerging study of human-AI ecosystems. Across these domains, common features recur - heterogeneity, interaction, feedback, adaptation, non-equilibrium dynamics, multiscale organization, and emergent collective outcomes. We also confront the limitations that have arisen in this field: convenience samples, platform dependence, weak causal identification, over-universal claims, insufficient engagement with meaning and institutions, privacy risks, and the danger that predictive models become instruments of manipulation. We propose a future Social Physics organized around theory - data reciprocity; causal and generative explanation; measurement validity and cross-context generalization; responsible, participatory data governance; genuinely interdisciplinary collaboration; and reflexive study of interventions and algorithms that alter the systems being observed.
ER -