[Colloquium] Sham Kakade talk today 12:15 at TTI

[Meridel Trimble]

Toyota Technological Institute at Chicago talk

Speaker’s name: Sham Kakade
University of Pennsylvania
Speaker 's Homepage: http://www.cis.upenn.edu/~skakade/

Date: Tuesday, March 16th, 2004 
Time: 12:15pm
Place: TTI-C (1427 E. 60th St. – 2nd Floor)
Lunch Provided

Title: Deterministic Calibration and Nash Equilibrium

Abstract: The most central question for justifying any game theoretic
equilibrium as a general solution concept is: can we view the equilibrium as a
convergent point of a sensible learning process? In this talk, I'll provide a
natural learning process in which the frequency of empirical play converges into
the set of convex combinations of Nash equilibria. The learning process is the
most traditional one: players make predictions of their opponents and take best
responses to their predictions. The key distinction is that in this learning
process players use "public" predictions formed by an accurate (eg calibrated)
prediction algorithm. This sensibility of the setting rests on a technical
result in which we establish the existence of a deterministic (ie public)
prediction algorithm that is weakly calibrated.

This setting deals with the coordination problem of ``which Nash equilibrium to
play?'' in a natural manner. The setting does not arbitrarily force play to any
single equilibrium and allows the possibility that players could (jointly)
switch play from one Nash equilibrium to another --- perhaps infinitely often.
Furthermore, although play converges to the convex combinations of Nash
equilibria, we have the stronger result that the public forecasts themselves are
frequently close to some Nash equilibrium (not general combinations of them). Of
course if the Nash equilibrium is unique, then the empirical play converges to it.

This is joint work with Dean Foster

If you have questions, or would like to meet the speaker, please contact Meridel
at mtrimble at tti-c.org/4-9873

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