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ORCIDGökçe Dayanikli
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2020 – today
- 2024
[c4]Kai Cui
, Gökçe Dayanikli, Mathieu Laurière, Matthieu Geist, Olivier Pietquin, Heinz Koeppl:
Learning Discrete-Time Major-Minor Mean Field Games. AAAI 2024: 9616-9625- [c3]Gökçe Dayanikli, Mathieu Laurière:
Multi-population Mean Field Games with Multiple Major Players: Application to Carbon Emission Regulations. ACC 2024: 5075-5081 - [c2]Gökçe Dayanikli, Mathieu Laurière, Jiacheng Zhang:
Deep Learning for Population-Dependent Controls in Mean Field Control Problems with Common Noise. AAMAS 2024: 2231-2233 - 2023
- [c1]Mohammad Dehghanimohammadabadai, Gökçe Dayanikli:
Enhancing Pandemic Preparedness Using Mean Field and Simulation Modeling. WSC 2023: 970-981 - [i3]René A. Carmona, Gökçe Dayanikli, François Delarue, Mathieu Laurière:
From Nash Equilibrium to Social Optimum and vice versa: a Mean Field Perspective. CoRR abs/2312.10526 (2023) - [i2]Kai Cui, Gökçe Dayanikli, Mathieu Laurière, Matthieu Geist, Olivier Pietquin, Heinz Koeppl:
Learning Discrete-Time Major-Minor Mean Field Games. CoRR abs/2312.10787 (2023) - 2022
- [j4]Alexander Aurell, René Carmona, Gökçe Dayanikli, Mathieu Laurière:
Finite State Graphon Games with Applications to Epidemics. Dyn. Games Appl. 12(1): 49-81 (2022) - [j3]René Carmona, Gökçe Dayanikli, Mathieu Laurière:
Mean Field Models to Regulate Carbon Emissions in Electricity Production. Dyn. Games Appl. 12(3): 897-928 (2022) - [j2]Alexander Aurell, René Carmona, Gökçe Dayanikli, Mathieu Laurière:
Optimal Incentives to Mitigate Epidemics: A Stackelberg Mean Field Game Approach. SIAM J. Control. Optim. 60(2): S294-S322 (2022) - [i1]René Carmona, Gökçe Dayanikli:
Mean Field Model for an Advertising Competition in a Duopoly. CoRR abs/2201.05182 (2022) - 2021
- [j1]René Carmona, Gökçe Dayanikli:
Mean Field Game Model for an Advertising Competition in a Duopoly. IGTR 23(4): 2150024:1-2150024:26 (2021)
Coauthor Index
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