Forecasting Public Sentiments via Mean Field Games

2025/06/10 by Klibanov, Michael V., McGoff, Kevin, Truong, Trung · 1 citation
#35R30 #91A16 #FOS: Mathematics #Numerical Analysis (math.NA)

paper · doi:10.48550/arxiv.2506.08465

Abstract

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.

Citations

• Toward Practical Forecasts of Public Sentiments via Convexification for Mean Field Games: Evidence from Real World COVID-19 Discussion Data
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• Lipschitz Stability Estimate and Uniqueness in the Retrospective Analysis for The Mean Field Games System via Two Carleman Estimates
• A numerical algorithm for inverse problem from partial boundary measurement arising from mean field game problem
• Convexification inversion method for nonlinear SAR imaging with experimentally collected data
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• Learning and Forecasting Opinion Dynamics in Social Networks

Cited by

• Toward Practical Forecasts of Public Sentiments via Convexification for Mean Field Games: Evidence from Real World COVID-19 Discussion Data

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