SI 679 Aggregation and Prediction Markets
Instructor: Rahul Sami (Office hours: TBA)
1.5 Credit, 7-week course module
Second half of Fall 2011
Tuesday, Thursday 10:00-11:30am, 2245 NQ
Course Learning Objectives:
Learn different approaches to aggregating opinions or information from
a number of sources in order to come up with a forecast.
Understand the prediction market design space, and how prediction markets
compare to alternative information aggregation methods.
Know how to design and deploy a prediction market for a given application.
In many settings, the wealth of information on a particular subject is
distributed among many entities, with no single source having all the
relevant information. In this course, we will study approaches to
elicit and combine this information in order to come up with a forecast
or estimate that reflects
the combined information of all sources. This idea of aggregating information
from multiple sources is an essential ingredient of many applications,
including weather forecasting, predicting election outcomes, market research
on tastes, and assigning betting odds. Recently, prediction markets have
been deployed to aggregate opinions and come up with forecasts on election
outcomes, scientific advances, product delivery dates, Academy Award outcomes,
and many other events.
We will study theoretical and practical aspects of several aggregation tools,
including opinion polls, machine-learning techniques to combine or select
experts, scoring rules, and prediction markets; we will focus on
incentive-centered design techniques to elicit honest and accurate
- Introduction to game theory: old SI 625, new SI563, or equivalent (may be taken concurrently), or permission of instructor.
- Some knowledge basics of probability. If you're not sure you are prepared
for this, I have put up a self-quiz here.
Note: some topics may take a little less or more than one lecture, so this schedule may shift
- Lecture 1:
Introduction and demo of prediction markets.
- Lecture 2:
Classification of forecasting processes. Comparison of prediction markets to alternatives.
- Lecture 3:
Sequential prediction and cascades
- Lecture 4:
Incentives to report truthfully. Proper scoring rules.
- Lecture 5:
Evaluation of forecasters and forecast sequences
- Lecture 6:
Theoretical underpinnings: Rational Expectations and no-trade results
- Lecture 7 and 8:
Market forms: Market Scoring Rules, Parimutuel Markets. Implementation of
- Lecture 9:
Manipulation in prediction markets
- Lecture 10:
Legal and policy Issues; real money vs. play money markets
- Lecture 11:
Case studies: Election Markets, Market Research Markets
- Lecture 12:
Review and Student Presentations
Academic Integrity Policy
The UM and SI Academic Integrity Policy applies to this course:
Collaboration while working on homework
problems, and while discussing and interpreting the reading assignments, is
learning is effective. Collaboration will be especially valuable in summarizing the
reading materials and picking out the key concepts. You must, however, write your
homework submission on your own, in your own words, before turning it in. If you
worked with someone on the homework before writing it, you must list any and all
collaborators on your written submission.
All written submissions must be your own, original work. Original
work for narrative questions is not mere paraphrasing of someone else's completed
answer: you must not share written answers with each other at all. At most, you
should be working from notes you took while participating in a study session. Largelyduplicate copies of the same assignment will receive an equal division of the total
point score from the one piece of work.
You may incorporate selected excerpts from publications by other authors, but
they must be clearly marked as quotations and must be attributed. If you build
on the ideas of prior authors, you must cite their work. You may obtain copy
editing assistance, and you may discuss your ideas with others, but all substantive writing and ideas must be your own, or be explicitly attributed to another.
Rackham Graduate policy on Academic and Professional Integrity
for the definition of plagiarism, and associated consequences.
Accommodations for Students with Disabilities
If you think you need an accommodation for a disability, please let me
know at your earliest convenience. Some aspects of this course, the assignments, the in-class activities, and the way we teach may be modified
to facilitate your participation and progress. As soon as you make me
aware of your needs, we can work with the Office of Services for Students
with Disabilities (SSD) to help us determine appropriate accommodations. SSD (734-763-3000; http://www.umich.edu/~sswd) typically rec-
ommends accommodations through a Verified Individualized Services and
Accommodations (VISA) form. I will treat any information you provide
as private and confidential.
Course Work and Assessment
- Participation in class discussion 10%
- 3 Assignments 30%
Assignments will include problem-solving and short questions based on
the techniques studied in class and the readings.
- Final project 60%
Submit a term paper (about 6-8 pages) on a topic related to prediction
Course policies in case of significant flu outbreak
- In case of a significant flu outbreak, the syllabus and schedule may be modified
- If you are suffering from flu-like symptoms, please stay at home until you are fever-free for 24 hours. Email the instructors about any questions you have, or to make alternative arrangements. Also feel free to contact Marsha Antal (email@example.com) with any questions you have.