An Information Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling
An Information Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling
复制标题
用于设计奖励说真话的信息获取机制的信息理论框架
DOI：
--
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
G. Schoenebeck
中科院分区：
文献类型：
--
作者：
Yuqing Kong;G. Schoenebeck
在信息无法验证的情况下，我们提出了一个简单而强大的信息理论框架——互信息范式——用于信息获取机制。我们的框架向每个代理支付其信号与同伴信号之间的互信息度量。我们要求互信息测量具有关键属性，即对两个随机变量的任何“数据处理”都会减少它们之间的互信息。我们确定了概括香农互信息的信息度量。我们的互信息范式克服了无需验证的信息获取中的两个主要挑战：（1）如何激励高质量报告并避免代理合谋报告随机或相同的响应； （2）如何激励自认为少数的代理人如实报告。在信息测量的帮助下，我们发现（1）我们使用该范式设计了一系列新颖的机制，其中说真话是一种主导策略，并且比任何其他策略配置文件支付更好（在问题数量很大的多问题、无细节、最小设置中）； (2) 我们通过提供对现有机制的统一理论理解来展示我们框架的多功能性——贝叶斯真值血清 Prelec (2004) 以及 Dasgupta 和 Ghosh (2013)——将它们映射到我们的框架中，以便可以轻松重建这些现有机制的理论结果。我们还给出了一个不可能的结果，从某种意义上说明了我们框架的最优性。
In the setting where information cannot be verified, we propose a simple yet powerful information theoretical framework—the Mutual Information Paradigm—for information elicitation mechanisms. Our framework pays every agent a measure of mutual information between her signal and a peer’s signal. We require that the mutual information measurement has the key property that any “data processing” on the two random variables will decrease the mutual information between them. We identify such information measures that generalize Shannon mutual information. Our Mutual Information Paradigm overcomes the two main challenges in information elicitation without verification: (1) how to incentivize high-quality reports and avoid agents colluding to report random or identical responses; (2) how to motivate agents who believe they are in the minority to report truthfully. Aided by the information measures, we found (1) we use the paradigm to design a family of novel mechanisms where truth-telling is a dominant strategy and pays better than any other strategy profile (in the multi-question, detail free, minimal setting where the number of questions is large); (2) we show the versatility of our framework by providing a unified theoretical understanding of existing mechanisms—Bayesian Truth Serum Prelec (2004) and Dasgupta and Ghosh (2013)—by mapping them into our framework such that theoretical results of those existing mechanisms can be reconstructed easily. We also give an impossibility result that illustrates, in a certain sense, the the optimality of our framework.