AF: Small: Computer Science and Decision Making

AF：小：计算机科学与决策

• 批准号: 0916782
• 负责人: Fred Roberts
• 金额: $ 40万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916782&HistoricalAwards=false
• 关键词: AF; Small; Computer; Science; Decision

Increasingly, automated economic transaction systems in eBay, Google and other institutions negotiate, buy and sell goods, services, advertisements etc. They use auctions to make decisions including pricing, allocation and optimization. As a result, we now have auctions systems far greater in scale than the traditional "human scale" of negotiations and specialized auctions, and they impact our lives in sophisticated ways. These systems face many algorithmic challenges in the interface of Economics, Learning Theory, and Optimization, which is the focus of this project.In this project researchers will (a) design and analyze models for the various parties (users, auctioneer, buyer and seller) and their impact on the auctions; (b) design and analyze mechanisms in the presence of parties with mixed utilities that go beyond the traditional linear profit; (c) quantify impacts of budgets in mechanisms on truthfulness, equilibria and utilities, which has been traditionally underemphasized; (d) study the effect of bounded computational power and rationality on mechanisms; (e) design richer mechanisms for futures, combinatorial goods as well as dynamic settings; (f) study privacy, security and verifiability of auction mechanisms; (g) study the various learning and optimization problems that are fundamental to the tasks above.This project ultimately addresses the questions of how various parties with natural knobs (budget, utility) interact with automated economic transaction systems, how information is learned, used and controlled in such systems, and how these systems will evolve over the long term. The project explores these questions via the specific research tasks above, as well as via training undergraduate and graduate students to work in the interface of Economics, Optimization and other areas.

越来越多的自动化经济交易系统在eBay，谷歌和其他机构的谈判，购买和出售商品，服务，广告等，他们使用拍卖作出决定，包括定价，分配和优化。因此，我们现在拥有的拍卖系统的规模远远大于传统的“人类规模”的谈判和专业拍卖，它们以复杂的方式影响着我们的生活。这些系统在经济学、学习理论和优化的接口中面临许多算法挑战，这是本项目的重点。（B）设计和分析具有超越传统线性利润的混合效用的各方存在的机制;（c）量化预算在机制中对真实性、均衡和效用的影响，这一点历来没有得到充分强调;（d）研究有限计算能力和理性对机制的影响;（e）为未来、组合商品以及动态设置设计更丰富的机制;（f）研究拍卖机制的隐私、安全和可验证性;（g）研究对上述任务至关重要的各种学习和优化问题。本项目最终解决的问题是，（预算、公用事业）与自动化经济交易系统交互，信息如何在此类系统中学习、使用和控制，以及这些系统将如何长期发展。该项目通过上述具体的研究任务，以及通过培训本科生和研究生在经济学，优化和其他领域的接口工作来探索这些问题。