Summer School in Logic and Formal Epistemology
There is a long tradition of fruitful interaction between philosophy and the sciences.
Logic and statistics emerged, historically, from the combined philosophical and scientific inquiry into the nature of mathematical and scientific inference; the modern conceptions of psychology, linguistics, and computer science are the results of sustained reflection on the nature of mind, language, and computation. In today's climate of disciplinary specialization, however, foundational reflection is becoming increasingly rare. As a result, developments in the sciences are often conceptually ill-founded, and philosophical debates often lack scientific substance and rigor.
The Department of Philosophy at Carnegie Mellon University hosts a summer school in logic and formal epistemology for promising undergraduates in philosophy, mathematics, computer science, linguistics, economics, and other sciences. During this three-week, intensive program, we introduce a small group of approximately 25 promising students to cross-disciplinary fields of research at an early stage in their career, forging lasting links between these disciplines along with friendships and professional contacts. Topics change from year to year, with daily sessions taught by CMU Philosophy faculty members and guest lectures sometimes offered by other professionals, grad students, and even summer school alumni.
The summer school is free: there is no tuition, and on-campus housing is provided at no cost.
The Summer School in Logic and Formal Epistemology is open to undergraduates, as well as to students who will have just completed their first year of graduate school. Applicants need not be US citizens. There are no grades, and the courses do not provide formal course credit. There is a $36 nonrefundable application fee.
Applications close on March 14, 2023 (1:00 pm EST).
June 5 - 9, 2023
A tour of linguistics
Abstract: This course introduces foundational topics in modern linguistics. We begin with speech sounds and look at how different languages organize sounds into abstract systems. Then we look at how larger linguistic units are built up, how meaning is grafted onto sounds, and how meaningful elements are combined into larger expressions. These investigations take us through major sub-disciplines of linguistics, from phonetics and phonology, to morphology and syntax, and onto semantics and pragmatics. Throughout, the focus is on language as a complex system of interlocking parts, and on the techniques and strategies by which linguists discover and analyze these parts.
June 12 -16, 2023
Two approaches to knowing and believing: epistemic logic and imprecise probabilities
Abstract: The first part of the week will introduce epistemic logic, a branch of modal logic concerned with reasoning about knowledge and belief. No background in modal logic will be assumed. We'll motivate the development of the formal tools, survey some classic results in the field, and consider some extensions of the basic framework, such as: multi-agent systems and common knowledge, public announcements, and topological approaches. In the second part of the week we'll motivate and explore some uses of imprecise probability theory to model aspects of uncertainty that elude the framework of precise (subjective) probability theory. These include formal models of cooperative group decision making and formal models of robustness of inductive inference.
June 19 - 23, 2023
Chance and randomness
Abstract: Probability theory plays a crucial role in science, from inductive learning and statistical inference to information theory, to economics and decision theory, to physics, just to name a few. It also gives rise to some of the deepest and most captivating philosophical puzzles. Are probabilities in the world or in our heads? What, if anything, is the relationship between objective chances, frequencies, and subjective degrees of belief? Can there be non-trivial objective probabilities in a universe governed by deterministic physical laws? Should a theory of probability be grounded in a prior account of randomness or, vice-versa, should we think of randomness as requiring a prior understanding of probabilities? Can computational considerations shed light on our concepts of chance and randomness? In this course, we will explore these questions and more.