AF:Small:Unifying Information Aggregation and Information Elicitation

众包是一种通过分布式努力来解决问题的方法。在未来，众包机制将被证明在收集和汇总各种环境中的信息方面是有用的，包括基础科学研究、声誉系统、同行评分以及许多决策环境，包括采购、产品开发、定价等。该项目将设计实用的众包机制，以激励用户报告准确的数据，即使无法直接验证其准确性。其中一个关键部分是准确衡量所报告信息的质量。建立足够强大的机制来处理不同类型的代理是这一建议成功的必要条件。因此，它的应用范围超越了众包，更广泛地应用于大数据（解释来自不同可靠性来源的数据）和算法公平性。研究工作将与研究者的教育和推广活动相结合，研究者有通过教学、推广项目和个人指导向高中、本科生和研究生广泛传播前沿研究的记录。信息引出领域的最新发展表明如何使用信息论概念真实地从代理人那里引出不可验证的信息。这些结果的核心是首先采用巧妙的技术间接测量代理之间的“互信息”，然后根据互信息测量成比例地补偿代理。在学习理论方面也有越来越多的工作，特别是在嘈杂或对抗数据的情况下进行学习。该建议的目标是在信息获取和信息聚合任务之间建立一座桥梁。通过使用信息论的基础，我们可以统一这些领域的进展，并且在许多情况下，将这些任务融合在一起。该项目将承担以下工作。1)通过将信息提取和聚合技术整合为一个流线型流程来设计完整的管道系统。2)研究如何更好地测量真实启发算法所需的信息理论概念。3)解决算法公平性、最优和现实支付以及鲁棒性等重要问题。此外，这一建议将有助于更好地将信息理论融入计算机科学社区，这在数据科学时代是必不可少的。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Crowdsourcing is an approach to solving problems via distributed effort. In the future, crowdsourcing mechanisms should prove useful in gathering and aggregating information in a variety of contexts including basic scientific research, reputation systems, peer grading, and many decision-making contexts including purchasing, product development, pricing, etc. This project will design practical crowdsourcing mechanisms to incentivize users to report accurate data, even when the veracity cannot be directly verified. A key part of this is accurately measuring the quality of the information reported. Building mechanisms sufficiently robust for dealing with diverse types of agents is necessary for the success of this proposal. Thus, it has applications beyond crowdsourcing to big data more broadly (interpreting data from diverse sources of varying reliability) and algorithmic fairness. The research efforts will be integrated with the educational and outreach activities of the investigator, who has a record of broadly disseminating cutting-edge research to high school, undergraduate, and graduate students through teaching, outreach programs, and personal mentoring.Recent developments in the field of information elicitation show how to use information-theoretic concepts to truthfully elicit unverifiable information from agents. The heart of these results is first to employ clever techniques to indirectly measure ``mutual information" between agents, then to compensate agents proportionally to the mutual information measurement. There is also increasing work in learning theory, especially to enable learning with noisy or adversarial data. The goal of this proposal is to create a bridge between the information-elicitation and information-aggregation tasks. By using an information-theoretic underpinning we can unify the progress of these fields, and, in many instances, fuse these tasks together. The project will undertake the following. 1) Design full pipeline systems by uniting the techniques of information elicitation and aggregation into one streamlined process. 2) Examine how to better measure information-theoretic concepts required for truthful elicitation algorithms. 3) Address important concerns such as algorithmic fairness, optimal and realistic payments, and robustness. Moreover, this proposal will help to better integrate information theory into the computer-science community, which is essential in the era of data science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.