Thursday, April 29, 2010

Postdoc at Caltech

Caltech has a two year postdoctoral fellowship funded through the McDonnell Collaborative on Causal Learning. Candidates should work in some area of philosophy or psychology that makes contact with the topic of causal learning. The terms of the fellowhsip are very generous. Details may be found here.

Wednesday, April 7, 2010

*Newton and Empiricism*

Saturday and Sunday, 10-11 April 2010
Center for Philosophy of Science
817 Cathedral of Learning
University of Pittsburgh, Pittsburgh , PA USA
Everyone welcome. Registration is requested but not required. To register, please contact pittcntr@pitt.edu.

Saturday Morning
8:30 Continental Breakfast
9:00 Katherine Dunlop, Brown University:, "Apriorism and Empirical Science in Barrow and Newton’s Metaphysics (of Space and Time)"
10:15 Coffee
10:30 Mary Domski, University of New Mexico, "Newton’s Empiricism in Cartesian Context: Revisiting the Argument for Space in De Gravitatione"
11:45 Lunch
Saturday Afternoon
1:15 Matthew Priselac, University of North Carolina, "Newton on Substance"
2:30 Coffee
2:45 Ori Belkind, University of Richmond,"The Divisibility Criterion in Locke and Newton"
4:00 Coffee
4:15 Invited Speaker: Lisa Downing, Ohio State University, "Locke's Metaphysics and Newtonian Metaphysics"

Sunday Morning
8:30 Continental Breakfast
9:00 Tammy Nyden, Grinnell College, "Living force at Leiden"
10:15 Coffee
10:30 Yoram Hazony, Shalem Center, "Newton and Hume: A Reappraisal"
11:45 Lunch
Sunday Afternoon
1:15 Geoff Gorham, Macalester College, "Locke and Newton on Time and Space"
2:30 Coffee
2:45 Erik Curiel, London School of Economics, "On Newton’s Third Rule of Reasoning in Philosophy"
4:00 Coffee
4:15 Chris Smeenk, University of Western Ontario, "Quantitative Empiricism"
Discussants:
Philip Catton
Robert DiSalle
Ed Slowik
David Miller
Gordon Steenbergen
Hylarie Kochiras

Program Committee:
Zvi Biener, Western Michigan University
J.E. McGuire, University of Pittsburgh
Eric Schliesser, Ghent University

Sponsors
Center for Philosophy of Science, University of Pittsburgh

Thursday, April 1, 2010

Mathematical models and philosophical progress.

One of the most attractive features of The Reasoner, www.thereasoner.org the montly zine out of Kent, is the interview this month with Hannes Leitgeb. One passage caught my attention:

"Hannes Leitgeib: Overall, and ultimately, mathematical methods are necessary for philosophical progress, yes. But of course there can be points in a philosophical argumentation at which there is no payoff applying such methods. And while I do not think that there is any area of philosophy that is ‘beyond mathematical methods’, in some areas they do not pay off as yet because these areas are not quite developed enough. Or that’s at least the diagnosis of a mathematical philosopher!"

Let's grant for the sake of argument that mathematical methods have a distinguished record of progress. (In the interview Leitgeib does not offer a historical argument for the claim, but surely we can point to the history of analytic philosophy with some satisfaction.) Let's also grant that all areas of philosophy can benefit from mathematical methods.
But what could the (mathematical???) argument be that mathematical methods are *necessary* for philosophical progress? What to make of un-mathematical philosophy; is all the progress achieved without mathematical methods merely apparent?

And...let's accept that philosophical progress by mathematical methods ought to be understood in terms of "clarity" (as Leitgeib seems to suggest in the interview). Ought we to accept that it is cost-free?
Here are some possible costs within philosophy (I created the list while thinking of the role of Bayesianism as an aid to understanding scientific practice in the fields I am familiar with):
1. Focus on tractable machinery and toy-examples (at expense of complexity)
2. Training in technical skill at expense of good judgment
3. Inflated expectations from technique rather than learning how to ask right questions (or the making of distinctions)
4. Focus on producing 'results' rather than insight
5. Focus on the model and not the messy world