Forecasting Public Sentiments via Mean Field Games

TL;DR

This paper introduces a novel numerical method based on Mean Field Games theory for forecasting public sentiments, providing theoretical convergence guarantees and demonstrating high accuracy through numerical experiments.

Contribution

The paper develops a convexification-based numerical method for sentiment forecasting within the Mean Field Games framework, with proven convergence and practical effectiveness.

Findings

The method converges globally with a quantifiable rate.

Numerical experiments show high accuracy of the convexification approach.

The approach highlights promising features for sentiment prediction applications.

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.