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

该项目的目标是为网络物理系统（CPS）（例如电力系统和共享车辆系统）的众包市场架构提供新理论。技术支持的平台以数字方式调解买家（例如能源消费者/乘客）和卖家（例如发电机/司机）之间的交易，帮助确定价格并将买家和卖家配对。与传统“网络”平台相比，大规模 CPS 施加了基本的物理约束，包括环境不确定性、空间差异、延迟和有限资源。这项工作的更广泛影响包括对高中生和教师的指导。该项目旨在为最佳平台设计提供指导。一个核心挑战是智能体的复杂行为，它们拥有有限的信息，收到的有关其行为如何转化为回报的反馈也有限，并且随着时间的推移不断学习以发现最佳策略。该项目的重点是平台可用的两个重要杠杆的设计：信息反馈和定价机制。研究议程分为两个重点：（1）有限反馈下代理动态行为的表征； (2)通过信息共享和定价进行平台设计。在第一个推力中，假设每个代理都使用合理的、“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。第一个推动力的成果将直接反馈到第二个推动力，其中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价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.