Computation and Learning in Economic Networks

Network models for game theory and economics provide a powerful framework for studying strategic interactions in large population settings. The semantics of these networks are that nodes represent parties (e.g. players or consumers) and edges represent strategic or economic interactions. These models allow the incorporation of rich structure into the network, allowing the promise of increased applicability of strategic reasoning to large, complex systems.

In this talk, I will present algorithms and learning models for game theoretic and economic equilibria --- focusing on how the network structure influences the learning process and the outcomes. This work highlights many natural connections to AI and modern probabilistic modeling. I will also provide results at the intersection of this line of study and topics in social network theory.

This is joint work with Dean Foster, Michael Kearns, John Langford, Luis Ortiz, Robin Pemantle, and Siddharth Suri.