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

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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.