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Prediction Mechanisms That Do Not Incentivize Undesirable Actions

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Internet and Network Economics (WINE 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5929))

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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.

This work is supported by NSF IIS-0812113, the Sloan Foundation, and a Yahoo! Faculty Research Grant. We thank the anonymous reviewers for helpful comments.

An Erratum can be found at http://dx.doi.org/10.1007/978-3-642-10841-9_66

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Shi, P., Conitzer, V., Guo, M. (2009). Prediction Mechanisms That Do Not Incentivize Undesirable Actions . In: Leonardi, S. (eds) Internet and Network Economics. WINE 2009. Lecture Notes in Computer Science, vol 5929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10841-9_10

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  • DOI: https://doi.org/10.1007/978-3-642-10841-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10840-2

  • Online ISBN: 978-3-642-10841-9

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