Boltzmann type control of opinion consensus through leaders
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Abstract (arXiv)
The study of formations and dynamics of opinions leading to the so called opinion consensus is one of the most important areas in mathematical modeling of social sciences. Following the Boltzmann type control recently introduced in [G. Albi, M. Herty, L. Pareschi arXiv:1401.7798], we consider a group of opinion leaders which modify their strategy accordingly to an objective functional with the aim to achieve opinion consensus. The main feature of the Boltzmann type control is that, thanks to an instantaneous binary control formulation, it permits to embed the minimization of the cost functional into the microscopic leaders interactions of the corresponding Boltzmann equation. The related Fokker-Planck asymptotic limits are also derived which allow to give explicit expressions of stationary solutions. The results demonstrate the validity of the Boltzmann type control approach and the capability of the leaders control to strategically lead the followers opinion.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 May 2014.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 0; 2026: 3.
6 papers.
- 2022A kinetic approach to consensus-based segmentation of biomedical imagescited by 1
- 2026MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Datacited by 1
- 2024Data/moment-driven approaches for fast predictive control of collective dynamicscited by 0
- 2024Optimal Control of Agent-Based Dynamics under Deep Galerkin Feedback Lawscited by 0
- 2026Discovery of interaction and diffusion kernels in particle-to-mean-field multi-agent systemscited by 0
- 2026A Multiscale Kinetic Framework for Image Segmentation: From Particle Systems to Continuum Modelscited by 0
- 2026A Multiscale Kinetic Framework for Image Segmentation: From Particle Systems to Continuum Models27 May 2026
- 2026MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Data1 Apr 2026
- 2026Discovery of interaction and diffusion kernels in particle-to-mean-field multi-agent systems16 Mar 2026
- 2024Optimal Control of Agent-Based Dynamics under Deep Galerkin Feedback Laws13 Jun 2024
- 2024Data/moment-driven approaches for fast predictive control of collective dynamics23 Feb 2024
- 2022A kinetic approach to consensus-based segmentation of biomedical images8 Nov 2022