Games, Decisions & Networks Research Lab

Artificial intelligence (AI) applications are becoming increasingly prevalent in social systems, such as the digital economy and urban mobility. They are now considered socio-technical systems, including human and AI-powered decision-makers. This raises new questions about how AI-powered decision-makers will interact with each other and with humans in such emerging dynamical systems and how we can apply control-theoretic methodologies for their reliable integration into our society. Therefore, at the GDN Lab,

  • We focus on developing a foundational understanding of learning and autonomy in complex, dynamic, and multi-agent systems.
  • We develop new methodologies for analyzing, controlling, and optimizing socio-technical systems.
  • We apply these methodologies to specific problems in urban mobility, robotics, and the digital economy.

See the following posters and the video recording as examples of our recent research projects:

A. S. Donmez, Y. Arslantas, and M. O. Sayin, “Team-fictitious play for reaching team-Nash equilibrium in multi-team games,” In Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024.
M. O. Sayin, K. Zhang, D. S. Leslie, T. Başar, and A. Ozdaglar, “Decentralized Q-learning in zero-sum Markov games,” In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS), virtual, 2021.
University of Minnesota, Institute for Mathematics & Its Applications, Data Science Seminar in Apr. 2023
ACM Conference on Economics and Computation, University of Colorado Boulder in July 2022

We are looking for new team members. Please get in touch with us if you are interested in!

Recent News

  • [Nov. 2024] Our paper “Balancing anarchy and efficiency: partial team formations and learning in potential games” got accepted to the Turkish Journal of Electrical Engineering & Computer Sciences.
  • [Sep. 2024] Our paper “Team-fictitious play for reaching team-Nash equilibrium in multi-team games” will appear in NeurIPS’24.
  • [Sep. 2024] Our papers “Strategic control of intersections for efficient traffic routing without tolls” and “Strategic control of experience-weighted attraction model in human-AI interactions” will appear in IFAC CPHS’24.
  • [Aug. 2024] Yuksel Arslantas presented his M.S. Thesis “Heterogeneity and strategic sophistication in multi-agent reinforcement learning” successfully.
  • [July 2024] Our paper “Generalized individual Q-learning for polymatrix games with partial observations” will appear in IEEE CDC’24.
  • [Jun. 2024] Our paper “(Smooth) fictitious-play in identical-interest stochastic games with independent continuation-payoff estimates” got accepted to the Applied and Computational Mathematics, Special Issue on Control, Teams, and Games (dedicated in honor of Tamer Başar).
  • [Jun. 2024] Our paper “Strategizing against Q-learners: A control-theoretical approach” got accepted to the IEEE Control Systems Letters (L-CSS).
  • [Mar. 2024] Dr. Sayin gave an invited talk titled “Taming the Wild West of AI: Game Theory for Predictable AI Interactions” at the Workshop on Vector Optimization, Active Learning, Design of Experiments, Game Theory and Their Applications.
  • [Dec. 2023] Dr. Sayin gave an invited talk titled “Convergence of Heterogeneous Learning Dynamics in Zero-sum Stochastic Games” at the IEEE Conference on Decision and Control, “Workshop on Control, Game, and Learning Theory for Security and Privacy”. [url]
  • [Oct 2023] Dr. Sayin gave an invited talk titled “Efficient-Q Learning for Stochastic Games” at the Annual INFORMS Meeting, “Algorithmic Learning in Games” Session.
  • [Sep 2023] Dr. Sayin gave an invited talk titled “Convergent Heterogenous Learning for Zero-sum Stochastic Games” at the Annual Allerton Conference.