Questions and Answers

Given the space constraints, we will at best be able to add a few more students to the class. If we are able to add students to the class, we will be inclined to favor students who have been keeping up with the class and showing that they are likely to make valuable contributions to it.

We hope to be able to offer the course again in Spring 2020, and should be able to have a larger number of students next year. (But, nothing is guaranteed about this yet.)

We will hopefully be able to add a few more students to the class, but we are not expecting to be able to get a bigger room - unfortunately classroom space at UVA is very limited - and the current limits are the maximum (or very close to it) we can have with the assigned room.

We won’t kick anyone who wants to attend out of the classroom (unless we get complaints from the fire marshall). The room is fairly small, but everyone should feel free to come, and in the unlikely event that there is a space problem, we’ll try to sort out a solution.

If you’re in the CS section of the course, there is no required Economics prerequisites, so definitely not expected that you would have taken econometrics or machine learning. If you are in the Econ section, you need to satisfy the prerequisite (which does include an Econometrics course).

The lectures should be accessible to students without any background beyond the prerequisites (so, we won’t assume any CS or economics background that students in the other section wouldn’t be expected to have). For the projects, you’ll be working in teams that include students in both sections, and we hope you’ll be able to work together and learn from each other in ways that benefit from your different backgrounds in CS and Economics.

See Class 2.

At some level, all problems in science are computing problems, and all practical problems are economic ones.

More concretely, there is a set of practical areas that have emerged in the past 10-15 years where Economics and Computer Science are are equal partners. For instance, the tasks of optimal routing (one of the “classical” questions in Computer Science) are now proved to be impossible to solve without invoking the idea of a Nash equilibrium. On the other hand, market design (which is a large area within Econimics) often reduces to a computational problem and solving it is often impossible without using the ideas from Computer Science such as compitational complexity.

The updated syllabus has a rough grade break down. But, we don’t grade by a simple forumla based on weighting each assignment. We’re looking for evidence to support the highest justifiable grade based on everything you’ve done in the class.

This is the first time teaching a class like this, so some things we’ll figure out as we go, and we definitely appreciate feedback and suggestions from students about how things are working or could work better. For the class meetings, we will mostly follow a fairly traditional (but hopefully engaging and enlightening!) lecture format, with both sections combined. If it seems useful to occasionally split into two groups, we might do that. For the projects, we will have students working in interdisciplinary teams, and hope that students will learn a lot from this experience and from working with teammates with different backgrounds to solve problems that require knowledge and skills in both Economics and Computing.

Sorry, this was a mistake in a preliminary version of the syllabus. We don’t actually plan to have any final exam in this class. (See the updated syllabus, which will be finalized before the first class, for more details.)

The course will have 5 assigned projects and 1 final project. All of them will be based on analyzing real datasets and we require using Python for this analysis. The course does not have CS prerequisites for the Economics students, so we are not expecting you to necessarily have any previous programming experience. The course will contain overview of the concepts from Economics and Computer Science that will be used. We also expect that interaction between Economics and Computer Science students will help mutual learning. The material will combine Economic theory with the concepts from Computer Science and use Econometric and Machine learning methods for bringing this theory to data.

Being able to tackle open-ended problems and work creatively to find good solutions, working well in a team, being able to express yourself well in writing (and in code or mathematics), and being open to learning new things and going beyond what is provided. Active participation in the class as well as contributing well to successful projects will be a sufficient condition for getting an A in the class.