Bayesian network models of political polarisation

政治极化的贝叶斯网络模型

• 批准号: 2427544
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2427544
• 关键词: Bayesian; network; models; political; polarisation

Cases of belief polarisation, where individuals' views become more divergent after consuming the same information, are often attributed to motivated reasoning. However, work outside political psychology has demonstrated that Bayesian network models of cognition can generate belief polarisation among agents whose only motivation is accuracy. These demonstrations have never been empirically tested within the domain of political psychology before. The goal of this research project, therefore, is to identify Bayesian network models which can plausibly explain real-world cases of political belief polarisation, and test whether these offer a better explanation than motivated reasoning accounts. One defining feature of Bayesian network explanations of polarisation is their assumption that individuals do not believe sources of information to be wholly truthful, whether intentionally or incidentally. A key factor here is whether people perceive sources of information to be biased. An attribution of bias might arise for numerous reasons - they might think the source is motivated to spread misinformation they know is untrue, or that they sincerely believe false information due to their gullibility, for instance. One further goal of the project, therefore, is to enhance understanding of why and when bias is attributed to information sources. Attributions of source bias could be the product of motivated reasoning - people attribute bias to people who say things they don't want to believe - or partisanship - people attribute bias to outgroup members and honesty to ingroup members. Or, they could be explained by people making reasonable inferences from their beliefs about the biasedness of the source and any relevant groups to which they belong, the biasedness implied by the content of their message, and how common misinformation and biased sources are in their political information environment. This would suggest that the information environment to which people are exposed, and their perception of it, is important for understanding polarisation, and might help explain why polarisation levels differ across countries, times, and political systems, something explanations grounded in motivated reasoning and partisanship struggle to address. Linking environmental-level factors to individual-level cognition through the lens of Bayesian network approaches, in pursuit of a fuller understanding of polarisation, is the ultimate aim of this research project. It is hoped that this will work will establish findings that can guide depolarisation initiatives in the future. The project will use a mixture of computational modelling work and experimentation. This research primarily considers literature within social psychology and cognitive psychology, but is also be informed by epistemology and sociological work.

信仰两极分化的情况下，个人的意见变得更加分歧后，消费相同的信息，往往归因于动机推理。然而，政治心理学之外的工作表明，贝叶斯网络认知模型可以在唯一动机是准确性的代理人之间产生信念极化。这些论证以前从未在政治心理学领域进行过实证检验。因此，本研究项目的目标是确定贝叶斯网络模型，这些模型可以合理地解释现实世界中的政治信仰两极分化的情况下，并测试这些是否提供了一个更好的解释比动机推理帐户。 贝叶斯网络对两极分化的解释的一个定义特征是他们假设个人不相信信息来源是完全真实的，无论是有意还是无意。这里的一个关键因素是人们是否认为信息来源是有偏见的。偏见的归因可能有很多原因-例如，他们可能认为消息来源有动机传播他们知道是不真实的错误信息，或者他们因为轻信而真诚地相信错误信息。因此，该项目的另一个目标是加强了解为什么以及何时将偏见归因于信息来源。 来源偏见的归因可能是动机性推理的产物--人们将偏见归因于那些说了他们不想相信的事情的人--或者党派偏见--人们将偏见归因于外群成员，将诚实归因于内群成员。或者，他们可以解释为人们从他们对消息来源和他们所属的任何相关群体的偏见的信念中做出合理的推断，他们的消息内容所暗示的偏见，以及错误信息和有偏见的消息来源在他们的政治信息环境中有多常见。这表明，人们所接触的信息环境以及他们对它的看法对于理解极化非常重要，并且可能有助于解释为什么极化水平在国家，时代和政治制度之间存在差异，这是基于动机推理和党派斗争的解释。 通过贝叶斯网络方法的透镜将环境层面的因素与个人层面的认知联系起来，以追求对极化的更全面的理解，是本研究项目的最终目标。希望这将工作将建立可以指导未来去极化倡议的发现。 该项目将使用计算建模工作和实验的混合物。本研究主要考虑社会心理学和认知心理学中的文献，但也受到认识论和社会学工作的影响。