Course Outline

The course covers a series of topics in game theory. Its' intention is to show you how you can use computational methods together with the game theory you have learned to enhance your practice. We'll cover a series of topics beginning with using symbolic algebra to help you with your game theory, ending with a full project involving relatively advanced theory, unsupervised computer learning, structural estimation and prediction.

Marking involves a series of assignments, likely six, 50%, then a final term project also 50%.

At this point you will have become familiar with jupyter notebooks. You likely have them installed on your own computer (best way if you havent is to use conda - https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html). Install the computer language julia. It is similar to python but faster. It is good for the kinds of computation you might do in microeconomics. It is complementary to R not a substitute for it. Since it frees you from matlab, it will save you money if nothing else.

There is a collection of notebooks you can use during the course. Clone them from https://github.com/michaelpetersubc/notebooks.

It will be assumed that you have read and understand the university required reading in this link - legal disclamer

The following is a partial list of topics.

The readings will be updated frequently so check back here for changes.

Theory, structural estimation, experiments

  1. Review of Bayesian Equilibrium
  2. Experiment - Ring Game, courtesty of Terri Kneeland - K-level reasoning and elementary structural estimation
  3. Experiment - Loss Aversion in the Ultimatum Game
  4. Auctions
  5. Matching
  6. Matching and algorithms