Bayesian network models of political polarisation

信念两极分化的案例，即个体在使用相同的信息后，观点变得更加不同，通常被归因于动机推理。然而，政治心理学以外的研究表明，认知的贝叶斯网络模型可能会在唯一动机是准确性的代理人中产生信念两极分化。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，这项研究项目的目标是确定贝叶斯网络模型，这些模型可以可信地解释现实世界中政治信仰两极分化的案例，并测试这些模型是否提供了比动机推理描述更好的解释。贝叶斯网络对两极分化的解释的一个决定性特征是，他们假设个人不相信信息来源完全真实，无论是有意还是无意的。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。对偏见的归因可能有很多原因--例如，他们可能认为来源是为了传播他们知道不真实的错误信息，或者他们真诚地相信虚假信息是因为他们容易受骗。因此，该项目的另一个目标是加强对为什么以及何时将偏见归因于信息来源的理解。来源偏见的归因可能是有动机的推理的产物--人们将偏见归因于说了他们不想相信的话的人--或者党派之争--人们将偏见归因于群外成员，将诚实归因于群内成员。或者，可以通过人们根据他们对消息来源和他们所属的任何相关群体的偏见、他们的信息内容所暗示的偏见以及他们的政治信息环境中错误信息和有偏见的消息来源的普遍程度的信念做出合理的推断来解释它们。这将表明，人们暴露在其中的信息环境以及他们对信息环境的看法，对于理解两极分化非常重要，并可能有助于解释为什么不同国家、不同时代和不同政治制度的两极分化程度不同，这是基于动机推理和党派斗争难以解决的解释。通过贝叶斯网络的方法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理解，这是本研究项目的最终目的。希望这将起作用，建立能够指导未来去极化举措的研究结果。该项目将采用计算建模工作和实验相结合的方式。本研究主要从社会心理学和认知心理学的角度来考察文学，同时也借鉴了认识论和社会学的研究成果。

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.