ITR: Axioms and Algorithms for Reputation

ITR：声誉公理和算法

• 批准号: 0428868
• 负责人: Benjamin Van Roy
• 金额: $ 120万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0428868&HistoricalAwards=false
• 关键词: ITR; Axioms; Algorithms; Reputation

The goal of this project is to design metrics of online reputation that are both robust to gaming agents and efficiently computable. This will be done through development of an axiomatic framework for reputation together with novel algorithms that make the framework useful. This work will be grounded in practical online reputation systems of contemporary interest, and issues arising in real systems will guide axiomatic and algorithmic developments.To navigate the complex web of information and agents on the Internet, society has increasingly come to rely on automated systems that assess reputation. Some prominent examples of such reputation systems are the PageRank mechanism used by the Google search engine, the feedback based rating scheme for the online auction site eBay, and the incentive scheme used by the popular file sharing system KaZaA. All these reputation systems are vulnerable to gaming by selfish agents trying to improve their own rating. Fundamentally, these reputation systems suffer from a misalignment between economic incentives of individual users and aggregate value to society. This research program will have a significant impact on several important problem domains such as ranking of web pages and blogs, online marketplaces, and peer-to-peer systems.Online services such as search engines and auction sites have become an important part of our national infrastructure and need to be protected against greedy or malicious tampering. This research will contribute to the robustness of such systems. Additionally, the purpose of reputation systems is to gather highly imperfect information from many sources, and to process it into a comprehensible prediction of outcomes (e.g., whether a vendor will be reliable). Certain national security needs could conceivably be met with variants of reputation systems. One of the great challenges of intelligence assessment is to determine what information is reliable and what is not. The proposed research should help in addressing this challenge.Elements of the research will be integrated into undergraduate and graduate courses on optimization, game theory, Markov chains, public policy, and Internet technology.

这个项目的目标是设计的在线声誉，既强大的游戏代理和有效的可计算的指标。这将通过开发一个公理框架的声誉连同新颖的算法，使框架有用。这项工作将立足于当代感兴趣的实用在线声誉系统，在真实的系统中出现的问题将指导公理和算法的发展。为了在互联网上浏览复杂的信息和代理网络，社会越来越依赖于评估声誉的自动化系统。这种声誉系统的一些突出例子是Google搜索引擎使用的PageRank机制，在线拍卖网站eBay的基于反馈的评级方案，以及流行的文件共享系统KaZaA使用的激励方案。所有这些声誉系统都容易受到自私代理试图提高自己评级的游戏的影响。从根本上说，这些声誉系统受到个人用户的经济激励和社会总价值之间的不一致。 该研究计划将对网页和博客排名、在线市场和对等系统等几个重要问题领域产生重大影响。搜索引擎和拍卖网站等在线服务已成为我们国家基础设施的重要组成部分，需要保护其免受贪婪或恶意篡改。 这项研究将有助于这种系统的鲁棒性。 此外，声誉系统的目的是从许多来源收集高度不完美的信息，并将其处理成可理解的结果预测（例如，供应商是否可靠）。某些国家安全需要可以通过各种不同的声誉系统来满足。情报评估面临的最大挑战之一是确定哪些信息是可靠的，哪些是不可靠的。拟议的研究应有助于应对这一挑战。研究的要素将整合到有关优化、博弈论、马尔科夫链、公共政策和互联网技术的本科生和研究生课程中。