CPS: Small: Collaborative Research: Information Design and Price Mechanisms in Platforms for Cyber-Physical Systems with Learning Agents

CPS：小型：协作研究：具有学习代理的网络物理系统平台中的信息设计和价格机制

• 批准号: 1931696
• 负责人: Ramesh Johari
• 金额: $ 20万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931696&HistoricalAwards=false
• 关键词: CPS; Small; Collaborative; Research; Information

The goal of this project is new theory for architectures for crowdsourced markets for cyber-physical systems (CPS), such as power systems and shared vehicle systems. A technology-enabled platform digitally mediates transactions between buyers (e.g., energy consumers/riders) and sellers (e.g., generators/drivers) helping to determine prices and pair buyers and sellers. Large-scale CPS impose fundamental physical constraints than traditional 'cyber' platforms, including environmental uncertainty, spatial difference, delay, and limited resources. The broader impacts of the work include mentoring with high-school students and teachers.The project aims to develop guidance for optimal platform design. A central challenge is the complex behavior of agents, which possess limited information and receive limited feedback about how their actions translate to payoffs, and learn continuously over time to discover optimal strategies. The focus of the project is on the design of two significant levers available to the platform: information feedback and price-setting mechanisms. The research agenda is divided into two thrusts: (1) characterization of the dynamical behavior of agents under limited feedback; and (2) platform design through information sharing and pricing. In the first thrust, each agent is assumed to use a reasonable, `natural' learning algorithm and the goal is to understand how mechanisms available to the platform impact the learning process and eventual collective behavior. Outcomes from the first thrust will feed directly into the second, where the primary aim is to design information mechanisms to supplement pricing in order to shape the aggregate behavior of agents.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个项目的目标是为网络物理系统(CPS)的众包市场(如电力系统和共享车辆系统)的体系结构提供新的理论。支持技术的平台在买家(例如，能源消费者/乘客)和卖家(例如，发电机/司机)之间进行数字调解交易，帮助确定价格并将买家和卖家配对。与传统的“网络”平台相比，大规模的CPS施加了基本的物理限制，包括环境不确定性、空间差异、延迟和有限的资源。这项工作的更广泛影响包括对高中生和教师的指导。该项目旨在为优化平台设计制定指导。一个核心挑战是代理人的复杂行为，他们拥有有限的信息，收到关于他们的行动如何转化为回报的有限反馈，并随着时间的推移不断学习以发现最优策略。该项目的重点是设计平台可用的两个重要杠杆：信息反馈和定价机制。研究议程分为两个部分：(1)描述有限反馈下代理的动态行为；(2)通过信息共享和定价进行平台设计。在第一个推力中，假设每个代理使用一种合理的、“自然的”学习算法，目标是了解平台可用的机制如何影响学习过程和最终的集体行为。第一个奖项的结果将直接进入第二个奖项，第二个奖项的主要目标是设计信息机制来补充定价，以塑造代理人的总体行为。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Experimental Design in Two-Sided Platforms: An Analysis of Bias

两侧平台的实验设计：偏差分析

• DOI: 10.1145/3391403.3399507
• 发表时间: 2020
• 期刊: ACM Economics and Computation (EC
• 作者: Johari, Ramesh;Li, Hannah;Weintraub, Gabriel
• 通讯作者: Weintraub, Gabriel