CS269: Mathematical Frameworks for Social Computing
Tuesday/Thursday, 2-3:50pm, Boelter 5422
Instructor: Jenn Wortman Vaughan

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Course Overview

This seminar-style course will explore theoretical models and frameworks for social computing. We will examine ways in which techniques from learning theory, game theory, and theoretical computer science can be used to model and analyze social computing systems such as prediction markets, crowdsourcing markets, and question and answer forums. Students will be expected to read and present research papers on these topics and complete an open-ended course project.

Who should take this course? This course is primarily intended for students interested in learning about the latest research on theoretical issues that arise in social computing systems. Students who enroll should be very familiar with the basics of probability theory (roughly at the level of the first half of CS 112) and algorithms (e.g., big O notation), comfortable reading and writing formal mathematical proofs, and eager to get involved in class discussions. A background in learning theory and/or game theory will be helpful, but is not required.

Who should not take this course? This course requires students to read and actively discuss several theoretical research papers each week. Students without the mathematical background or interest to do so will struggle with this course. If you are unsure about your background, try reading some of the papers from the reading list and see what you think.

This course has been approved to count for credit for the AI major and minor field requirements for computer science Ph.D. students.

There is no required text book for this course. All reading material is posted below.

Office hours for this course are by appointment only. Email Jenn to schedule an appointment.

Breakdown of Grades

Grades will be based on the following components:

For borderline cases, active class participation will be taken into account when determining final grades.

Paper Reviews

Paper reviews must be submitted using this form by 11:59pm on the night before each class, starting with the fourth class. To receive full credit, you must make a reasonable attempt at answering every question. Half credit will be assigned to students who submit incomplete responses. No credit will be given for responses submitted after midnight.

Reviews must contain an answer to every question, and these answers must be in your own words, not copied from the text of the paper. You may not need more than 1-2 sentences per answer, but it should be clear that you have put thought into every question. For tips on how to read and summarize a research paper, you might want to check out Michael Mitzenmacher's blog post and the link to his advice.


When it is your turn to lead the discussion, you should come prepared with enough material that you would be able to get through the whole hour and fifty minutes on your own if nobody in the class said a word. Hopefully you won't need it all, but having a little extra material prepared isn't a bad thing. Even if you don't get through everything you prepared, it will give you more flexibility in that you can decide what to present on the fly based on the interests of the class. Writing up presentation notes will also help you solidify your own understanding of the material. I often falsely believe that I fully understand a model or proof and then realize there was a subtle point I missed when I actually write up detailed notes on each step to present.

You should spend the first 20-30 minutes presenting the main ideas in the paper in detail. Yes, everyone should have read it on their own, but there is a good chance that many people were confused about different points, and this will help make sure that everyone has a basic understanding of the ideas and give people a chance to ask about things they didn't understand.

As a rough guideline, it should take a minimum of about 3 hours per person to prepare the material to present after you have read and understood the paper. I might suggest having everyone on your team read the paper individually and then get together for a couple of hours to plan the presentation.

You might find it useful to read Kilian Weinberger's General Advice on presentations. Here are a few more tips:

Contact me if you have questions about any of these points when preparing your presentation.

Final Research Projects

The final project guidelines are posted here. Be sure to read the guidelines carefully and make a note of the key dates and milestones!

Good places to look for inspiration for your project include recent workshops on related topics, like HCOMP or the Workshop on Social Computing and User Generated Content. ACM EC also contains many related papers, as do some AI and machine learning conferences.

Schedule & Reading Material

This schedule is tentative and may shift as we get further into the material. Lecture slides will be posted here when available.

Introduction and Background on Game Theory

Prediction Markets

Task Routing in Social Networks

User Generated Content and Crowdsourcing

Reputation and Recommendation Mechanisms

Games with a Purpose

Research Project Presentations

Academic Honesty Policy

All students are expected to meet the guidelines laid out in UCLA's Student Guide to Academic Integrity. Any student suspected of academic dishonesty will be referred to the Dean of Students for disciplinary action.


Many thanks to Yiling Chen, Arpita Ghosh, Chien-Ju Ho, Shaili Jain, and Haoqi Zhang for their fantastic suggestions of relevant papers to include in this course.