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Sparse control of Hegselmann-Krause models: black hole and declustering. (English) Zbl 1422.91620

Summary: This paper elaborates control strategies to prevent clustering effects in opinion formation models. This is the exact opposite of numerous situations encountered in the literature where, on the contrary, one seeks controls promoting consensus. In order to promote declustering, instead of using the classical variance that does not capture well the phenomenon of dispersion, we introduce an entropy-type functional that is adapted to measuring pairwise distances between agents. We then focus on a Hegselmann-Krause-type system and design declustering sparse controls in both finite-dimensional and kinetic models. We provide general conditions characterizing whether clustering can be avoided as a function of the initial data. Such results include the description of black holes (where complete collapse to consensus is not avoidable), safety zones (where the control can keep the system far from clustering), basins of attraction (attractive zones around the clustering set), and collapse prevention (when convergence to the clustering set can be avoided).

MSC:

91D30 Social networks; opinion dynamics
93C15 Control/observation systems governed by ordinary differential equations
93C20 Control/observation systems governed by partial differential equations
93D99 Stability of control systems
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