Behavioral Game Theory: Experiments in Strategic Interaction

Course Description

Life sometimes seems illogical. Humans do strange things: fight with strangers, take obviously unfavorable bets, pay for gym subscriptions they never use. Life can also feel like a machine crafted with great precision: people drive on the same side of the road, follow a schedule, and organize to produce thousands of products. Why is that? Are we rational beings that calculate every decision, or are we instead flawed decision-makers who commit predictable mistakes, even when the consequences are negative for ourselves and even others? In this course, we will dive into the motives behind human behavior. Along the way, we will introduce tools that economists use to understand people and anticipate how they will react in different situations. We will show that such tools can be applied in designing policies that help individuals reach better decisions for themselves and their communities, such as the design of savings plans for retirement.

The course is divided into two parts.

During the first part, we will explore human behavior under the assumption that people are rational, i.e., they make choices by carefully weighing its associated costs and benefits. We will use this assumption to study situations in which people interact strategically, such as the best way to play “rock, paper, scissors,” why everybody drives on the “correct” side of the road, and the Cuban missile crisis. This discussion will be based on a powerful mathematical tool: Game Theory. We will note that (1) making good decisions can lead to bad outcomes, and (2) sometimes humans seem not to behave like the rational model suggests they would.

In the second part of this course, we will analyze the consequences of dropping the rationality assumption. We will discuss many examples that illustrate how humans do not always behave rationally and tend to make predictable mistakes. Some examples of irrationality that we’ll discuss include overconfidence, temptation and self-control, the gambler’s fallacy, and herd behavior. Often, the presence of cognitive biases and shortcuts (heuristics) can explain why people don’t take the optimal action in a given situation. We will explore tools economists use to uncover those heuristics and thus help individuals make better choices.

Throughout this course, you will gain insight regarding how people interact, why they act the way they do, and how human behavior is modeled in Economics.

By the end of this course, you will be able to:

  • Recognize the basic concepts in Game Theory such as simultaneous move games and sequential move games
  • Comprehend how economists use Game Theory to model real-life situations
  • Understand the shortcomings of the rationality assumption and the basic concepts of Behavioral Economics
  • Become acquainted with the process by which economists run experiments and how they scientifically interpret results
In-person course details:
We will meet daily for two weeks. There will be seven main lectures, in which we will discuss course material and discuss complementary assignments, and two lab sessions in which we will meet in the computer room to play some games. You will also be required to participate in asynchronous online discussions and complete two assignments. In the last class, you will present a group project to discuss some real-life scenarios using the tools learned during the course.

Online course details:
The online section will involve asynchronous lectures, synchronous office hours, online lab experiments, and group projects (where you may freely coordinate how they prefer to work). There will be 1-2 video lectures posted per day, 3-4 days per week. There will also be 2-3 quizzes per week and two group projects (one due at the half of the course and the other at the end). For the second group project, we will hold two Zoom meetings to present your work to their classmates.


The course assumes knowledge of basic mathematics, such as solving linear and quadratic equations, plotting linear functions, and computing averages. Basic knowledge of probability would be a plus. For the in-person course, this course recommends a laptop for course-related programming, games/simulations, etc. Please note that some devices (e.g., Chromebooks) do not allow software downloads onto a desktop and so will not accommodate the specific needs of this course. If you have questions about this requirement please reach out to [email protected]


Two Sections Available to Choose From:

Online sections of Pre-College courses are offered in one of the following modalities: Asynchronous, Mostly asynchronous, or Blended. Please review full information regarding the experience here.

Dates: June 20, 2022 - July 01, 2022
Duration: 2 Weeks
Meeting Times: M-F 8:30A-11:20A
Status: Closed
Format: On-Campus
Instructor(s): Santiago Hermo
Course Number: 10166

Dates: July 11, 2022 - August 05, 2022
Duration: 4 Weeks
Meeting Times: Online - Mostly Asynchronous
Status: Closed
Format: Online
Instructor(s): Juan Pedro Ronconi
Course Number: 10139