论文标题
从概率到合伙性：解释价值如何实施贝叶斯推理
From Probability to Consilience: How Explanatory Values Implement Bayesian Reasoning
论文作者
论文摘要
认知科学的最新工作发现了各种各样的解释价值或我们认为解释更好或更糟的维度。我们提出了贝叶斯对这些价值如何符合指导解释的说明。由此产生的分类法提供了一组预测因素，人们对人们更喜欢的解释，并展示了心理学，统计学和科学哲学的核心价值观如何从共同的数学框架中出现。除了操作与科学争论相关的解释性美德外，该框架还使我们能够重新解释推动阴谋论，妄想和极端主义意识形态的解释性恶习。
Recent work in cognitive science has uncovered a diversity of explanatory values, or dimensions along which we judge explanations as better or worse. We propose a Bayesian account of how these values fit together to guide explanation. The resulting taxonomy provides a set of predictors for which explanations people prefer and shows how core values from psychology, statistics, and the philosophy of science emerge from a common mathematical framework. In addition to operationalizing the explanatory virtues associated with, for example, scientific argument-making, this framework also enables us to reinterpret the explanatory vices that drive conspiracy theories, delusions, and extremist ideologies.
