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， b谷歌和其他机构谈判，买卖商品，服务，广告等。他们通过拍卖来做出定价、分配和优化等决策。因此，我们现在拥有的拍卖系统的规模远远大于传统的谈判和专门拍卖的“人类规模”，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方面面临许多算法挑战，这是本项目的重点。在这个项目中，研究人员将(a)设计和分析各方（用户、拍卖商、买方和卖方）的模型及其对拍卖的影响；(b)设计和分析超越传统线性利润的混合效用各方存在的机制；(c)量化预算在机制中对真实性、平衡和效用的影响，这一点历来没有得到重视；(d)研究有限计算能力和理性对机制的影响；(e)为期货、组合商品和动态环境设计更丰富的机制；(f)研究拍卖机制的隐私性、安全性和可核查性；(g)研究对上述任务至关重要的各种学习和优化问题。这个项目最终解决的问题是，拥有自然旋角（预算、效用）的各方如何与自动化经济交易系统互动，信息如何在这样的系统中被学习、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，以及通过培养本科生和研究生在经济学、优化等领域的界面工作来探索这些问题。