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On a Closed-Loop Controller for the Coevolutionary Model of Actions and Opinions via Broadcasting Information

  • Roberta Raineri
  • Mengbin Ye
  • Lorenzo Zino
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Abstract

We deal with controlling a complex social network in which agents have actions and opinions that coevolve, mutually influencing one another. We consider an input consisting in broadcasting information to a target set of agents with the objective of steering the population, initially at a consensus, to a different consensus. For a constant input, we derive a monotone convergence result, building on which we design an algorithm that determines whether a target set is sufficient to achieve the objective and an effective heuristic to optimize the target set. Then, we introduce a feedback control law that, using information on the state of the system, dynamically revises the target set, reducing the effort needed to achieve the objective while guaranteeing convergence to the desired consensus state.

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Cite

BibTeXRIS
BibTeX
@misc{raineri2026closedloop,
  title = {{On a Closed-Loop Controller for the Coevolutionary Model of Actions and Opinions via Broadcasting Information}},
  author = {Roberta Raineri and Mengbin Ye and Lorenzo Zino},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.22986},
  eprint = {2609.22986},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.22986},
}
RIS
TY  - UNPB
TI  - On a Closed-Loop Controller for the Coevolutionary Model of Actions and Opinions via Broadcasting Information
AU  - Roberta Raineri
AU  - Mengbin Ye
AU  - Lorenzo Zino
PY  - 2026
DA  - 2026/09/19
PB  - arXiv
DO  - 10.48550/arxiv.2609.22986
UR  - https://arxiv.org/abs/2609.22986
AB  - We deal with controlling a complex social network in which agents have actions and opinions that coevolve, mutually influencing one another. We consider an input consisting in broadcasting information to a target set of agents with the objective of steering the population, initially at a consensus, to a different consensus. For a constant input, we derive a monotone convergence result, building on which we design an algorithm that determines whether a target set is sufficient to achieve the objective and an effective heuristic to optimize the target set. Then, we introduce a feedback control law that, using information on the state of the system, dynamically revises the target set, reducing the effort needed to achieve the objective while guaranteeing convergence to the desired consensus state.
ER  -