Course Description:
The goal of this class is to provide a broad and rigorous introduction to the theory, methods and algorithms of multi-agent systems. The material spans disciplines as diverse as engineering (including control theory and signal processing), computer science (including artificial intelligence, algorithms and distributed systems), micro-economic theory, operations research, public policies, psychology and belief systems. A primary focus of the course is on the application of cooperative and non-cooperative game theory for both static and dynamic models, with deterministic as well as stochastic descriptions. The coverage will encompass both theoretical and algorithmic developments, with multi-disciplinary applications.
Grading:
Homework: 30%
Midterm Exam: 30%
Term Project: 40%
Required Text:
[BO] T. Başar and G.J. Olsder, Dynamic Noncooperative Game Theory, 2nd edition, Classics in Applied Mathematics, SIAM, Philadelphia, 1999
[GO] G. Owen, Game Theory, 3rd edition, Academic Press, 1995
[FT] D. Fudenberg and J. Tirole, Game Theory, MIT Press, 1991.
[RB] R. Gibbons, Game Theory for Applied Economists, Princeton University Press, 1992.
Additional References:
[RI] R. Isaacs, Differential Games, Kruger, NY, 2nd ed., 1975 (First edition: Wiley, NY, 1965).
[VM] J. von Neumann and O. Morgenstern, Theory of Games and Economic Behavior, Princeton University Press, Princeton, NJ, 2nd ed., 1947 (first edition: 1944).
[VB] T. L. Vincent and J. S. Brown, Evolutionary Game Theory, Natural Selection, and Darwinian Dynamics, Cambridge University Press, Cambridge, England, 2005.
[BB] T. Başar and P. Bernhard, H-infinity Optimal Control and Related Minimax Design Problems: A Dynamic Game Approach, 2nd edition, Birkhäuser, Boston, MA, August 1995.
[CBL] N. Cesa-Bianchi and G. Lugosi, Prediction, Learning, and Games, Cambridge University Press, 2006.
[MOJ] M. O. Jackson, Social and Economic Networks, Princeton University Press, 2010
[OR] M. J. Osborne and A. Rubinstein, A Course in Game Theory, MIT Press, 1994
[DBP] D. P. Bertsekas, Dynamic Programming and Optimal Control, Athena Scientific; 4th edition, 2007
[VK] V. Krishna, Auction Theory, Second Edition, Academic Press, 2009
Course Outline:
Lecture 1: Introduction and zero-sum finite static games
Lecture 2: Zero-sum and nonzero-sum finite static games
Lecture 3: Extensive games, Stackelberg games and mechanism design
Lecture 4: Bayesian games and auction theory
Lecture 5: Learning in games and socio-economic networks
Lecture 6: Repeated games
Lecture 7: Infinite continuous-kernel games and resource allocation games
Lecture 8: Cooperative games and bargaining
Lecture 9: Optimal control and dynamic games
Lecture 10: Deterministic dynamic games and cooperative control
Lecture 11: Stochastic dynamic games and security games
Lecture 12: Differential games and pursuit and evasions
Lecture 13: Large population games and mean-field games
Homeworks:
Exams: