Instructor Interview
Below, Prof. Ian Ball describes various aspects of how he taught 14.12 Economic Applications of Game Theory in the fall semester of 2025.
OCW: Game theory is relevant not only to games in the usual sense (e.g. chess, poker) but to a wide range of other human activities—commerce, warfare, even the choice of a romantic partner or a spouse. What do games have in common with all these other activities?
Ian Ball: The unifying principle behind all these activities is strategic interdependence: different agents (individuals, firms, organizations, governments) are making choices, and the best choice for each agent depends on the choices made by the other agents.
OCW: In the games you run with the students in class, it’s evident that not all of the students are making their game choices in what economists would call a “rational” manner. From a pedagogical standpoint, how do these anomalous players affect the usefulness of the exercise?
Ian Ball: These deviations are extremely valuable. In fact, I intentionally select games in which I expect students’ play to deviate from theoretical predictions. Then we can discuss as a class why these deviations occur. In some cases, students are making “mistakes," and their behavior shifts closer to the theoretical prediction when we play again after the class discussion. In other cases, students aren’t making “mistakes”—they’re content with their choices and they’ve made them for good reasons. Often this means that something is missing from the mathematical model of the game. I emphasize in class that one of the most important aspects of applied game theory is choosing the right model to capture a situation.
OCW: Speaking of those in-class games, can you share your thoughts on the relative teaching value of face-to-face games (like the Prisoner’s Dilemma, as you ran it in class) and those mediated by technology (like the Keynesian Beauty Contest, for which you used MobLab)?
Ian Ball: The advantage of an app like MobLab is that it records students’ choices and then illustrates the range of play within the class. For two-person games, however, I think it’s valuable for students to play face-to-face because this crystallizes the central challenge of game theory—as you’re reasoning about the best way to play, your opponent is engaging in exactly the same reasoning.
OCW: You assign problem sets but don’t collect them; instead, you administer in-class quizzes based on the problem set material. Why did you adopt this policy, and how has it worked in practice?
Ian Ball: In previous years, I collected the problem sets. I moved to quizzes this year because generative AI has progressed to the point that it can accurately solve essentially all of the problem set questions. Some students reported that they didn’t like the quizzes, and I’m still thinking about the best structure for next year. But I do think the quizzes achieved their pedagogical goal: students seriously engaged with the material outside of class each week, and I was happy to see that students generally performed well on the in-class exams.
OCW: What would you like to share about teaching 14.12 that we haven’t yet addressed?
Ian Ball: I feel very fortunate to have inherited this class from Muhamet Yildiz when I joined MIT in 2021. Muhamet taught 14.12 for many years, and he deserves credit for refining the structure of the course.
Assessment
Grade Breakdown
Students’ grades were based on the following activities:
- 20% Quizzes
- 30% Midterm exam
- 50% Final exam
Curriculum Information
Prerequisites
and one of the following:
- 14.04 Intermediate Microeconomic Theory
- 14.30 Introduction to Statistical Methods in Economics
- 6.041 Probabilistic Systems Analysis and Applied Probability
- 18.05 Introduction to Probability and Statistics
Requirements Satisfied
- 14.12 fulfills the social sciences component of the General Institute Requirement (GIR) in Humanities and Social Sciences.
- The course can be applied toward a Bachelor of Science in Economics or a Bachelor of Science in Mathematical Economics, but is not required.
Offered
Every fall semester
Student Information
Enrollment
111 students
Breakdown by Year
The course attracts a mix of second-, third-, and fourth-year undergraduates.
Breakdown by Major
Aside from economics majors, the class included students from many other fields, especially computer science and math.
How Student Time Was Spent
During an average week, students were expected to spend 12 hours on the course, roughly divided as follows:
Lectures
Met 2 times per week for 1.5 hour per session; 26 sessions total; mandatory attendance
Recitations
Met 1 time per week for 1 hour per session; 13 sessions total; mandatory attendance
Out of Class
Outside of class, students worked on problem sets to prepare for quizzes and studied for the midterm and final exam.
