From Probability to Consilience: How Explanatory Values Implement Bayesian Reasoning

TL;DR

This paper presents a Bayesian framework that unifies various explanatory values from psychology, philosophy, and statistics, predicting explanation preferences and illuminating the roots of scientific virtues and cognitive biases.

Contribution

It introduces a comprehensive taxonomy of explanatory values based on Bayesian reasoning, linking diverse criteria for good explanations within a single mathematical model.

Findings

Predicts explanation preferences based on Bayesian criteria

Unifies explanatory virtues across disciplines

Reinterprets explanatory vices in conspiracy theories and delusions

Abstract

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.