Rensselaer Polytechnic Institute
Department of Computer Science


CSCI 6963/4963: E-Commerce, Social Networks, and Collective Intelligence

Spring 2011

ANNOUNCEMENTS

OVERVIEW

The Internet has transformed how people interact with each other, lowering the cost of communication, and enabling us to rapidly both discover and pass on new information. This transformation has had major impacts in how we conduct market transactions (think of eBay, Orbitz, or Amazon), how we maintain our social personae (Facebook, Twitter), and how we accumulate and produce knowledge for consumption (Wikipedia, Yelp). This course will cover theoretical foundations of e-commerce and social networks, as well as focusing on practical aspects of understanding how the design of online venues affects the interacions of participants and the success of the venue.

STAFF

Instructor: Sanmay Das
Office: Lally 302
Phone: x2782
Office hours: Thursdays from 3:30-5:00 PM, and by appointment.


POLICIES

Detailed policies are in the official syllabus. However, a couple of things are worth stressing. This will be taught as a graduate / upper level undergraduate class. That means (especially after the first few weeks) that I will often expect you to have done the reading in advance, and you should come to class prepared to participate. That means you need to both attend class regularly, and participate in class discussions. I consider these essential parts of this class, and will grade accordingly. Also, I will be relatively flexible about the particular topics we cover, so there is definitely room to adapt our schedule according to the interests of students in the class!

TEXTBOOKS

There are two textbooks for this class. They will be supplemented with readings from the academic literature.

PREREQUISITES

I expect some mathematical maturity. Officially, you should previously have taken at least one 4000-level or higher class in Machine Learning, Artificial Intelligence, Statistics, or Economics. Also, you should have exposure to algorithms, at least at the level of CSCI 2300. If you are not comfortable with calculus and probability, you may have a hard time in this class.

LECTURES

Lectures will be on Mondays and Thursdays from 2:00PM to approximately 3:30PM in Ricketts 212.
Date Topics Readings Extras
Jan 24 Introduction. Course policies. Course overview. Some interesting problems. Foreword to NRTV, Chapter 1 of EK.
Jan 27 Graphs. EK Chapter 2.
Jan 31 Game Theory 1: Example games and basic solution concepts: dominant strategy solutions and Nash equilibria. EK 6.1-6.6. NRTV 1.1-1.3
Feb 3 Game Theory 2: Mixed equilibria, subgame perfection EK 6.7, 6.8, NRTV 1.5.
Feb 7 Game Theory 3 and Auctions 1a: Subgame perfection contd. Ultimatum game. Correlated equilibria. Intro to auctions NRTV 1.5, 1.3.6, EK 9.6.
Feb 10 Auctions 1b: First price, second price sealed bid auctions and analysis with uniform distributions. EK Chapter 9 (incl. 9.7)
Feb 14 Auctions contd. Matching markets. Stable matching.
Feb 17 Stable matching. Price of anarchy in matching. Matching with valuations.
Feb 21 No class. President's Day.
Feb 24 Equity markets and prediction markets.
Feb 28 Stock trading games. Information aggregation.
Mar 3 Market-making in prediction markets. The loarithmic market scoring rule (LMSR) market maker.
Mar 7 Asymmetric information and the market for lemons. Horse races and utility functions.
Mar 10 Wealth-weighted information aggregation. Gambler's ruin trading games.
Mar 21 Social Choice 1: Condorcet's paradox, Arrow's theorem.
Mar 24 Social choice 2: The Gibbard-Satterthwaite theorem. Role of money.
Mar 28 VCG Mechanisms. The Clarke Pivot Rule.
Mar 31 The GSP Auction and Sponsored Search
Apr 4 SD presents papers I
Apr 7 SD presents papers II
Apr 11 Bot trading contest.
Apr 14 SD presents papers III
Apr 18 Student presentations I
Apr 21 Student presentations II
Apr 25 Student presentations III
Apr 28 Student Presentations IV
May 2
May 5
May 9 5 minute presentations of final projects.