Two Strongly Truthful Mechanisms for Three Heterogeneous Agents Answering One Question
Two Strongly Truthful Mechanisms for Three Heterogeneous Agents Answering One Question
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三个异质代理回答一个问题的两种强真实机制
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Fang
中科院分区：
文献类型：
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作者：
G. Schoenebeck;Fang
同伴预测机制通过将代理人的报告与他们的同伴进行比较，激励自利代理人即使在没有验证的情况下也如实地报告他们的信号。我们提出了两个新的机制，源和目标差分对等预测，并证明了一个非常一般的设置非常强的保证。我们的差分对等预测机制是非常真实的：说出真相是一个严格的贝叶斯纳什均衡。此外，说实话的报酬严格高于任何其他均衡，不包括排列均衡，它与说实话的报酬相同。这些保证适用于代理之间的不对称先验，而机制在单一问题设置中不需要知道这些先验（无细节）。此外，他们只需要三个代理，每个代理提交一个项目报告：两个报告他们的信号（答案），另一个报告她的预测（预测另一个代理的报告之一）。我们的证明技术是简单的，概念上的动机，并打开对数评分规则的特殊属性。此外，我们可以将贝叶斯真理血清机制[20]重新纳入我们的框架。我们还可以将我们的结果扩展到连续信号的设置，对真实均衡的最优性的保证稍弱。
Peer prediction mechanisms incentivize self-interested agents to truthfully report their signals even in the absence of verification by comparing agents’ reports with their peers. We propose two new mechanisms, Source and Target Differential Peer Prediction, and prove very strong guarantees for a very general setting. Our Differential Peer Prediction mechanisms are strongly truthful: Truth-telling is a strict Bayesian Nash equilibrium. Also, truth-telling pays strictly higher than any other equilibria, excluding permutation equilibria, which pays the same amount as truth-telling. The guarantees hold for asymmetric priors among agents, which the mechanisms need not know (detail-free) in the single question setting. Moreover, they only require three agents, each of which submits a single item report: two report their signals (answers), and the other reports her forecast (prediction of one of the other agent’s reports). Our proof technique is straightforward, conceptually motivated, and turns on the logarithmic scoring rule’s special properties. Moreover, we can recast the Bayesian Truth Serum mechanism [20] into our framework. We can also extend our results to the setting of continuous signals with a slightly weaker guarantee on the optimality of the truthful equilibrium.
多任务同行预测的局限性
DOI：
10.1145/3465456.3467642
发表时间：
2021
期刊：
EC '21: Proceedings of the 22nd ACM Conference on Economics and Computation
影响因子：
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作者：
Zheng, Shuran;Yu, Fang-Yi;Chen, Yiling
通讯作者：
Chen, Yiling
从喧闹的人群中获取信息
DOI：
10.1145/3442381.3449840
发表时间：
2021
期刊：
Proceedings of the Web Conference (WWW '21
影响因子：
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作者：
Schoenebeck, Grant;Yu, Fang-Yi;Zhang, Yichi
通讯作者：
Zhang, Yichi
学习和高度真实的多任务同伴预测：一种变分方法
DOI：
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发表时间：
2021
期刊：
12th Innovations in Theoretical Computer Science Conference (ITCS 2021
影响因子：
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作者：
Schoenebeck, Grant;Yu, Fang-Yi
通讯作者：
Yu, Fang-Yi