Mean-field Approaches, optimal TRansport and artificial Intelligence: a mathematical framework for compleX systems
Modern societies rely on interconnected systems in which a large number of complex agents interact amid uncertainty and systemic shocks. In recent years, the mathematical analysis of such stochastic systems has advanced in three closely related areas: mean-field games (MFGs), McKean-Vlasov systems and stochastic partial differential equations (SPDEs). However, multiple roadblocks still prevent the rigorous treatment of intricate real-life problems, such as households in a smart grid, banks in a network, or autonomous vehicles in a city. The MATRIX project (Mean-field Approaches, optimal TRansport and artificial Intelligence: a mathematical framework for compleX systems) aims to address the limitations of the current models by accounting for shared randomness (events affecting all agents simultaneously, such as policy changes or extreme weather), causal consistency (to prevent information leakage), and non-Gaussian dynamics (such as outages or crashes). A special emphasis will be placed on systems incorporating neural-network-driven agents and on their interaction at the mean-field limit. The proposed research is organised into three mutually reinforcing work packages (WPs). WP1 involves constructing reinforcement-learning algorithms for MFGs driven by common noise; the aim is to produce scalable representations of optimal controls under shared shocks. WP2 focuses on establishing rigorous foundations for adapted Wasserstein distances in path-dependent McKean-Vlasov systems, providing quantitative stability and propagation-of-chaos results that respect causality. WP3 tackles the well-posedness of McKean-Vlasov SPDEs with Lévy noise and develops physics-informed neural networks that can approximate the underlying solutions despite discontinuities. The MATRIX project will strengthen the capability to model, predict and control modern complex systems, supporting strategic objectives related to green-energy transition, financial stability and autonomous systems.
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Imported from OpenAIRE United Kingdom | Grant 101276756.
Open sourceOverview
Grant agreement 101276756 | MATRIX
Open sourceModern societies rely on interconnected systems in which a large number of complex agents interact amid uncertainty and systemic shocks. In recent years, the mathematical analysis of such stochastic systems has advanced in three closely related areas: mean-field games (MFGs), McKean-Vlasov systems and stochastic partial differential equations (SPDEs). However, multiple roadblocks still prevent the rigorous treatment of intricate real-life problems, such as households in a smart grid, banks in a network, or autonomous vehicles in a city. The MATRIX project (Mean-field Approaches, optimal TRansport and artificial Intelligence: a mathematical framework for compleX systems) aims to address the limitations of the current models by accounting for shared randomness (events affecting all agents simultaneously, such as policy changes or extreme weather), causal consistency (to prevent information leakage), and non-Gaussian dynamics (such as outages or crashes). A special emphasis will be placed on systems incorporating neural-network-driven agents and on their interaction at the mean-field limit. The proposed research is organised into three mutually reinforcing work packages (WPs). WP1 involves constructing reinforcement-learning algorithms for MFGs driven by common noise; the aim is to produce scalable representations of optimal controls under shared shocks. WP2 focuses on establishing rigorous foundations for adapted Wasserstein distances in path-dependent McKean-Vlasov systems, providing quantitative stability and propagation-of-chaos results that respect causality. WP3 tackles the well-posedness of McKean-Vlasov SPDEs with Lévy noise and develops physics-informed neural networks that can approximate the underlying solutions despite discontinuities. The MATRIX project will strengthen the capability to model, predict and control modern complex systems, supporting strategic objectives related to green-energy transition, financial stability and autonomous systems.