“Games, Networks, and People”

Speaker: Professor Michael Kearns
Professor of Computer and Information Science
University of Pennsylvania
Founding Director, Penn program in Networked and Social Systems Engineering (NETS)
Founding Director, Warren Center in Network and Data Sciences

 

Talk Summary: 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.