There is a long tradition of fruitful interaction between
philosophy and the sciences. Logic and statistics emerged,
historically, from combined philosophical and scientific inquiry
into the nature of mathematical and scientific inference; and 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.
In 2013, the Department of
Philosophy at Carnegie
Mellon University will hold a three-week summer school in
logic and formal epistemology for promising undergraduates in
philosophy, mathematics, computer science, linguistics, economics, and
other sciences. The goals are to
The summer school will be held from Monday, June 3 to
Friday, June 21, 2013. There will be morning and afternoon
lectures and daily problem sessions, as well as planned
outings and social events.
The summer school is free. That is, we will
So students need only pay for round trip travel to Pittsburgh and
living expenses while here. We expect to be able to accept about 25
students in 2013. There are no grades, and the courses do not
provide formal course credit.
The summer school 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 is a $30 nonrefundable application fee.
Applications are due by Friday, March 15, 2013. Please help us
spread the word. There is a flyer that
is suitable for distributing, framing, or hanging on an office door,
and a plain-text announcement.
The Topology of Inquiry
Monday, June 3 to Friday, June 7
Instructor: K.T. Kelly
The standard mathematical frameworks for understanding reasoning are logic and computability for mathematical reasoning and probability theory for empirical reasoning. In this summer school session, we examine an alternative, topological viewpoint according to which computational and empirical undecidability can both be viewed as reflections of topological complexity. That may sound a bit odd, since topology is usually understood to be "rubber geometry", or the study geometrical relationships preserved under stretching operations that neither cut nor paste pieces together. In fact, topology is better understood as studying the mathematical structure of epistemic verifiability. Topological concepts and results will be applied to provide a unified, explanatory perspective on empirical underdetermination, formal undecidability, and the elusive connection between simplicity and empirical truth.
1) "The Logic of Success"
British Journal for the Philosophy of Science, special millennium issue, 51, 2001, 639-666.
2) Several overview papers are available on my web-site:
(with O. Schulte) "Church's Thesis and Hume's Problem," in Logic and Scientific Methods, M. L. Dalla Chiara, et al., eds. Dordrecht: Kluwer, 1997, pp. 383-398.
3) "Justification as Truth-finding Efficiency: How Ockham's Razor Works", Minds and Machines 14: 2004, pp. 485-505.
An Introduction to the theory of Rational Choice: Games and Decisions
Monday, June 10 to Friday, June 14
Instructor: Teddy Seidenfeld
Office: Baker Hall 135-J
How to choose rationally? One perspective on a choice problem is to frame it with a single decision maker, you, and to consider what follows from the simple, prudential consideration that you should not pass up sure-gains. That is, you should not choose option 1 if you are sure to do better under option 2 regardless which state of nature obtains. Another perspective on choice is to frame a problem as involving multiple decision makers (who may have competing goals with your own), and to consider what follows from the equally simple prudential consideration that you should not permit the opponents to take advantage of you. That is, you should not play the game in a way that allows the opponents to make you worse off than you might be by playing differently, particularly when they benefit at your expense by doing so. These two perspectives on how to choose are not exclusive. A decision problem where you are uncertain about which state of nature obtains can be converted into a 2-person (zero-sum) game where you play against Nature. In this component of the summer school, we will examine decision theory and game theory in order to understand what each has to teach us about choosing rationally.
Graphical Causal Models
Monday, June 17 to Friday, June 21
Instructor: Richard Scheines
In the late 1980s and early 1990s, philosophers, computer scientists, and statisticians developed a graph theoretic representation of causal systems, which, when connected to probability through a few simple axioms, broke down a century of statistical dogma characterized by the slogan: "correlation isn't causation." This representation, called
"graphical causal models," or "causal Bayes nets," has produced an explosion of rigorous work on topics such as:
a) when are causal structures empirically (in)distinguishable,
b) are there tractable algorithms for searching an astronomically large space of causal structures,
c) can these algorithms be proved reliable, and in what sense?,
d) is it possible to tell if a "causal" parameter can be estimated from data, even if the model is unknown,
e) can we compute the shortest sequence of experiments required to identify causal structure, even in the presence of unmeasured, or hidden variables, and
f) can we characterize when ordinary regression analysis can be used to get at causal hypotheses.
In this week, I will gently introduce graphical causal models, show how they provide a unified representation for causal modeling in disciplines as diverse as economics, biology, sociology, psychology, and educational research, and touch on each of the topics above. By extensive use of Tetrad (state-of-the-art causal modeling software) and the Causality Lab
(educational software to simulate empirical research on causal questions), we will learn "hands-on" exactly how much more there is to the topic of causation than "correlation is not causation."
1) A long and interactive introduction to the pre-cursor concepts for
graphical causal models is available at:
Choose "Peek-In" - and browse through the course material.
2) Several overview papers are available on my web-site:
http://www.hss.cmu.edu/philosophy/scheines/cv.htm under the section:
"Causation and Statistics-Reviews/Handbook/Encyclopedia Articles."
A particularly gentle, but dated, introduction is the paper "An
Introduction to Causal Inference."
How to apply
Thanks for your interest, but the application deadline for the 2012 Summer School has passed!
Please see the Information page to find out about travel, accommodations, schedule, etc.
The summer school was launched in 2006. The National Science
Foundation provided substantial funding in 2006 and 2007, and partial funding for 2009, 2010 and 2011. You may also view:
The summer school is directed by Teddy Seidenfeld.
Inquiries may be directed to teddy[at]stat[dot]cmu[dot]edu.