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