Gökçe Dayanıklı

About

John Hart is a Professor of Computer Science and the Director of the Office of Professional Education at the University of Illinois Urbana-Champaign. His role involves overseeing professional education initiatives and programs within the university system. The page indicates his involvement in the System Credentials Working Group, which explores non-degree credential offerings across the University of Illinois System, highlighting his engagement in educational program development and credentialing strategies.

Research topics

• Economics
• Computer Science
• Artificial Intelligence
• Microeconomics
• Statistics
• Mathematical economics
• Mathematical optimization
• Ecology
• Mathematics
• Environmental economics

Selected publications

• Multi-population Mean Field Games with Multiple Major Players: Application to Carbon Emission Regulations

  2024-07-10 · 9 citations

  article1st authorCorresponding

  In this paper, we propose and study a mean field game model with multiple populations of minor players and multiple major players, motivated by applications to the regulation of carbon emissions. Each population of minor players represent a large group of electricity producers and each major player represents a regulator. We first characterize the minor players' equilibrium controls using forward-backward differential equations, and show existence and uniqueness of the minor players' equilibrium…

  PublisherDOI

• A Machine Learning Method for Stackelberg Mean Field Games

  Mathematics of Operations Research · 2024-12-02 · 8 citations

  article1st authorCorresponding

  We propose a single-level numerical approach to solve Stackelberg mean field game (MFG) problems. In the Stackelberg MFG, an infinite population of agents plays a noncooperative game and chooses their controls to optimize their individual objectives while interacting with the principal and other agents through the population distribution. The principal can influence the mean field Nash equilibrium at the population level through policies, and she optimizes her own objective, which depends on the…

  PublisherDOI

• Learning Discrete-Time Major-Minor Mean Field Games

  Proceedings of the AAAI Conference on Artificial Intelligence · 2024-03-24 · 3 citations

  articleOpen access

  Recent techniques based on Mean Field Games (MFGs) allow the scalable analysis of multi-player games with many similar, rational agents. However, standard MFGs remain limited to homogeneous players that weakly influence each other, and cannot model major players that strongly influence other players, severely limiting the class of problems that can be handled. We propose a novel discrete time version of major-minor MFGs (M3FGs), along with a learning algorithm based on fictitious play and partit…

  PublisherOA PDFDOI

• Multi-population Mean Field Games with Multiple Major Players: Application to Carbon Emission Regulations

  arXiv (Cornell University) · 2023-09-28 · 2 citations

  preprintOpen access1st authorCorresponding

  In this paper, we propose and study a mean field game model with multiple populations of minor players and multiple major players, motivated by applications to the regulation of carbon emissions. Each population of minor players represent a large group of electricity producers and each major player represents a regulator. We first characterize the minor players equilibrium controls using forward-backward differential equations, and show existence and uniqueness of the minor players equilibrium.…

  PublisherOA PDFDOI

• A Machine Learning Method for Stackelberg Mean Field Games

  arXiv (Cornell University) · 2023-02-21 · 2 citations

  preprintOpen access1st authorCorresponding

  We propose a single-level numerical approach to solve Stackelberg mean field game (MFG) problems. In Stackelberg MFG, an infinite population of agents play a non-cooperative game and choose their controls to optimize their individual objectives while interacting with the principal and other agents through the population distribution. The principal can influence the mean field Nash equilibrium at the population level through policies, and she optimizes her own objective, which depends on the popu…

  PublisherOA PDFDOI

Frequent coauthors

• Mathieu Laurière

  New York University Shanghai

  19 shared

• René Carmona

  10 shared

• Alexander Aurell

  Princeton University

  5 shared

• Matthieu Geist

  3 shared

• Kai Cui

  Technical University of Darmstadt

  2 shared

• Heinz Koeppl

  Technical University of Darmstadt

  2 shared

• Olivier Pietquin

  2 shared

• Maxim Bichuch

  1 shared

Labs

• System Credentials Working GroupPI

Education

• PhD, Operations Research & Financial Engineering

  Princeton University

  2022

Similar researchers at University of Illinois Urbana-Champaign

• Dr. Sabyasachi Chatterjeeh-70
• Ruoqi Yuh-61
• Trevor H Parkh-61