Uncertainty in Artificial Intelligence
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Strictly Proper Mechanisms with Cooperating Players
SangIn Chun, Ross Shachter
Abstract:
Prediction markets provide an efficient means to assess uncertain quantities from forecasters. Traditional and competitive strictly proper scoring rules have been shown to incentivize players to provide truthful probabilistic forecasts. However, we show that when those players can cooperate, these mechanisms can instead discourage them from reporting what they really believe. When players with different beliefs are able to cooperate and form a coalition, these mechanisms admit arbitrage and there is a report that will always pay coalition members more than their truthful forecasts. If the coalition were created by an intermediary, such as a web portal, the intermediary would be guaranteed a profit.
Keywords:
Pages: 125-134
PS Link:
PDF Link: /papers/11/p125-chun.pdf
BibTex:
@INPROCEEDINGS{Chun11,
AUTHOR = "SangIn Chun and Ross Shachter",
TITLE = "Strictly Proper Mechanisms with Cooperating Players",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "125--134"
}


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