TY - GEN
T1 - Control of Agreement and Disagreement Cascades with Distributed Inputs
AU - Bizyaeva, Anastasia
AU - Sorochkin, Timothy
AU - Franci, Alessio
AU - Ehrich Leonard, Naomi
N1 - Funding Information:
Supported by ONR grant N00014-19-1-2556, ARO grant W911NF-18-1-0325, DGAPA-UNAM PAPIIT grant IN102420, and Conacyt grant A1-S-10610, and by NSF Graduate Research Fellowship DGE-2039656.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - For a group of autonomous communicating agents to carry out coordinated objectives, it is paramount that they can distinguish meaningful input from disturbance, and come rapidly and reliably to agreement or disagreement in response to that input. We study how opinion formation cascades through a group of networked decision makers in response to a distributed input signal. Using a nonlinear opinion dynamics model with dynamic feedback modulation of an attention parameter, we prove how the triggering of an opinion cascade and the collective decision itself depend on both the distributed input and node agreement and disagreement centrality indices, determined by the spectral properties of the network graph. Moreover, we show how the attention dynamics introduce an implicit threshold that distinguishes between distributed inputs that trigger cascades and ones that are rejected as disturbance.
AB - For a group of autonomous communicating agents to carry out coordinated objectives, it is paramount that they can distinguish meaningful input from disturbance, and come rapidly and reliably to agreement or disagreement in response to that input. We study how opinion formation cascades through a group of networked decision makers in response to a distributed input signal. Using a nonlinear opinion dynamics model with dynamic feedback modulation of an attention parameter, we prove how the triggering of an opinion cascade and the collective decision itself depend on both the distributed input and node agreement and disagreement centrality indices, determined by the spectral properties of the network graph. Moreover, we show how the attention dynamics introduce an implicit threshold that distinguishes between distributed inputs that trigger cascades and ones that are rejected as disturbance.
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U2 - 10.1109/CDC45484.2021.9683650
DO - 10.1109/CDC45484.2021.9683650
M3 - Conference contribution
AN - SCOPUS:85116358464
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 4994
EP - 4999
BT - 60th IEEE Conference on Decision and Control, CDC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 60th IEEE Conference on Decision and Control, CDC 2021
Y2 - 13 December 2021 through 17 December 2021
ER -