

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 12) 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
2.12.4, 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
DeductiveNomological (DN) and the
InductiveStatistical (IS) models of explanation.
We will also probably begin to discuss the
StatisticalRelevance (SR) 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.

