These materials are from a past semester of this course at the U-M School of Information. For details and a syllabus of the current course, please see https://www.si.umich.edu/programs/courses/563.
This is a standard course in "game theory," designed with the School of Information MSI students as the primary audience. This course is the pre-requisite for several ICD courses. To be well-prepared for management, policy and analysis in the information professions you need to first have a solid grounding in game theory and its applications to problem solving. Thus, the primary objective is to teach you a set of useful theories and how to apply them to solve problems. The emphasis is on method and application.
Instructor: Yan Chen
dScribe: Mike Harmala
Course Level: Graduate
Course Structure: 90 minute classes - two times a week
Syllabus
Required Text:
Strategy: An Introduction to Game Theory (2nd Edition) by Joel Watson, Norton Publishing. Selected chapters (pdf) are posted on ctools, under Resource/Watson.
Course Description:
This is a standard course in "game theory," designed with the School of Information MSI students as the primary audience. This course is the pre-requisite for several ICD courses. To be well-prepared for management, policy and analysis in the information professions you need to first have a solid grounding in game theory and its applications to problem solving. Thus, the primary objective is to teach you a set of useful theories and how to apply them to solve problems. The emphasis is on method and application. You will, consequently, be expected to do a lot of problem-solving homework. It is essential to practice the skills if you want to learn how to use them (and to succeed in the course).
Course Requirements:
There will be approximately 6 homework assignments. I strongly encourage you to form "partnerships" of two or three students to work on your homework problems. Partnerships can turn in a single homework, signed by all members. Assignments are due at the beginning of your assigned lecture period (Thursday 1:10pm), unless otherwise specified. Assignments should be submitted in hard copy.
We will return graded assignments in your mailboxes no later than two weeks after the original due date. An answer key for the assignment will be posted to CTools on the Thursday following the assignment due date.
There will be an in-class final exam on 10/16/2008. It is closed-book, closed notes, but you will be allowed to have a 2-sided “cheat sheet” and a non-graphical calculator. I will post sample exams from past years. The final exam will be one hour and 20 minutes long, held during regular lecture hours in the regular classroom. You are responsible for attending the exam. No make-ups will be held except for students with a medical excuse from a doctor.
Basis for Course Grades
Homework 60%: In order to receive credit for a homework assignment, you must turn it in on time. One point will be deducted from your total for every 24 hours of delay.
Final Exam 40%: There will be no make-up final except for students with a medical excuse from a doctor.
Distribution of Grades: Students above the median will receive A+, A, and A-, while those below the median will receive B+ or below.
Course Schedule:
Week #1, Sept 2, 4
Introduction; Representing games
Readings: Ch. 1-5
Week #2, Sept 9, 11
Dominance; Nash Equilibrium
Readings: Ch. 6, 7, 9-12
Homework: Representation
Week #3, Sept 16, 18
SPNE; Bargaining
Readings: Ch. 14-16, 18, 19
Homework: Dominance; Nash
Week #4, Sept 23, 25
Repeated games and reputation
Readings: Ch. 22, 23
Homework: SPNE; Bargaining
Week #5, Sept 30, Oct 2
Information: Bayesian Nash Equilibrium
Readings: Ch. 24-27
Homework: Repeated games
Week #6, Oct 7, 9
Information: Perfect Bayesian Equilibrium
Readings: Ch. 28, 29
Week #7, Oct 14, 16
Review; Final exam
Schedule
Week #1, Sept 2, 4
Introduction; Representing games
Readings: Ch. 1-5
Week #2, Sept 9, 11
Dominance; Nash Equilibrium
Readings: Ch. 6, 7, 9-12
Homework: Representation
Week #3, Sept 16, 18
SPNE; Bargaining
Readings: Ch. 14-16, 18, 19
Homework: Dominance; Nash
Week #4, Sept 23, 25
Repeated games and reputation
Readings: Ch. 22, 23
Homework: SPNE; Bargaining
Week #5, Sept 30, Oct 2
Information: Bayesian Nash Equilibrium
Readings: Ch. 24-27
Homework: Repeated games
Week #6, Oct 7, 9
Information: Perfect Bayesian Equilibrium
Readings: Ch. 28, 29
Week #7, Oct 14, 16
Review; Final exam
About the Creators
Yan Chen is a professor in the School of Information and coordinator of the Incentive-Centered Design specialization of the Master of Science in Information program. She is also director of the STIET program. Before coming to the School of Information, she was on the faculty of the U-M Department of Economics.
The fundamental challenge her research addresses is the design of robust economic mechanisms when agents are not perfectly rational. Mechanism design theory assumes people are perfectly rational and can reach an equilibrium instantly in an economic situation. Chen's research looks at questions of how people really learn in such situations, what types of mechanisms aid that learning, and whether such learning can eventually lead to the states of "equilibrium" predicted by theory. She conducts both theoretical and experimental research, bringing human subjects into the laboratory to work with economic games.
- Ph.D. in Economics, California Institute of Technology
- BA, Tsinghua University
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Syllabus |
Yan Chen
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Week 01: Introduction and Representation of Games |
Yan Chen
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Week 02: Dominance and Nash Equilibrium |
Yan Chen
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Week 03: Solving Extensive Form Games: SPNE |
Yan Chen
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Week 04: Bargaining |
Yan Chen
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Week 05: Repeated Games and Reputation |
Yan Chen
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Week 06: Normal Form Games of Incomplete Information |
Yan Chen
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