ICES: Small: Risk Aversion in Algorithmic Game Theory and Mechanism Design

ICES：小：算法博弈论和机制设计中的风险规避

• 批准号: 1216103
• 负责人: Evdokia Nikolova
• 金额: $ 37万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216103&HistoricalAwards=false
• 关键词: ICES; Small; Risk; Aversion; Algorithmic

The fields of Algorithmic Game Theory and Mechanism Design study the strategic interaction of multiple agents. The growth of available data and computational power enable these fields to make a real impact in understanding the agents' incentives and improving their decisions and the resulting social outcomes. A grand challenge is the presence of uncertainty and risk, which calls for new game theoretic models and algorithmic techniques. As a brief example, consider going to the airport to catch a flight. In uncertain traffic and a deadline, risk considerations are critical and a less risky, albeit longer, route might be preferable over one that minimizes expected delay. But the fundamental questions do not depend merely on one individual's preferences, but on a broader social model that captures many individuals and their interactions. For instance, how does strategic risk-aware routing by multiple users affect route selection and the overall congestion in the network? And how different is the individually optimal (equilibrium) route selection of multiple risk-averse users from a socially optimal traffic allocation?This research program outlines an agenda for investigating how uncertainty and risk aversion transform traditional models and methodologies employed by Algorithmic Game Theory and Mechanism Design. It will import models and tools from areas with a rich tradition in studying risk, such as Financial Engineering, and adapt them to multi-user computational environments. Ultimately, the goal is to develop new game theoretic models and analysis, as well as new algorithms for solving the computational questions in these models. The program focuses on problems that arise in complex multi-user systems in the real world and has the potential to improve a variety of applications that involve uncertainty and risk-averse users, for example, reduce congestion in transportation and telecommunication networks, enhance the quality of streaming applications over the Internet, increase the safety and security in military operations, autonomous and robot navigation, etc. From a research standpoint, the transformative potential of the proposed research is to fundamentally shift thinking about uncertain multi-user environments away from optimizing simple expected performance and instead towards a systematic treatment of contingencies and risk.

博弈论和机制设计研究多个主体之间的战略互动。 可用数据和计算能力的增长使这些领域能够在理解代理人的动机和改进他们的决策以及由此产生的社会结果方面产生真实的影响。 一个巨大的挑战是不确定性和风险的存在，这需要新的博弈论模型和算法技术。 举个简单的例子，考虑去机场赶飞机。在不确定的交通和最后期限，风险的考虑是至关重要的，一个风险较小，虽然较长的路线可能是最好的一个最小化预期延误。但根本问题并不仅仅取决于个人的偏好，而是取决于一个更广泛的社会模型，它涵盖了许多个人及其互动。例如，多个用户的战略风险感知路由如何影响路由选择和网络中的整体拥塞？多个风险厌恶用户的个体最优（均衡）路线选择与社会最优交通分配有多大不同？本研究计划概述了一个议程，调查如何不确定性和风险规避转换传统的模型和方法所采用的博弈论和机制设计。 它将从具有丰富风险研究传统的领域（如金融工程）导入模型和工具，并使其适应多用户计算环境。 最终，目标是开发新的博弈论模型和分析，以及解决这些模型中计算问题的新算法。该计划侧重于在真实的世界中复杂的多用户系统中出现的问题，并有可能改善涉及不确定性和风险厌恶用户的各种应用，例如，减少交通和电信网络的拥塞，提高互联网上流媒体应用的质量，提高军事行动，自主和机器人导航的安全性和安全性，从研究的角度来看，所提出的研究的变革潜力是从根本上改变对不确定的多用户环境的思考，从优化简单的预期性能，而不是对突发事件和风险的系统处理。