Information Elicitation Mechanisms for Statistical Estimation
Information Elicitation Mechanisms for Statistical Estimation
复制标题
统计估计的信息获取机制
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Fang
中科院分区：
文献类型：
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作者：
Yuqing Kong;G. Schoenebeck;Biaoshuai Tao;Fang
我们研究学习统计特性的战略代理与私人信息。在这个问题中，必须激励代理人如实透露他们的信息，即使它不能直接验证。此外，代理人报告的信息必须汇总为统计估计数。我们研究两个基本的统计性质：估计一个未知的高斯平均值，和线性回归高斯误差。每个主体的信息都是欧几里得空间中的一个点，我们的主要结果是针对每个问题的两种机制，它们最优地聚合了主体在说真话均衡中的信息：·一种针对大群体的最小（非揭示）机制--主体只需要报告一个值，但该值不一定是他们的点。·非最小群体的机制-代理人需要回答不止一个问题。这些机制是“知情的真实”机制，其中报告未更改的数据（说实话）1）形成严格的贝叶斯纳什均衡2）具有严格高于任何遗忘均衡的福利，其中代理人的策略独立于他们的私人信号。我们还显示了一个最小的启示机制（每个代理只报告她的信号）的限制设置，并使用不可能的结果来证明这种restriction.We建立在同行预测文献中的单问题设置的必要性，但是，大多数以前的工作在这方面的重点是离散信号，而我们的设置是固有的连续性，我们进一步简化代理的报告。
We study learning statistical properties from strategic agents with private information. In this problem, agents must be incentivized to truthfully reveal their information even when it cannot be directly verified. Moreover, the information reported by the agents must be aggregated into a statistical estimate. We study two fundamental statistical properties: estimating the mean of an unknown Gaussian, and linear regression with Gaussian error. The information of each agent is one point in a Euclidean space.Our main results are two mechanisms for each of these problems which optimally aggregate the information of agents in the truth-telling equilibrium:• A minimal (non-revelation) mechanism for large populations — agents only need to report one value, but that value need not be their point.• A mechanism for small populations that is non-minimal — agents need to answer more than one question.These mechanisms are “informed truthful” mechanisms where reporting unaltered data (truth-telling) 1) forms a strict Bayesian Nash equilibrium and 2) has strictly higher welfare than any oblivious equilibrium where agents' strategies are independent of their private signals. We also show a minimal revelation mechanism (each agent only reports her signal) for a restricted setting and use an impossibility result to prove the necessity of this restriction.We build upon the peer prediction literature in the single-question setting; however, most previous work in this area focuses on discrete signals, whereas our setting is inherently continuous, and we further simplify the agents' reports.
用于统计估计的最佳数据采集
DOI：
10.1145/3219166.3219195
发表时间：
2018
期刊：
ACM Conference on Economics and Computation
影响因子：
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作者：
Chen, Yiling;Immorlica, Nicole;Lucier, Brendan;Syrgkanis, Vasilis;Ziani, Juba
通讯作者：
Ziani, Juba