“From Probability to Consilience: How Explanatory Values Implement Bayesian Reasoning”, 2020-10-23 (; similar):
Recent experiments show that we value explanations for many reasons, such as predictive power and simplicity.
Bayesian rational analysis provides a functional account of these values, along with concrete definitions that allow us to measure and compare them across a variety of contexts, including visual perception, politics, and science.
These values include descriptiveness, co-explanation, and measures of simplicity such as parsimony and concision. The first two are associated with the evaluation of explanations in the light of experience, while the latter concern the intrinsic features of an explanation.
Failures to explain well can be understood as imbalances in these values: a conspiracy theorist, for example, may over-rate co-explanation relative to simplicity, and many similar ‘failures to explain’ that we see in social life may be analyzable at this level.
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.
[Keywords: explanation, explanatory values, Bayesian cognition, rational analysis, simplicity, vice epistemology]