标题： 意见动力学：统计物理与超越 显示英文标题

标题： Opinion dynamics: Statistical physics and beyond

摘要： 意见动力学，研究个体信念和集体公众意见如何演变，是将统计物理应用于复杂社会现象的一个富有成效的领域。 与物理系统类似，社会从局部相互作用中表现出宏观规律，导致共识或分裂等结果。 该领域发展迅速，吸引了跨学科的方法，并受到大规模行为数据激增的推动。 本综述涵盖了其快速进展，弥合了文献的分散性。 我们首先介绍基本概念和定义，包括意见的本质、微观和宏观动力学。 这一基础引导出实证研究的概述，从实验室实验到大规模数据分析，这些研究为意见动力学模型提供了信息并进行了验证。 然后我们介绍基于个体的模型，按其宏观现象（例如共识、极化、回音室）和微观机制（例如同质性、同化）进行分类。 我们还回顾了社会传播现象，强调它们与意见动力学的联系。 此外，综述涵盖了常见的分析和计算工具，包括随机过程、处理方法、模拟和优化。 最后，我们探讨了新兴前沿，例如将实证数据与模型联系起来，以及使用人工智能代理作为新型社会现象的测试平台。 通过系统化术语并强调与传统物理学的类比，本综述旨在整合知识，提供坚实的理论基础，并塑造意见动力学未来的研究方向。 显示英文摘要

摘要： Opinion dynamics, the study of how individual beliefs and collective public opinion evolve, is a fertile domain for applying statistical physics to complex social phenomena. Like physical systems, societies exhibit macroscopic regularities from localized interactions, leading to outcomes such as consensus or fragmentation. This field has grown significantly, attracting interdisciplinary methods and driven by a surge in large-scale behavioral data. This review covers its rapid progress, bridging the literature dispersion. We begin with essential concepts and definitions, encompassing the nature of opinions, microscopic and macroscopic dynamics. This foundation leads to an overview of empirical research, from lab experiments to large-scale data analysis, which informs and validates models of opinion dynamics. We then present individual-based models, categorized by their macroscopic phenomena (e.g., consensus, polarization, echo chambers) and microscopic mechanisms (e.g., homophily, assimilation). We also review social contagion phenomena, highlighting their connection to opinion dynamics. Furthermore, the review covers common analytical and computational tools, including stochastic processes, treatments, simulations, and optimization. Finally, we explore emerging frontiers, such as connecting empirical data to models and using AI agents as testbeds for novel social phenomena. By systematizing terminology and emphasizing analogies with traditional physics, this review aims to consolidate knowledge, provide a robust theoretical foundation, and shape future research in opinion dynamics.