Linguistic Fuzzy Information Evolution with Random Leader Election Mechanism for Decision-Making Systems
Qianlei Jia, Witold Pedrycz

TL;DR
This paper introduces three novel models for linguistic fuzzy information evolution using a random leader election mechanism, enhancing decision-making robustness and information sharing in agent-based systems.
Contribution
It proposes new models incorporating random leader selection in fuzzy information dynamics, addressing limitations of existing models and improving system robustness.
Findings
Models effectively improve decision-making robustness.
Simulation results validate the models' effectiveness.
Enhanced information sharing reduces echo chamber effects.
Abstract
Linguistic fuzzy information evolution is crucial in understanding information exchange among agents. However, different agent weights may lead to different convergence results in the classic DeGroot model. Similarly, in the Hegselmann-Krause bounded confidence model (HK model), changing the confidence threshold values of agents can lead to differences in the final results. To address these limitations, this paper proposes three new models of linguistic fuzzy information dynamics: the per-round random leader election mechanism-based DeGroot model (PRRLEM-DeGroot), the PRRLEM-based homogeneous HK model (PRRLEM-HOHK), and the PRRLEM-based heterogeneous HK model (PRRLEM-HEHK). In these models, after each round of fuzzy information updates, an agent is randomly selected to act as a temporary leader with more significant influence, with the leadership structure being reset after each update.…
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Taxonomy
TopicsCognitive Computing and Networks · Advanced Text Analysis Techniques
