Open projects
Conducting an Orchestra: Learning in Stackelberg Games with Maestro
Various strategic interactions involve hierarchical decision-making processes, where one entity leads and others react accordingly. Stackelberg games provide a mathematical framework to model such scenarios, capturing the dynamics between a leader and multiple followers. However, in many real-world applications of such structures, we often only observe the response of the followers but we are unsure about the optimization problem that the followers are optimizing. This research question, also known as inverse game theory, poses significant challenges, further complicated by noisy observations, bounded rationality, and many more. This project aims to develop methodologies for inferring the utility functions of followers in such scenarios by leveraging observed actions and partial knowledge of their parameters, working on Swissgrid energy market data provided by the MAESTRO project.
Keywords: Game Theory, Learning, Data Analysis, Energy Market
Contacts: ishilov@ethz.ch, jmatt@ethz.ch