T1 Prediction Markets: Economics, Computation, and Mechanism Design

Tutor: Yiling Chen, Yahoo! Research

A prediction market is a financial market designed to elicit a forecast for an uncertain event. To achieve this, the market offers a security whose payoff is tied to the outcome of the event and attracts traders willing to speculate on the outcome. With sufficient liquidity, traders will converge to a consensus price reflecting their collective information about the value of the security, which corresponds to a probabilistic forecast of the event. Prediction markets often yield better forecasts than other methods across a diverse array of settings. This tutorial will introduce the main concepts and issues in prediction markets. Topics covered include classical economic theory, laboratory investigations of experimental markets, field tests, recent empirical studies, and computational aspects of these markets. The tutorial will survey recent research on prediction market mechanism design, including compound markets, permutation betting markets, market scoring rules, dynamic pari-mutuel markets, and automated market makers. I will also touch on the legality of prediction markets. The tutorial will be largely self-contained. Attendees will benefit from a rudimentary knowledge of probability.

T2 Online Trust and Reputation Systems

Tutor: Neel Sundaresan, Director of Research and Head, eBay Research Labs

As online commerce, social networks, and user-generated content become common, the need for trust and reputation models become prime. This tutorial will give an overview of trust and reputation systems as studied by social network researchers. Topics to be covered include: Reputation and its relationship to security and fraud; Feedback and other Manifestations and Implementations of trust and reputations; Models of reputation; platforms: P2P systems, Centralized systems; Auctions, Incentive systems; Collaborative filtering; Social Networking and Social reputation; Portability and Universality of Identity, trust, and reputation.

Computing Community Consortium talk

Christos Papadimitriou, UC Berkeley
"The Algorithmic Lens: How the Sciences are Being Transformed by the Computational Perspective"
Grand Exhibit Hall

T3 Modeling Complex Networks for Electronic Commerce

Tutors: Arun Sundararajan and Foster Provost, Stern School of Business, New York University

Complex networks connect businesses, consumers, and the artifacts they create, such as pages, products, and accounts. Modeling these networks can help focus on the characteristics that will be useful for understanding or predicting important ecommerce phenomena, such as product demand or illicit behavior. Our tutorial will cover various generic models of (i) the structure of complex networks, and (ii) the probabilistic dependencies among networked entities. We will then discuss practical examples where the different types of models have been or could be used in order to improve understanding or to improve performance. For example, models of the structure of social networks can improve the theoretical understanding of network effects. Modeling the structure of co-purchase networks can help explain demand patterns in electronic commerce. Models of networked probabilistic dependencies can improve prediction tasks including the targeting of advertisements/offers, the detection of illicit behavior (such as fraud), and the identification of interesting Web pages. The tutorial will be largely self-contained. Attendees will be assumed to know basic economics, probability and statistics

T4 Tutorial on Sponsored Search

Tutors: Kartik Hosanagar, The Wharton School of Business, University of Pennsylvania, Co-founder, Natpal Inc and Michael Schwarz, Marketplace Design Scientist, Yahoo!

Sponsored search or Search Engine Marketing (SEM) is a multi-billion dollar industry in rapid growth. Major search engines, including Ask, Google, Microsoft, and Yahoo!, auction off advertising space next to their standard algorithmic search results. Simultaneously, advertisers use sponsored search to procure leads and manage their customer acquisition process. This tutorial provides an introduction to the main concepts and issues in sponsored search. Topics covered include design of the auction mechanism and its implications for search engine revenue, advertiser incentives and bidding, keyword generation, click fraud, click rate learning, contextual advertising, matching algorithms, and targeting. The presentation will be largely self-contained. Attendees will be assumed to know basic economics, probability and statistics.

Computing Community Consortium talk

Bob Colwell, Independent Consultant
"Future of Computer Architecture '07"
Grand Exhibit Hall