Strictly Proper Mechanisms with Cooperating Players

SangIn Chun, Ross D. Shachter
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:153-162, 2011.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR9-chun11a, title = {Strictly Proper Mechanisms with Cooperating Players}, author = {Chun, SangIn and Shachter, Ross D.}, booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence}, pages = {153--162}, year = {2011}, editor = {Cozman, Fabio and Pfeffer, Avi}, volume = {R9}, series = {Proceedings of Machine Learning Research}, month = {14--17 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r9/main/assets/chun11a/chun11a.pdf}, url = {https://proceedings.mlr.press/r9/chun11a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Strictly Proper Mechanisms with Cooperating Players %A SangIn Chun %A Ross D. Shachter %B Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2011 %E Fabio Cozman %E Avi Pfeffer %F pmlr-vR9-chun11a %I PMLR %P 153--162 %U https://proceedings.mlr.press/r9/chun11a.html %V R9 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Chun, S. & Shachter, R.D.. (2011). Strictly Proper Mechanisms with Cooperating Players. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R9:153-162 Available from https://proceedings.mlr.press/r9/chun11a.html. Reissued by PMLR on 04 October 2026.

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