SI 679 Aggregation and Prediction Markets

Instructor: Rahul Sami (Office hours: Monday 5-6pm #3246E SI-N; Thursday 11:30-12noon, 314 West Hall)

1.5 Credit, 7-week course module
Second half of Fall 2009
Tuesday, Thursday 10:00-11:30am, 409WH

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.

Overview:

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 predictions.

Prerequisites

Course Schedule

Note: some topics may take a little less or more than one lecture, so this schedule may shift

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 encouraged. Active 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. See the 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

Course policies in case of significant flu outbreak