Game Theoretic Solutions and How to Compute Them


When multiple self-interested agents act in the same environment, what
is optimal for one agent to do generally depends on what the other
agents do.  Example environments are numerous but include auctions,
elections, and games such as poker.  Game theory provides a number of
solution concepts that prescribe how each agent should act in such a
setting.  In this tutorial, we will review several of these concepts,
including dominance, iterated dominance, minimax strategies, Nash
equilibrium, and Stackelberg strategies.  We will also review some
basic algorithms for computing these solutions.  No prior background
in game theory is required.


Vincent Conitzer is an Assistant Professor of Computer Science and
Economics at Duke University. His research focuses on computational
aspects of microeconomics, in particular game theory, mechanism
design, voting/social choice, and auctions; his work also involves
techniques from artificial intelligence and multiagent systems. He
received Ph.D. (2006) and M.S. (2003) degrees in Computer Science from
Carnegie Mellon University, and an A.B. (2001) degree in Applied
Mathematics from Harvard University. He also received an Alfred P.
Sloan Research Fellowship (2008), an Honorable Mention for the 2007
ACM Doctoral Dissertation Award, the 2006 IFAAMAS Victor Lesser
Distinguished Dissertation Award, the AAMAS Best Program Committee
Member Award (2006), and an IBM Ph.D. Fellowship (2005). He is a
co-author on papers winning a AAAI-08 Outstanding Paper Award and the
AAMAS-08 Pragnesh Jay Modi Best Student Paper Award.