Beginning with the introduction of graphical games and related models, there is now a rich body of algorithmic connections between probabilistic inference, game theory and microeconomics. Strategic analogues of belief propagation and other inference techniques have been developed for the computation of Nash, correlated and market equilibria, and have played a significant role in the evolution of algorithmic game theory over the past decade.
There are also important points of departure between probabilistic and strategic graphical models — perhaps most notably that in the latter, vertices are not random variables, but self-interested humans or organizations. It is thus natural to wonder how social network structures might influence equilibrium outcomes such as social welfare or the relative wealth and power of individuals. One logical path that such questions lead to is human-subject experiments on strategic interaction in social networks.
Michael Kearns is Professor and National Center Chair in the Computer and Information Science department at the University of Pennsylvania. His research interests include topics in machine learning, algorithmic game theory, social networks, and computational finance. Prior to joining the Penn faculty, he spent a decade at AT&T/Bell Labs, where he was head of AI Research. He is co-director of Penn’s Warren Center for Network and Data Sciences (warrencenter.upenn.edu), and founder of Penn’s Networked and Social Systems Engineering (NETS) undergraduate program (www.nets.upenn.edu). Kearns consults extensively in technology and finance, and is a Fellow of the Association for the Advancement of Artificial Intelligence and the American Academy of Arts and Sciences.