Please use this identifier to cite or link to this item: 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: 3642108407
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
Appears in Collections:Aurora harvest 7
Electrical and Electronic Engineering publications

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