Instructor: Kevin T. Kelly.
Time: 1:30 AM to 3:50 AM Wed
Room DH 4303
Office 135 K BH.
Office hours: 1:00 – 2:00 PM Tuesday and Thursday or by appt.
Text: online scanned papers linked to this page.
Epistemology is traditionally the study of the
nature of knowledge and associated concepts including belief,
justification, evidence, truth, testimony, and coherence. Most
contemporary work in epistemology is already logically structured, up
to a penumbra of vagueness that is sometimes intentional and that
sometimes represents a lack of contact with logical and probabilistic
details. Formal epistemology aims to improve the situation in
Epistemology via rigorous application of potentially relevant
mathematical tools, most notably, Bayesian decision theory and
conditioning, epistemic modal logic, belief revision theory, and formal
learning theory. Traditional epistemologists are curious whether
and to what extent the excursion into mathematical detail will really
improve the situation, rather than merely shifting the focus from
genuine questions to merely "technical" ones.
Since there is no graduate course on Epistemology in the department, we will begin with a rapid review of some classic papers in epistemology. Then we will review some recent, central results in formal learning theory. We will start with probabilistic belief and Bayesian epistemology. Then we will consider propositional belief states, modal epistemic logic, and belief revision theory. Finally, we will examine attempts to connect probabilistic with propositional belief, focusing on the lottery paradox and theories of acceptance.
One major issue that has received less attention in formal epistemology than in standard epistemology is the relationship between justification and truth. Standard epistemic logic and Bayesian credal states say little about how justification relates to truth. I am interested in addressing that lacuna by paying more attention to the concept of truth-conduciveness, itself.