ICES: Small: Collaborative Research: Algorithms and Mechanisms for Pricing, Influencing Dynamics, and Economic Optimization

ICES：小型：协作研究：定价、影响动态和经济优化的算法和机制

• 批准号: 1101215
• 负责人: Avrim Blum
• 金额: $ 19.96万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1101215&HistoricalAwards=false
• 关键词: ICES; Small; Collaborative; Research; Algorithms

The intersection of Computer Science and Economics has become increasingly important to the development of both fields. Today's software often must handle multiple individuals with their own interests in mind, bringing incentive issues to the forefront in algorithm design. Economic problems, especially in electronic commerce, increasingly involve large numbers of goods and buyers as well as unknown and complex market conditions, making algorithms and machine learning of key importance. This project aims to address fundamental questions at the heart of the intersection of these two fields. These include problems of modeling and influencing behavior in systems with large numbers of agents and components, problems of optimization under complex and changing preferences and constraints in electronic commerce, and problems of efficiently computing and estimating basic economic quantities.This project specifically has three main thrusts. The first is development of algorithms and analysis techniques for positively influencing dynamics in systems with large numbers of interacting agents. For example, if behavior is currently at a poor-quality equilibrium, when can additional information or few targeted incentives be used "nudge" behavior towards a good equilibrium? This applies not only to self-interested agents but also to components in a distributed system acting on local information (such as sensors in a sensor network). The second thrust is development of algorithms for efficiently computing or estimating important economic quantities. This includes approximately computing Nash equilibria in large interactions, and learning submodular functions and other common valuation classes from observations of behavior or experimentation. The third thrust is developing mathematical frameworks for understanding and solving problems of pricing and resource allocation in settings with unknown and changing market conditions. These frameworks are crucial for next-generation markets of resources such as computing power and network bandwidth.

计算机科学和经济学的交叉对这两个领域的发展变得越来越重要。 今天的软件通常必须处理多个人，考虑到他们自己的利益，将激励问题带到算法设计的最前沿。 经济问题，特别是电子商务中的经济问题，越来越多地涉及大量的商品和买家以及未知和复杂的市场条件，使得算法和机器学习变得至关重要。 该项目旨在解决这两个领域交叉的核心问题。 这些问题包括在具有大量代理和组件的系统中建模和影响行为的问题，在电子商务中复杂和不断变化的偏好和约束下的优化问题，以及有效计算和估计基本经济量的问题。第一个是开发算法和分析技术，积极影响系统中的动态与大量的相互作用的代理。 例如，如果行为目前处于低质量均衡，那么何时可以使用额外的信息或少量有针对性的激励措施来“推动”行为走向良好的均衡？ 这不仅适用于自利代理，也适用于分布式系统中作用于本地信息的组件（例如传感器网络中的传感器）。 第二个重点是开发有效计算或估计重要经济量的算法。这包括近似计算大型交互中的纳什均衡，以及从行为或实验的观察中学习子模函数和其他常见的估值类。第三个重点是发展数学框架，以理解和解决在未知和不断变化的市场条件下的定价和资源配置问题。 这些框架对于计算能力和网络带宽等下一代资源市场至关重要。