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https://hdl.handle.net/2440/88089
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Type: | Conference paper |
Title: | Prediction mechanisms that do not incentivize undesirable actions |
Author: | Shi, P. Conitzer, V. Guo, M. |
Citation: | Lecture Notes in Artificial Intelligence, 2009 / Stefano Leonardi, (ed./s), vol.5929 LNCS, pp.89-100 |
Publisher: | Springer |
Publisher Place: | Berlin, Germany |
Issue Date: | 2009 |
ISBN: | 9783642108402 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | International Workshop on Internet and Network Economics (WINE) (14 Dec 2009 - 18 Dec 2009 : Rome, Italy) |
Editor: | Stefano Leonardi, |
Statement of Responsibility: | Peng Shi, Vincent Conitzer, Mingyu Guo |
Abstract: | A potential downside of prediction markets is that they may incentivize agents to take undesirable actions in the real world. For example, a prediction market for whether a terrorist attack will happen may incentivize terrorism, and an in-house prediction market for whether a product will be successfully released may incentivize sabotage. In this paper, we study principal-aligned prediction mechanisms–mechanisms that do not incentivize undesirable actions. We characterize all principal-aligned proper scoring rules, and we show an “overpayment” result, which roughly states that with n agents, any prediction mechanism that is principal-aligned will, in the worst case, require the principal to pay Θ(n) times as much as a mechanism that is not. We extend our model to allow uncertainties about the principal’s utility and restrictions on agents’ actions, showing a richer characterization and a similar “overpayment” result. |
Keywords: | Prediction Markets; Proper Scoring Rules; Mechanism Design |
Rights: | © Springer-Verlag Berlin Heidelberg 2009 |
DOI: | 10.1007/978-3-642-10841-9_10 |
Published version: | http://dx.doi.org/10.1007/978-3-642-10841-9_10 |
Appears in Collections: | Aurora harvest 7 Electrical and Electronic Engineering publications |
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