Department of Psychology, City, University of London, London EC1V 0HB, UK.
School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK.
Proc Biol Sci. 2021 Feb 10;288(1944):20202957. doi: 10.1098/rspb.2020.2957. Epub 2021 Feb 3.
Bayesian inference offers an optimal means of processing environmental information and so an advantage in natural selection. We consider the apparent, recent trend in increasing dysfunctional disagreement in, for example, political debate. This is puzzling because Bayesian inference benefits from powerful convergence theorems, precluding dysfunctional disagreement. Information overload is a plausible factor limiting the applicability of full Bayesian inference, but what is the link with dysfunctional disagreement? Individuals striving to be Bayesian-rational, but challenged by information overload, might simplify by using Bayesian networks or the separation of questions into knowledge partitions, the latter formalized with quantum probability theory. We demonstrate the massive simplification afforded by either approach, but also show how they contribute to dysfunctional disagreement.
贝叶斯推断提供了一种处理环境信息的最优方法，因此在自然选择中具有优势。我们考虑了一个明显的、最近的趋势，即在政治辩论等方面，功能失调的分歧正在增加。这令人费解，因为贝叶斯推断得益于强大的收敛定理，排除了功能失调的分歧。信息过载是限制完全贝叶斯推断适用性的一个合理因素，但它与功能失调的分歧有什么联系呢？个体在努力实现贝叶斯理性的过程中，但受到信息过载的挑战，可能会通过使用贝叶斯网络或将问题分为知识分区来简化，后者可以用量子概率论来形式化。我们展示了这两种方法所带来的巨大简化，但也展示了它们如何导致功能失调的分歧。
