Tuesday, 21 March 2006
Modeling in Philosophy of Science
London School of Economics & UC Irvine
, 817R Cathedral of Learning
are a principal instrument of modern science.
They are built, tested, compared, and revised in the laboratory,
and subsequently, introduced, applied and interpreted in an expansive
literature. Throughout this talk, I will argue that
models are also a valuable tool for the philosopher of science. In particular, I will discuss how the
methodology of Bayesian Networks can elucidate two central problems
in the philosophy of science.
The first thesis I will explore
is the variety-of-evidence thesis, which argues that the more varied
the supporting evidence, the greater the degree of confirmation
for a given hypothesis. However,
when investigated using Bayesian methodology, this thesis turns
out not to be sacrosanct.
In fact, under certain conditions, a hypothesis receives
more confirmation from evidence that is obtained from one rather
than more instruments, and from evidence that confirms one rather
than more testable consequences of the hypothesis.
The second challenge that I will investigate is scientific
theory change. This
application highlights a different virtue of modeling methodology.
In particular, I will argue that Bayesian modeling illustrates
how two seemingly unrelated aspects of theory change, namely the
(Kuhnian) stability of (normal) science and the ability of anomalies
to over turn that stability and lead to theory change, are in fact
united by a single underlying principle, in this case, coherence.
In the end, I will argue that these two examples bring out some
metatheoretical reflections regarding the following questions: What
are the differences between modeling in science and modeling in
philosophy? What is
the scope of the modeling method in philosophy?
And what does this imply for our understanding of Bayesianism?
will present “Unification and Coherence”at
Mellon University, Thursday, March 23, 4:30, A
53 Baker Hall
the last donut?