We will begin by reading chapters 1, 2, 6, and 8 of Brian Skyrms' book Choice and Chance. These chapters survey and begin to hone many of the central logical and analytical tools we will need for the remainder of the course. In particular, these chapters outline many important relationships between deductive logic, inductive logic, and probability. Our subsequent discussions of confirmation and explanation will presuppose an understanding of much of the material in these chapters. See my lecture notes and my list of paper topics (weeks 1-2) for highlights of some of the key issues and questions raised in these chapters.

Next, we will move on to the course reader. We will (more or less) cover this material in the order in which it appears in the reader. This means we will begin with the chapter entitled "The Confirmation of Scientific Hypotheses", by Wesley Salmon and John Earman. I will not discuss all of this chapter in my lectures. But, I recommend that you read this chapter in its entirety (it's quite a good survey!). I will focus mainly on sections 2.1--2.4, 2.9, and 2.10. The exercises at the end of this chapter are also quite useful. Many of these would serve quite well as short paper topics (or as exam questions!) for the course.

Then, we will discuss the chapters (in the course reader) from John Earman's Bayes or Bust. This material largely overlaps with the Salmon and Earman chapter. But, Earman covers it in much more depth and with may more references to the literature. My discussion will focus on the problem of old evidence (chapter 5), as well as various "success stories" (chapter 3) of Bayesian confirmation, and how it improves on previous accounts.

Now, we're moving on to Scientific Explanation. In particular, we will be reading the early chapters of Salmon's Four Decades of Scientific Explanation. This will include (at least) discussion of the Deductive-Nomological (D-N) and the Inductive-Statistical (I-S) models of explanation. We will also probably begin to discuss the Statistical-Relevance (S-R) model of explanation as well. This will lead into discussions of important issues concerning the nature of probability and causality in science and scientific inference.

The last two weeks of the course will cover interpretations of probability, the pragmatics of explanation, and the scientific realism/empiricism debate in philosophy of science.