Professor: Li, Hao, 604-822-6685, Iona Building 201C, email@example.com, https://lihao.microeconomics.ca/.
Course Canvas page: https://canvas.ubc.ca/courses/123668.
Classes: Tuesdays and Thursdays 5:00-6:30pm, at Buchanan B208.
Announcements: Check the Announcement section on the Canvas page regularly for updates on all things about the course.
Teaching Assistant: Lia Lorena Kale Ribeiro Braga (firstname.lastname@example.org). Every week Lia will conduct a one-hour tutorial session that you should have already signed up for. Lia will also hold weekly office hours, from 1-2pm every Wednesday at Iona 038.
Textbook: An Introduction to Game Theory, by Martin Osborne. Previously I have used Game Theory for Applied Economists by Robert Gibbons. Compared to the textbook by Osborne, the book by Gibbons has a less comprehensive treatment of game theory but focuses more on economic applications. The book by Osborne is required, and is available at the UBC bookstore, but some of the material in this course will be incorporated from the book by Gibbons in my lecture notes. Osborne and Rubinstein’s A Course in Game Theory contains some examples used in class but is a graduate level textbook. For the mathematically inclined, Fudenberg and Tirole’s graduate-level Game Theory is the ultimate source.
Office hours: Wednesdays 10:30am-12pm.
Goals: The main goal of this course is to introduce you to (non-cooperative) game theory as a bag of tools essential to modern economic analysis. The focus will be on tools and frameworks that are used in economic applications. Along the way, you will be expected to learn the most important concepts in game theory, and to develop your ability to model and analyze economic problems in general.
Math level: Calculus is required for this course. Prior knowledge of basic set theory and probability theory is helpful, but is not required as they will be explained in sufficient detail when it is necessary for understanding the course material. The textbook has an appendix (Chapter 17) that may be useful for a quick math review.
Evaluation: Your grade in the course will be based on your marks in 10 homework assignments, 1 midterm test and 1 final exam. The total weight of the assignments in the course grade is 10%, so each assignment is worth 1 point out of 100. I will post assignments on the Canvas page; most but not all the assignments are from the textbook. You will have two weeks to complete an assignment and submit it online on Canvas. After the assignments are submitted the answers will be posted. The TA will not correct the assignments, and will grade them according to how much effort was put in: 0 for no effort or very little effort, 0.5 for some but insufficient effort, and 1 for sufficient effort. The weight on your midterm is 40% and the weight on your final is 50%. If your score on the final (out of 100) is better than your score on the midterm (out of 100), the final score will count for 90%. The midterm is tentatively set to Thursday October 26 at 5pm in class. If for medical reasons or other emergencies you are unable to take the midterm, all 40% of the weight on the midterm will be automatically transferred to the final; there will not be a make-up test.
Structure: The course is divided into four parts according to the classes of games we use as the framework for economic applications: static games of complete information (Chapters 2 to 4 in the textbook), dynamic games of incomplete information (Chapters 5-7), static games of incomplete information (Chapter 9), and dynamic games of incomplete information (Chapter 10). My lecture notes follow the textbook fairly closely, especially for the first two parts. In greater detail, the order of chapters in the textbook to be covered is as follows:
Lecture 1: Nash Equilibrium. Chapter 1 (Introduction) 1-3; Chapter 2 (Nash Equilibrium: Theory) 1-6, 8, 9
Lecture 2: Applications of Nash Equilibrium. Chapter 3 (Nash Equilibrium: Illustrations) 1-3
Lecture 3: Mixed-strategy Nash Equilibrium and Applications. Chapter 4 (Mixed-strategy Equilibrium) 1-3, 8, 12
Lecture 4: Subgame Perfect Equilibrium. Chapter 5 (Extensive Games with Perfect Information: Theory) 1-5; Chapter 7 (Extensive Games with Perfect Information: Extensions and Discussion) 1, 2, 6
Lecture 5: Applications of Subgame Perfect Equilibrium. Chapter 6 (Extensive Games with Perfect Information: Illustrations) 1, 2
Lecture 6: Infinite-horizon Games. Chapter 16 (Bargaining) 1; Chapter 14 (Repeated Games: The Prisoner’s Dilemma) 1-3, 9-11.
Lecture 7: Bayesian Nash Equilibrium. Chapter 9 (Bayesian Games) 1, 2
Lecture 8: Applications of Bayesian Nash Equilibrium. Chapter 9 (Bayesian Games) 3-8
Lecture 9: Perfect Bayesian Nash Equilibrium. Chapter 10 (Extensive Games with Imperfect Information) 1, 2, 3, 4
Lecture 10: Signaling Games. Chapter 10 (Extensive Games with Imperfect Information) 5-8