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26 - Computational Aspects of Prediction Markets
- from IV - Additional Topics
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- By David M. Pennock, School of Engineering and Applied Sciences Harvard University, Rahul Sami, School of Information University of Michigan
- Edited by Noam Nisan, Hebrew University of Jerusalem, Tim Roughgarden, Stanford University, California, Eva Tardos, Cornell University, New York, Vijay V. Vazirani, Georgia Institute of Technology
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- Book:
- Algorithmic Game Theory
- Published online:
- 31 January 2011
- Print publication:
- 24 September 2007, pp 651-676
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- Chapter
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Summary
Abstract
Prediction markets (also known as information markets) are markets established to aggregate knowledge and opinions about the likelihood of future events. This chapter is intended to give an overview of the current research on computational aspects of these markets. We begin with a brief survey of prediction market research, and then give a more detailed description of models and results in three areas: the computational complexity of operating markets for combinatorial events; the design of automated market makers; and the analysis of the computational power and speed of a market as an aggregation tool. We conclude with a discussion of open problems and directions for future research.
Introduction: What Is a Prediction Market?
Consider the following mechanism design problem called the information aggregation problem. Suppose that an individual (“the aggregator”) would like to obtain a prediction about an uncertain variable, say the global average temperature in 2020. A number of individuals (“the informants”) each hold different and nonindependent sets of information bearing on the outcome of the variable. The goal is to design a mechanism that extracts the relevant information from the informants, aggregates the information appropriately, and provides a collective prediction or forecast. The forecast should ideally be equivalent to the omniscient forecast that has direct access to all the information available to all informants.
28 - Sponsored Search Auctions
- from IV - Additional Topics
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- By Sébastien Lahaie, School of Engineering and Applied Sciences Harvard University, David M. Pennock, Yahoo! Research New York, Amin Saberi, Department of Management Science and Engineering Stanford University, Rakesh V. Vohra, M.E.D.S. Kellogg School of Management Northwestern University
- Edited by Noam Nisan, Hebrew University of Jerusalem, Tim Roughgarden, Stanford University, California, Eva Tardos, Cornell University, New York, Vijay V. Vazirani, Georgia Institute of Technology
-
- Book:
- Algorithmic Game Theory
- Published online:
- 31 January 2011
- Print publication:
- 24 September 2007, pp 699-716
-
- Chapter
- Export citation
-
Summary
Abstract
One of the more visible means by which the Internet has disrupted traditional activity is the manner in which advertising is sold. Offline, the price for advertising is typically set by negotiation or posted price. Online, much advertising is sold via auction. Most prominently, Web search engines like Google and Yahoo! auction space next to search results, a practice known as sponsored search. This chapter describes the auctions used and how the theory developed in earlier chapters of this book can shed light on their properties. We close with a brief discussion of unresolved issues associated with the sale of advertising on the Internet.
Introduction
Web search engines like Google and Yahoo! monetize their service by auctioning off advertising space next to their standard algorithmic search results. For example, Apple or Best Buy may bid to appear among the advertisements – usually located above or to the right of the algorithmic results – whenever users search for “ipod.” These sponsored results are displayed in a format similar to algorithmic results: as a list of items each containing a title, a text description, and a hyperlink to the advertiser's Web page. We call each position in the list a slot. Generally, advertisements that appear in a higher ranked slot (higher on the page) garner more attention and more clicks from users. Thus, all else being equal, merchants generally prefer higher ranked slots to lower ranked slots.