Research
Preprints:
"A machine learning method for Stackelberg mean field games." G. Dayanıklı, M. Laurière. (2023, preprint). [arXiv]
"From Nash equilibrium to social optimum and vice versa: a mean field perspective." R. Carmona, G. Dayanıklı, F. Delarue, M. Laurière. (2023, preprint). [arXiv]
Publications:
"Learning discrete-time major-minor mean field games." K. Cui, G. Dayanıklı, M. Laurière, M. Geist, O. Pietquin, H. Koeppl. (2024) Proceedings of AAAI Conference on Artificial Intelligence. [arXiv]
"Deep learning for population-dependent controls in mean field control problems." G. Dayanıklı, M. Laurière, J. Zhang. (accepted as an Extended Abstract at 2024 AAMAS). [arXiv]
"Multi-population mean field games with multiple major players: Application to carbon emission regulations." G. Dayanıklı, M. Laurière. (accepted at 2024 American Control Conference (ACC)). [arXiv]
"Finite state graphon games with applications to epidemics." A. Aurell, R. Carmona, G. Dayanıklı, M. Laurière. (2022) Dynamic Games and Applications. [arXiv]
"Optimal incentives to mitigate epidemics: A Stackelberg mean field game approach." A. Aurell, R. Carmona, G. Dayanıklı, M. Laurière. (2022) SIAM Journal on Control and Optimization. [arXiv]
"Mean field models to regulate carbon emissions in electricity production." R. Carmona, G. Dayanıklı, M. Laurière. (2022) Dynamic Games and Applications. [arXiv]
"Mean field game model for an advertising competition in a duopoly." R. Carmona, G. Dayanıklı. (2021) International Game Theory Review. [arXiv]