Lecture slides
I'll update these as we go along.
1. Introduction (Chapter 1)
 Normal form, pure strategies, and mixed strategies.
 Utilities/payoffs, relation to rational preferences, human risk preferences.
 Payoffbased classifications of games (zerosum, nonzerosum, symmetric, etc.)
 Many examples.
2. Analyzing normalform games (Chapter 2)
 Pareto optimality, best responses, Nash equilibria,
 Examples: the Prisoner's Dilemma, Battle of the Sexes, penalty kicks in soccer, Braess' Paradox (road network design), and others.
3. More about normalform games (Chapter 3)
 Maximin and minimax strategies, Minimax theorem
 Dominance, elimination of dominated strategies, dominant strategy equilibria
 Rationalizability
 Correlated equilibria, convex combinations
 Tremblinghand perfect equilibria, epsilonNash equilibria
 Evolutionary stability
 Examples: Battle of the Sexes, Morra, Prisoner's Dilemma, pbeauty contest, HawkDove game
4a. Extensiveform games (Chapter 4)
 Extensive form, subgameperfect equilibria, and backward induction
 Examples: the Centipede Game, and others
4b. Gametree search (not in the book)
 Minimax search, alphabeta pruning, boundeddepth search, static evaluation functions.
 Enhancements (node ordering, quiescence search, transposition tables, etc.)
 Monte Carlo rollouts
 Examples: chess, checkers, tictactoe, and go
4c. Lookahead pathology
(not in the book)
 Games in which deeper gametree saerch produces worse decisions
 Relationships to characteristics of the game tree
 Examples: Pgames, Ngames, chess, and kalah
Homework assignment for Lectures 4a, 4b, and 4c.
Review for the midterm exam
5. Imperfectinformation games (Chapter 5)
 Information sets, sequential equilibria, behavioral strategies versus mixed strategies
 Imperfectinformation gametree search, opponent modeling
 Examples: bridge, poker, kriegspiel chess, and several others
6a. Repeated games (Chapter 6, sections 1 and 2)
 Finitely and infinitely repeated games, strategies for such games
 Differences between theoretical predictions and empirical results
 Opponent modeling and noise filtering
 Examples: Roshambo, the Iterated Prisoner's Dilemma (with and without noise), and the infantry trenches in World War I
6b. Stochastic games (Chapter 6, section 3)
 Markov games, reward functions, strategies, and equilibria
 Evolutionary simulation games, imitation dynamics
 Statedependent risk preferences
 Examples will include Backgammon, Cultaptation, lottery games, and evolution of statedependent risk preferences
Homework assignment for Lectures 6a and 6b
7a. Incompleteinformation games (Chapter 7)
 Comparison with imperfect information
 Relation to uncertainty about payoffs
 Bayesian games, extensive games with chance moves, and BayesNash equilibria
 English, Dutch, and sealedbid auctions
 Many examples
Homework assignment for Lecture 7
7b. Cultaptation
8. Coalitional games
(Chapter 8)
 Classes of coalitional games (superadditive, additive, convex, simple, etc.)
 Payoff sets, imputation sets, Shapley value, core, stability
 Examples: voting games, probably others.
Review for the final exam
Schedule for the rest of the semester
 Nov 11  Homework 6, start Chapter 8
 Nov 16  Homework 7, finish Chapter 8
 Nov 18  ?
 Nov 23  Homework 8, review
 Nov 25  no class (Thanksgiving)
 Nov 30  termproject presentations
 Dec 2  termproject presentations
 Dec 7  review
 Dec 9  lecture by Inon Zuckerman

Term projects
Private materials
Some proofs of the Minimax Theorem
Syllabus
This course will provide a comprehensive introduction to game theory. It will count as a core course for the Computer Science PhD and MS requirements.
Game theory is about interactions among agents (either human or computerized) that each have their own objectives and preferences  which may differ from the objectives and preferences of other agents. Game theory has applications in fields ranging from economics and evolutionary biology to engineering and computer science. Some examples of computer science applications include computer networking, ecommerce, and computer gameplaying.
Location: 1122 CSIC
Time: Tuesday/Thursday, 3:30–4:45pm
Instructor: Dana Nau
 Office: Room 3241 AVW
 Office hours: after class until about 5:30pm; other times by appointment
 Telephone: 3014052684
 Email: nau & cs.umd.edu (change & to @)
Class page: follow the link my home page or from the CS Department's list of class pages.
Discussion forum: web site and RSS feed
 That's where I'll post announcements to the class.
 Consider posting your questions and comments there, instead of emailing them to me.
Textbook: LeytonBrown and Shoham, Essentials of Game Theory. Morgan & Claypool, 2008.
I'll make additional resources available online.
Topics to be covered
 In the lefthand column are a preliminary list of topics, and preliminary versions of the lecture slides.
 I'll update both of those as we go along.
Workload
Homework: I'll assign several sets of homework problems, but won't grade them. About a week after each assignment, I'll discuss the answers in class.
Term projects: Each of you will need to do a term project. It will be like a miniature version of the research projects that most of you will need to do repeatedly throughout your career. It will include
the following (the dates are tentative):
 a written proposal, due Oct 1;
 an inclass presentation of your proposal, during Oct 5–12;
 a written report on the results of your project, due Nov 27 (or for 10% off, Nov 29);
 an inclass presentation of your report, during Nov 30–Dec 7.
I haven't yet decided whether the term projects will be done individually or by small groups; this will depend partly on the class size.
Exams: There will be two exams:
 I've tentatively scheduled the midterm exam for Tuesday, October 19.
 According to the university exam schedule, the final exam will be on Saturday, December 18, at 10:30am.
 Both exams will be open book and open notes. A few days before each exam, I'll do an inclass review of the material we've covered, to help you prepare for the exam.
Grading
The term project will count for 30% of your grade. The two exams will count for 70% of your grade. When you take the final exam, I'll let you choose among any of the following:
 20% for the midterm and 50% for the final;
 30% for the midterm and 40% for the final (roughly proportional to the length of each exam);
 40% for the midterm and 30% for the final.
