Jointly sponsored by the Depts. of Computer Science and Philosophy

Many philosophers of science have argued that a set of evidence that is "coherent" confirms a hypothesis which explains such coherence.   In this talk, we examine the relationships between probabilistic models of all three of these concepts: coherence, confirmation, and explanation.  For coherence, we consider Shogenji's measure of association (deviation from independence).  For confirmation, we consider several measures in the literature, and for explanation, we turn to Causal Bayes Nets and resort to causal structure and its constraint on probability.  All else equal, we show that focused correlation (Wheeler 2009), which is the ratio of the coherence of evidence and the coherence of the evidence conditional on a hypothesis, tracks confirmation.  We then show that the causal structure of the evidence and hypothesis can put strong constraints on how coherence in the evidence does or does not translate into confirmation of the hypothesis. Our results suggest how to reset the discussion of Bayesian coherentism within formal epistemology, for once we control for the role that causal structure plays in probabilistic models of coherence, we can see that the impossibility results have a much more limited scope than generally noted.