About the project
Objective
The project aims to develop a unified mathematical framework for understanding, predicting, and steering collective behavior in social systems where people interact in overlapping groups. Specifically, it will: 1. model evolutionary games with both group interactions and group-based strategy updating, accounting for finite resources; 2. develop control and adaptive incentive mechanisms to promote cooperation and socially beneficial outcomes. 3. validate the theory using simulations and real-world networks, including political cooperation, co-authorship, and online discussions.
Objective
Social behavior emerges through interactions within teams, communities, and other overlapping groups. Hypergraphs capture these group interactions more naturally than conventional networks based on pairwise links. However, existing models often assume independent multiplayer games, overlook resource constraints, and represent social learning through pairwise updates. General methods for guiding collective behavior and validating these models against real-world data also remain limited. SHAPE builds on the existing Research Pairs project, Analysis and Synergy of Hyper-networked Autonomy at the Societal Scale (ASH-NASS), extending its theoretical results toward more realistic models, intervention methods, and empirical validation.
Cross-disciplinary collaboration
The project brings together researchers from two KTH schools and combines mathematics, evolutionary game theory, network science, nonlinear dynamics, control theory, and online learning:
- Angela Fontan, KTH/EECS: nonlinear network dynamics, collective decision-making, and cyber-physical-human systems.
- Silun Zhang, KTH/SCI: incentive algorithms, nonlinear control, and online learning.
- Anzhi Sheng, joint postdoctoral researcher: evolutionary game theory and network science.
The project also aims to strengthen an emerging collaboration with Professor Joshua B. Plotkin at the University of Pennsylvania, connecting the team’s expertise with evolutionary dynamics and theoretical biology.
