Prediction Mechanisms That Do Not Incentivize Undesirable Actions (2009)
| Venue: | In WINE |
| Citations: | 5 - 1 self |
BibTeX
@INPROCEEDINGS{Shi09predictionmechanisms,
author = {Peng Shi and Vincent Conitzer and Mingyu Guo},
title = {Prediction Mechanisms That Do Not Incentivize Undesirable Actions},
booktitle = {In WINE},
year = {2009}
}
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Abstract
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.
