Forecasting Public Sentiments via Mean Field Games
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911239983071232 |
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| author | Klibanov, Michael V. McGoff, Kevin Truong, Trung |
| author_facet | Klibanov, Michael V. McGoff, Kevin Truong, Trung |
| contents | Motivated by the goal of forecasting public sentiments, we consider a forecasting problem in the context of the Mean Field Games theory. We develop a numerical method, which is a version of the so-called convexification method. We provide theoretical convergence analysis that establishes global convergence of the method with a convergence rate. We also conduct numerical experiments that demonstrate the accurate performance of the convexification technique and highlight some promising features of this approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08465 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forecasting Public Sentiments via Mean Field Games Klibanov, Michael V. McGoff, Kevin Truong, Trung Numerical Analysis 91A16, 35R30 Motivated by the goal of forecasting public sentiments, we consider a forecasting problem in the context of the Mean Field Games theory. We develop a numerical method, which is a version of the so-called convexification method. We provide theoretical convergence analysis that establishes global convergence of the method with a convergence rate. We also conduct numerical experiments that demonstrate the accurate performance of the convexification technique and highlight some promising features of this approach. |
| title | Forecasting Public Sentiments via Mean Field Games |
| topic | Numerical Analysis 91A16, 35R30 |
| url | https://arxiv.org/abs/2506.08465 |