**
Equilibrium Selection Via Natural Learning**

**Georgios Piliouras**

Cornell, Department of Computer Science** **** **

**M**onday February 23, 2009

4:00 PM, 5130 Upson Hall

__Abstract:__

Game theory is saturated with plausible equilibrium concepts. Which is the "right" one?
Arguably, the most coveted one is that of Nash equilibria. However, even if we choose
to concentrate on them, the multiplicity of Nash poses a similar dilemma. Which Nash is
the right one?

We explore this question from the perspective of learning theory. Can players reach Nash
(and which one) while applying simple and natural learning algorithms? Our results imply
exponential improvement on the performance bounds of well-known learning protocols in wide
classes of games.

This is joint work with Robert Kleinberg and Eva Tardos (STOC 09).