From Probability to Consilience: How Explanatory Values Implement Bayesian Reasoning
This work addresses the challenge of unifying explanatory values for researchers in cognitive science, philosophy, and related fields, offering a foundational framework that could influence explanation theory and applications.
The paper tackles the problem of explaining how diverse explanatory values in cognitive science fit together to guide explanation, proposing a Bayesian account that provides predictors for explanation preferences and shows how core values from psychology, statistics, and philosophy of science emerge from a common framework.
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.