发表机构
Central European University; Université Paris-Saclay; CEA; CNRS; Institute of Complex Systems, CNR-ISC; Saha Institute of Nuclear Physics; Sciences Po; University of Berkeley; Yukhnovskii Institute for Condensed Matter Physics, National Academy of Sciences of Ukraine; Warsaw University of Technology; Indian Institute of Technology Indore; KAIST; George Washington University; University of Oxford; Christian Albrechts University of Kiel; Universitá degli Studi di Palermo; University of Maribor; Aalto University; University of Western Australia; Institute of Science Tokyo; Trinity College Dublin; East China University of Science and Technology（中欧大学; 巴黎萨克雷大学; 法国原子能和替代能源委员会; 法国国家科学研究中心; 意大利国家研究委员会复杂系统研究所; 萨哈核物理研究所; 巴黎政治学院; 加州大学伯克利分校; 乌克兰国家科学院尤赫诺夫斯基凝聚态物理研究所; 华沙理工大学; 印度理工学院印多尔分校; 韩国科学技术院; 乔治华盛顿大学; 牛津大学; 基尔大学; 巴勒莫大学; 马里博尔大学; 阿尔托大学; 西澳大学; 东京科学大学; 都柏林圣三一学院; 华东理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文宣言性回顾社会物理学领域，强调其跨学科量化研究集体行为的方法，总结其进展与局限，并提出以理论-数据互惠、因果解释和负责任治理为核心的未来发展纲领。
AI 中文摘要
社会物理学寻求对集体人类行为的量化、可实证检验的解释。其名称有着悠久且充满争议的历史，但其当代纲领既不是声称社会在字面意义上是物理系统，也不是试图用物理学取代社会科学。它是一种跨学科实践：观察与实验、模型构建、数学与计算分析，以及反复与数据对照。该领域已因数字痕迹分析、网络科学、基于智能体的建模、大规模实验以及跨经济、社会、技术、生物和物理层研究耦合动力学的能力而发生变革。我们回顾了金融分析与建模的进展；经济复杂性；社交网络的结构与演化；传播、观点和信息动力学；不平等、合作、冲突、流动性、城市、文化、科学和集体行为；以及新兴的人机生态系统研究。在这些领域中，常见特征反复出现——异质性、交互、反馈、适应、非平衡动力学、多尺度组织和涌现的集体结果。我们还直面该领域出现的局限性：便利样本、平台依赖性、因果识别薄弱、过度普适的主张、对意义和制度参与的不足、隐私风险，以及预测模型成为操纵工具的危险。我们提出一个未来的社会物理学，围绕理论-数据互惠性；因果和生成性解释；测量有效性和跨情境泛化；负责任、参与式的数据治理；真正的跨学科合作；以及对改变被观察系统的干预措施和算法的反思性研究来组织。
英文摘要
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.
Comments35 pages Typo in the authors' list corrected
