Large-scale socio-technical systems are characterized by the interaction between human factors (such as individual behaviors, group dynamics, organizations, and firms) and physical infrastructure. Prominent examples include energy and transportation networks, which are undergoing a profound transformation driven by renewable energy integration, battery storage, ubiquitous computation and communication technologies, the rise of the sharing economy, and the emergence of new market participants such as prosumers.
A central challenge is understanding the interplay of strategic behavior, economic incentives, network constraints, and regulatory decisions in shaping collective outcomes. Identifying optimal designs and interventions is further complicated by multiple, often competing, objectives, including efficiency, fairness, reliability, and sustainability. Despite recent advances, existing mathematical models often fail to jointly capture these aspects and trade-offs, limiting their ability to provide reliable predictions and support effective decision-making.
The aim of the PhD project is to address these gaps by developing theoretical and computational tools that combine game theory, graph theory, control, and optimization to study the interaction of these factors. Some questions of interest include (but are not limited to):
- Modeling and analyzing strategic equilibrium problems with different incentives, mechanisms and regulatory scenarios, evaluating their performance with respect to different objectives and their trade-offs.
- Developing decentralized and bi-level algorithms to help firms, system operators and users to reach optimal or stable outcomes in dynamical systems with full and partial information;
- Computing systemic optimal interventions to improve the system performance under budget constraints.
Apart from the research project, the candidate is expected to contribute (10% of total workload) to the teaching and supervision of Bachelor and Master students.
