The Possibilities and Limitations of Private Prediction Markets
The Possibilities and Limitations of Private Prediction Markets
复制标题
私人预测市场的可能性和局限性
DOI：
10.1145/3412348
复制
发表时间：
2020
影响因子：
1.2
通讯作者：
Vaughan, Jennifer Wortman
中科院分区：
文献类型：
--
作者：
Cummings, Rachel;Pennock, David M.;Vaughan, Jennifer Wortman
我们考虑私人预测市场的设计，金融市场旨在引发对不确定事件的预测，而不透露太多有关市场参与者行为或信念的信息。我们的目标是设计市场机制，让参与者的交易或赌注以一种能够产生准确预测的方式影响市场行为，但没有任何一个参与者对其他人能够观察到的东西有太大的影响力。我们使用差异隐私工具研究这种机制的可能性和局限性。我们首先设计一种私人一次性投注机制，其中投注者指定对未来事件可能性的信念以及相应的货币投注。赌注在投注者之间重新分配，从而对预测准确的人给予更高的奖励。我们提供了一类投注机制，保证满足真实性、预期预算平衡和其他理想属性，同时额外保证投注者报告信念中的ε-联合差分隐私，并分析可实现的隐私水平和投注者对其自己的报告支付的敏感性之间的权衡。然后，我们询问是否有可能在动态预测市场中获得隐私，将我们的注意力集中在流行的成本函数框架上，在该框架中，自动做市商买卖与未来事件相关的支付证券。我们表明，在一般条件下，这样的做市商不可能同时实现有限的最坏情况损失和ε-差分隐私，而不允许隐私保证随着交易数量的增长（至少是交易数量的对数增长）而极快地退化，使得这样的市场在重视隐私的环境中不切实际。最后，我们提出了几种可能规避这一下限的途径。
We consider the design ofprivate prediction markets, financial markets designed to elicit predictions about uncertain events without revealing too much information about market participants’ actions or beliefs. Our goal is to design market mechanisms in which participants’ trades or wagers influence the market’s behavior in a way that leads to accurate predictions, yet no single participant has too much influence over what others are able to observe. We study the possibilities and limitations of such mechanisms using tools from differential privacy. We begin by designing a private one-shot wagering mechanism in which bettors specify a belief about the likelihood of a future event and a corresponding monetary wager. Wagers are redistributed among bettors in a way that more highly rewards those with accurate predictions. We provide a class of wagering mechanisms that are guaranteed to satisfy truthfulness, budget balance on expectation, and other desirable properties while additionally guaranteeing ε-joint differential privacy in the bettors’ reported beliefs, and analyze the trade-off between the achievable level of privacy and the sensitivity of a bettor’s payment to her own report. We then ask whether it is possible to obtain privacy in dynamic prediction markets, focusing our attention on the popular cost-function framework in which securities with payments linked to future events are bought and sold by an automated market maker. We show that under general conditions, it is impossible for such a market maker to simultaneously achieve bounded worst-case loss and ε-differential privacy without allowing the privacy guarantee to degrade extremely quickly as the number of trades grows (at least logarithmically in number of trades), making such markets impractical in settings in which privacy is valued. We conclude by suggesting several avenues for potentially circumventing this lower bound.
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影响因子：
5.9
作者：
Hanson, R
通讯作者：
Hanson, R
DOI：
10.1016/s1574-0722(07)00080-7
发表时间：
2008
期刊：
--
影响因子：
--
作者：
Joyce E. Berg;Robert Forsythe;F. Nelson;Thomas A. Rietz
通讯作者：
Joyce E. Berg;Robert Forsythe;F. Nelson;Thomas A. Rietz
DOI：
10.1128/microbe.1.459.1
发表时间：
2006
期刊：
Microbe Magazine
影响因子：
--
作者：
P. Polgreen;F. Nelson;G. Neumann
通讯作者：
G. Neumann
DOI：
--
发表时间：
2015
期刊：
Journal of Economics Theory
影响因子：
--
作者：
Nicolas S. Lambert;J. Langford;Jennifer Wortman Vaughan;Yiling Chen;Daniel M. Reeves;Y. Shoham;David M. Pennock
通讯作者：
David M. Pennock
派利分成投注市场异常：赛马场和彩票
DOI：
--
发表时间：
1988
期刊：
影响因子：
--
作者：
Richard H. Thaler;W. Ziemba
通讯作者：
W. Ziemba