多任务同伴预测中的信息真实性
Informed Truthfulness in Multi-Task Peer Prediction
摘要：
同伴预测的问题是从没有任何客观的基础事实来评分报告的设置中的代理中获取信息。同伴预测机制试图利用信号之间的相关性，使激励与真实的报告相一致。一个长期存在的问题是无信息均衡的可能性。对于二进制信号，多任务机制[Dasgupta-Ghosh '13]实现了强真实性，因此真实均衡严格地最大化了收益。我们的信号分布的特征条件，这种机制仍然强烈真实的非二进制信号，也提供了一个大大简化的证明。我们引入了相关协议（CA）机制，它处理多个信号，并提供知情的真实性：没有策略配置文件提供更多的回报比真实的报告，在均衡和真实的均衡是严格优于任何不知情的策略（代理人避免获得信号的努力）。CA机制是最大强真实的，因为在一个广泛的机制类中没有机制对更大的信号分布族是强真实的。我们还提供了一个无细节版本的机制，该机制消除了设计人员的任何知识要求，使用许多任务的报告来学习统计数据，同时保留ε-知情的真实性。
英文摘要：
The problem of peer prediction is to elicit information from agents in settings without any objective ground truth against which to score reports. Peer prediction mechanisms seek to exploit correlations between signals to align incentives with truthful reports. A long-standing concern has been the possibility of uninformative equilibria. For binary signals, a multi-task mechanism [Dasgupta-Ghosh '13] achieves strong truthfulness, so that the truthful equilibrium strictly maximizes payoff. We characterize conditions on the signal distribution for which this mechanism remains strongly-truthful with non-binary signals, also providing a greatly simplified proof. We introduce the Correlated Agreement (CA) mechanism, which handles multiple signals and provides informed truthfulness: no strategy profile provides more payoff in equilibrium than truthful reporting, and the truthful equilibrium is strictly better than any uninformed strategy (where an agent avoids the effort of obtaining a signal). The CA mechanism is maximally strongly truthful, in that no mechanism in a broad class of mechanisms is strongly truthful on a larger family of signal distributions. We also give a detail-free version of the mechanism that removes any knowledge requirements on the part of the designer, using reports on many tasks to learn statistics while retaining epsilon-informed truthfulness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
