Cooperative game theory spans the formation of coalitions among collaborative agents, as well as proposing reasonable payoff divisions among them. This branch of game theory is rooted in Von-Neumann & Morgenstern’s foundational work, with many beautiful theoretical ideas; however, it has seen relatively sparse application. In this talk, I will discuss several research thrusts which aim at making the theory of cooperative games more applicable; I will discuss how the introduction of overlapping coalition structures – i.e. allowing agents to divide their resources among more than one coalition – allows one to model complex agent interaction.

Moreover, I will show how one can overcome the computational challenges traditionally associated with finding cooperative solution concepts by relaxing our requirements. By looking for a probably approximately correct (PAC) solution, and applying ideas from computational learning theory, one can find good solutions to cooperative games while eliminating computational overhead.

Finally, I will discuss exciting directions for the study of cooperative games, both in the application of the theory to causality and classification, and in empirical human trials.

**BIO:**

Yair Zick is a postdoctoral research fellow in the computer science department at Carnegie Mellon University. He has completed his PhD at Nanyang Technological University, SPMS (funded by the Singapore A*STAR SINGA award). He received his B.Sc (Mathematics and the "Amirim" honors program) from the Hebrew University of Jerusalem. His research interests are game theory, fair division and their applications to domains such as machine learning, security, and privacy. He is the recipient of the 2014 IFAAMAS Victor Lesser Distinguished Dissertation Award, and the 2011 Pragnesh Jay Modi Best Student Paper Award.