{"ID":5493407,"CreatedAt":"2026-07-01T11:34:04.422684973Z","UpdatedAt":"2026-07-01T11:34:04.422684973Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2402.09297","arxiv_id":"2402.09297","title":"Reconstructing a state-independent cost function in a mean-field game model","abstract":"In this short note, we consider an inverse problem to a mean-field games system where we are interested in reconstructing the state-independent running cost function from observed value-function data. We provide an elementary proof of a uniqueness result for the inverse problem using the standard multilinearization technique. One of the main features of our work is that we insist that the population distribution be a probability measure, a requirement that is not enforced in some of the existing literature on theoretical inverse mean-field games.","url_abs":"https://arxiv.org/abs/2402.09297v2","url_pdf":"https://arxiv.org/pdf/2402.09297v2","authors":"Kui Ren, Nathan Soedjak, Kewei Wang, Hongyu Zhai","published":"2024-02-14T16:37:09Z","has_code":false}