Prediction mechanisms that do not incentivize undesirable actions

Published

Journal Article

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. © 2009 Springer-Verlag Berlin Heidelberg.

Full Text

Duke Authors

Cited Authors

  • Shi, P; Conitzer, V; Guo, M

Published Date

  • December 1, 2009

Published In

Volume / Issue

  • 5929 LNCS /

Start / End Page

  • 89 - 100

Electronic International Standard Serial Number (EISSN)

  • 1611-3349

International Standard Serial Number (ISSN)

  • 0302-9743

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-10841-9_10

Citation Source

  • Scopus