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Mean-field Approaches, optimal TRansport and artificial Intelligence: a mathematical framework for compleX systems

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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

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recordType

award

status

SIGNED

region

EU

value

276187.92

unit

EUR