[en] When communicating agents form opinions about a set of possible options, agreement and disagreement are both possible outcomes. Depending on the context, either can be desirable or undesirable. We show that for nonlinear opinion dynamics on networks, and a variety of network structures, the spectral properties of the underlying adjacency matrix fully characterize the occurrence of either agreement or disagreement. We further show how the corresponding eigenvector centrality, as well as any symmetry in the network, informs the resulting patterns of opinion formation and agent sensitivity to input that triggers opinion cascades.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Bizyaeva, Anastasia; Princeton University, Department of Mechanical and Aerospace Engineering, Princeton, United States
Matthews, Ayanna; Princeton University, Department of Mechanical and Aerospace Engineering, Princeton, United States
Franci, Alessio ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Brain-Inspired Computing ; National Autonomous University of Mexico, Mexico City, Mexico
Leonard, Naomi Ehrich; Princeton University, Department of Mechanical and Aerospace Engineering, Princeton, United States
Language :
English
Title :
Patterns of Nonlinear Opinion Formation on Networks
Publication date :
25 May 2021
Event name :
2021 American Control Conference (ACC)
Event place :
Virtual, New Orleans, Usa
Event date :
25-05-2021 => 28-05-2021
Audience :
International
Main work title :
2021 American Control Conference, ACC 2021
Publisher :
Institute of Electrical and Electronics Engineers Inc.
Halliburton MathWorks Mitsubishi Electric Research Laboratory (MERL) US National Member Organization (NMO) of the International Federation of Automatic Control (IFAC)
Funding text :
Supported by NSF grant CMMI-1635056, ONR grant N00014-19-1-2556, ARO grant W911NF-18-1-0325, DGAPA-UNAM PAPIIT grant IN102420, and Conacyt grant A1-S-10610, and NSF Grad. Res. Fell. DGE-2039656.
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