ToRealSim Towards realistic computational models of social influence dynamics

ToRealSim 迈向社会影响动态的现实计算模型

• 批准号: 396901899
• 负责人: Professor Dr. Klaus Boehnke
• 金额: --
• 依托单位: Complex Systems Lab (LISC)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/396901899?language=en
• 关键词: ToRealSim; Towards; realistic; computational; models; ToRealSim; Towards; realistic; computational; models

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.

最近，许多社会转向了观点上的两极分化和波动性，例如在对移民或气候政策的态度方面，但理解的根本原因是很少的。一个关键的障碍是，社会中的意见动态涉及社会影响力的基本人际关系过程，网络结构（例如网络结构）和宏观成果（例如意见分布中的两极分化）之间的复杂微麦克罗相互作用。基于代理的仿真模型（ABM）提供了强大的工具，可以弥合理论上的微观流程和宏观级别的动态。但是，迄今为止，社会影响力的ABM未能为实证研究和政策提供信息。两个主要的障碍是1）缺乏各种模型的理论整合以及2）缺乏模型假设和预测的经验测试。 Torealsim是ABM领先专家和经验研究的一项独特的努力，以克服这两个障碍。结合了关于突出建模方法的专业知识，我们开发了一个系统地比较模型及其预测的框架。利用我们在定性研究，实验，社交网络分析和调查研究方面的专业知识，一种混合​​方法将模型与经验证据联系起来。我们从微观的假设和宏观预测方面进行了经验测试模型，提出了新一代的社会影响模型，该模型综合了Torealsim的见解。