ORA (Round 5) Towards realistic computational models of social influence dynamics

最近，许多社会的观点（例如对移民或气候政策的态度）变得更加两极分化和不稳定，但人们对其根本原因知之甚少。一个关键的障碍是，社会舆论动态涉及复杂的微观-宏观相互作用，其中包括社会影响的基本人际过程、中观条件（如网络结构）和宏观结果（如舆论分布的两极分化）。基于智能体的仿真模型（ABM）为微观过程和宏观动态之间的理论桥梁提供了强大的工具。然而，迄今为止，社会影响的ABM未能为实证研究和政策提供信息。两个主要的障碍是：1)缺乏对大量模型的理论整合；2)缺乏对模型假设和预测的实证检验。ToRealSim是由ABM和实证研究领域的领先专家组成的联盟为克服这两个障碍而做出的独特努力。结合突出建模方法的专业知识，我们开发了一个框架来系统地比较模型及其预测。在此基础上，混合方法将利用我们在定性研究、实验、社会网络分析和调查研究方面的专业知识，将模型与经验证据联系起来。我们从微观层面的假设和宏观层面的预测两方面对模型进行了实证检验，提出了新一代的社会影响模型，该模型综合了来自ToRealSim的见解。

Recently, many societies shifted towards more polarization and volatility in opinions, for example in attitudes about immigration or climate policy, but underlying reasons are poorly understood. A key obstacle is that opinion dynamics in society involve a complex micro-macro interaction between fundamental interpersonal processes of social influence, meso-level conditions like network structures, and macro-level outcomes, like polarization in opinion distributions. Agent-based simulation models (ABM) offer powerful tools to bridge theoretically micro-level processes and macro-level dynamics. However, ABM of social influence hitherto fails to inform empirical research and policy. Two major roadblocks are 1) a lack of theoretical integration of the large variety of models and 2) a lack of empirical tests of model assumptions and predictions. ToRealSim is a unique effort by a consortium of leading experts in ABM and empirical research to overcome both roadblocks. Combining expertise on prominent modelling approaches, we develop a framework to systematically compare models and their predictions. Drawing on this, a mixed-method approach will link models to empirical evidence, utilizing our expertise on qualitative research, experiments, social network analysis and survey research. We test models empirically, both in terms of micro-level assumptions and macro-level predictions, proposing a new generation of social influence models that synthesizes insights from ToRealSim.