ITR: Axioms and Algorithms for Reputation

这个项目的目标是设计在线声誉的指标，既对游戏代理健壮，又能有效地计算。这将通过开发一个公理化的声誉框架以及使框架有用的新算法来实现。这项工作将以当代关注的实际在线声誉系统为基础，实际系统中出现的问题将指导公理和算法的发展。为了驾驭互联网上复杂的信息网络和代理人，社会越来越依赖于评估声誉的自动化系统。这种信誉系统的一些突出例子是谷歌搜索引擎使用的PageRank机制，在线拍卖网站eBay基于反馈的评级机制，以及流行的文件共享系统KaZaA使用的激励机制。所有这些声誉系统都很容易受到自私的代理人试图提高自己评级的影响。从根本上说，这些声誉系统受到个人用户的经济激励与社会总价值之间的不一致的影响。这项研究计划将对几个重要的问题领域产生重大影响，如网页和博客的排名，在线市场和点对点系统。在线服务，如搜索引擎和拍卖网站已经成为我们国家基础设施的重要组成部分，需要防止贪婪或恶意篡改。这项研究将有助于这类系统的鲁棒性。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）。可以想象，某些国家安全需求可以通过各种形式的声誉系统得到满足。情报评估的一大挑战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解决这一挑战。研究内容将被整合到本科和研究生课程中，包括最优化、博弈论、马尔可夫链、公共政策和互联网技术。

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.