LAUSR

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 these values that clarifies their function and shows how they fit together to guide explanation-making. The resulting taxonomy shows that core values from psychology, statistics, and the philosophy of science emerge from a common mathematical framework and provide insight into why people adopt the explanations they do. This framework not only operationalizes the explanatory virtues associated with, for example, scientific argument-making, but also enables us to reinterpret the explanatory vices that drive phenomena such as conspiracy theories, delusions, and extremist ideologies.