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CS286r in Previous Years
| Meeting | Monday, Wednesday 1:00– 2:30PM | ||
| Location: MD 319. |
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| Instructor | Yiling Chen (please call me Yiling) | Teaching Fellow | Mike Ruberry |
| yiling – at– eecs.harvard.edu | mruberry – at – seas.harvard.edu | ||
| OH @ MD 339: Monday 2:30 – 3:30 or by appointment. |
OH Just ask and we'll find time that works for you. |
Project presentations will be on 12/7 at 1-3:30 PM in MD G135. You will all present from a common laptop due to time constraints so email the course TF your slides as a PDF at least an hour before the presentations. You will have 5 minutes to present.
This is a rotating topics course that studies the interplay between
computation and economics. Topics covered include electronic commerce,
computational social choice, computational mechanism design, peer
production, prediction markets and reputation systems. The class is
seminar style and readings are drawn from artificial intelligence,
theoretical computer science, multi-agent systems, economic theory, and
operations research.
Note: Enrollment of this course will be limited to facilitate
seminar-style discussion of papers. If necessary, we'll use a survey to
help with the selection of students, with preference given to graduate
students and students with the strongest background.
Many problems of interests are fundamentally about obtaining and aggregating information from the crowd. Think about influenza surveillance. Timely information on flu activities is highly desirable for influenza prevention and treatment, but is often possessed by different people, including doctors who meet patients, clinical microbiologists who perform respiratory culture tests, pharmacists who fill prescriptions, and even people who have acquired influenza. How to elicit, aggregate, and make use of such small pieces of information is crucial for informed decision making. Similarly, consider a machine learning system that has access to predictions of heterogeneous learners. A central problem is how the learning system combines the predictions of the learners to obtain a good prediction. The recent DARPA Network Challenge further pushes the idea of harnessing the collective intelligence by asking teams to provide coordinates of ten red weather balloons placed at different locations in the continental United States.
In fall 2012, we consider research directions related to information,
prediction, and collective intelligence, covering topics of information
theory (very briefly), peer prediction mechanisms, prediction markets,
online learning, and collective task solving. Some example research
challenges we will consider are: how to incentivize truthful report of
information, especially when the information is subjective or about some
uncertain event whose outcome is not verifiable in the future; how to
combine information from different sources; and how to design mechanisms
to allow people to express their information more freely.
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 and mechanism design, 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.
Formal requirements include a basic course in linear algebra (AM 21b
or equivalent), a probability and statistics course (STAT 110 or
equivalent), an algorithms course (CS 124 or equivalent), and a
background in either AI or microeconomic theory (CS 181, CS 182, EC
1011a, or equivalent.) Familiarity
with economic theory is helpful but not required. Familiarity with AI
and computer science theory is helpful but not required.
Students with a background in theoretical microeconomics and an interest
in computational issues should be able to appreciate the class
materials.
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 attend office hours
during the first week.
This course is primarily a seminar course. We will spend most of the
term reading and discussing research papers. However, the first few
classes will include lectures on some important background material that
will help with understanding the non-CS related material in the papers
that we will read. There will be 2 problem sets.
The final grade in the class will breakdown roughly as: participation
and comments 25%, problem sets 25%, presentation of research papers 15%,
project 35%.
Students are expected to read the papers in advance, submit
short summaries and questions before class, participate in class
discussion, and present and lead discussion on one or two 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 may work in pairs on problem sets and are
encouraged to work in pairs for final projects other than exposition
papers.
Collaboration Policy: If you work in a team for problem sets and
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 must also adhere to
standard citation practices 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.
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.) You MUST upload comments on the readings by midnight before class. 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...):
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 in pairs and, in addition to the presentation, be
ready to lead a discussion in class. Students presenting papers must
come by to office hours 1.5 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.
There is no required text. All readings will be distributed electronically and sometimes in class. Additional references include:
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 information, prediction and collective
intelligence. 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 two 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.
Assignment and Project dates