COMPSCI 236R: Topics at the Interface between Computer Science and Economics

Fall 2022: Value of Information and Data

Meetings: Tuesdays and Thursdays 9:45am-11:00am ET, SEC 2.118 
Instructor: Yiling Chen, yiling@seas.harvard.edu 
Teaching fellow: Satyapriya Krishna, skrishna@g.harvard.edu   
Office Hours: 
     Yiling: After 8/31, Tuesdays 11:00-12:00 or by appointment, SEC 5.306. (I will hold a Zoom office hour on Monday August 22, 10:30am - 11:30am, https://harvard.zoom.us/j/3145851810) 
     Satya: Thursdays 11-12 PM (Venue : Zoom link) .  

Note: Enrollment of this course will be limited to facilitate seminar-style discussion of papers. If you are interested in enrolling, please complete this survey by 5pm ET Tuesday August 23. When necessary, we'll give preference to graduate students and students who are better prepared for the course.  

General Information

This is a rotating topics course that studies the interplay between computation and economics. The class is mostly seminar style. Readings are drawn from artificial intelligence, theoretical computer science, machine learning, multi-agent systems, economics, psychology and operations research.  

The topic of Fall 2022 is value of information and data. Data and information have become the new oil. Data-driven approaches are substantially impacting almost all aspects of the society. This semester we will navigate the literature related to understanding data and information. How should we quantify value of information? Can we measure value of data for machine learning? Since data and information are valuable, how should we price and trade them? Does the presence of data and information affect mechanism design? What are some legal considerations of data governance? We hope to expose students to a diverse set of topics of this exciting and emerging area. 

Course Goals

The main goal of this course is to provide an introduction to the interdisciplinary literature for students looking to identify research directions in this area. Along the way, we will also develop some technical background in game theory, economic modeling, machine learning and algorithms, and hopefully also more general skills related to reading papers and thinking about research problems. This is a seminar course and students will be expected to participate in class discussion, present one or more papers, and write a final course paper. Students are expected to achieve a comfort level with both economic and computational thinking, become familiar with the status quo in the area, and, to the extent possible, work on an open research problem.

Prerequisites

Formal requirements include a basic course in calculus (AM 21a or equivalent), a linear algebra course (AM 21b or equivalent),  probability course (STAT 110 or equivalent) and a background in either AI or microeconomic theory (CS 181, CS 182, EC 1011a, or equivalent). The informal requirement is a reasonable level of mathematical maturity. CS 136 is helpful but not required. Familiarity with economic theory is helpful but not required. Familiarity with AI and computer science theory is helpful but not required.

Mathematical analysis and formalism will be fundamental to the course, and students should expect to learn additional mathematics on their own as necessary. I recommend that students unsure about their background read a couple of papers from the reading list, and email or talk to us during the first week.

Course Structure and Grading Policy

This course is primarily a seminar course. We will spend most of the term reading and discussing research papers. However, we will include lectures on some important background materials that will help with understanding the material in the papers that we will read. There will be 2 problem sets.

The final grade in the class will break down roughly as: participation and comments 25%, problem sets 20%, presentation of research papers 20%, project 35%.

Students are expected to read the papers in advance, submit short summaries, comments and answers to reading questions before class, participate in class discussion, and present and lead discussion on one or more sets of papers (typically in a pair).

In lieu of a final exam there will be a final research paper, on a topic of the student's choice. Good papers can form a foundation for a research leading to a conference publication, or a senior thesis for undergraduates. Students are encouraged to work in pairs for final projects other than exposition papers.

Collaboration Policy: Discussion and the exchange of ideas are essential to academic work. If you work in a team for final project, collaboration within the team is essential and strongly encouraged. However, it is expected that each member of a team makes roughly equal contributions. For final projects, you are encouraged to consult with your classmates outside of your team on the choice of topics and to share sources. You may find it useful to discuss your chosen topic with your peers, particularly if you are working on the same topic as another team. However, you should ensure that any written work your team submit for evaluation is the result of your team's research and writing and that it reflects your team's approach to the topic. You must also adhere to standard citation practices in this discipline and properly cite any books, articles, websites, lectures, etc. that have helped you with your work. If you received any help with your writing (feedback on drafts, etc), you must also acknowledge this assistance.

Submitting Comments and Presenting Papers

You are required to read papers and other listed reading materials before each class. (Materials listed under Extra Readings on the Schedule page are optional.) We'll use Perusall for pre-class readings. You MUST submit comments on the readings by midnight before class once we start to read papers. Your comments should include good-faith answers to posted reading questions (if any) and general comments. For research papers, things to think about for general comments include (you don't need to hit all of these...):

• what is the main contribution of the paper?
• is this important, why?
• what was the main insight in getting the result?
• what is not clear to you?
• what did the authors not do?
• what are the most important assumptions, are they limiting?
• if applicable, what applications does this suggest?
• how does this relate to other things we have seen?
• what extensions does this suggest?
• can you suggest a two-sentence project idea based around the ideas in this paper?

I also recommend you read the blog post by Prof. Michael Mitzenmacher on How to Read a Research Paper.

You won't be graded on the correctness or the rigorousness of your answers to reading questions. These questions are designed to assist in understanding the material and to encourage discussion.

Presenting papers: Students will present papers (likely with a partner) and, in addition to the presentation, be ready to lead a discussion in class. Students presenting papers must come by to office hours one week before their presentation and talk with me about the paper(s) before their presentation. Students are also asked to propose reading questions for the papers they present. Please read the Presentation Notes for expectations on student presentations.

Course Reading

There is no required text. All readings will be distributed electronically and sometimes in class. 

Final Paper

The goal of the final paper is to develop a deep understanding of a specific research area related to the topic of the class, and to the extent possible to work on an open research problem. Although paper topics must be approved, students are free to pick a topic of interest in the general field related to the topic of the semester. Students are required to submit a proposal, give a short presentation, and submit a final paper (maximum 10 pages except for Appendix material). Papers may be computational, theoretical, experimental or empirical. Students may write an exposition paper (maximum 10 page) on at least three related technical papers of their choice that are related to the course material. Such a paper MUST include an exposition of formal results in these papers, provide a critical discussion of assumptions made by the authors and suggestions about future work, and provide a new perspective.

Tentative schedule:

- Tuesday 10/18: project proposal due 

- Tuesday 11/29 and Thursday 12/1: project presentation

- Tuesday 12/6: final paper due