A model-independent theory of consensus and dissensus decision making

Franci, Alessio; Golubitsky, Martin; Bizyaeva, Anastasia; Leonard, Naomi Ehrich

doi:10.48550/arxiv.1909.05765

Cited by 7 publications

(27 citation statements)

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“…We study a model of N a agents communicating over a network and forming opinions on two options through a nonlinear process specialized from the multi-option general model in [6], [7]. As in [8], we specialize to agents that are homogeneous with respect to three fixed parameters in the dynamics: the rate of forgetting (damping coefficient d > 0), the edge weight in the communication network (γ ∈ R), and the strength of self-reinforcement of opinion (α ≥ 0).…”

Section: Opinion Dynamics Modelmentioning

confidence: 99%

“…With this as motivation, we focus in this paper on a general model of distributed opinion formation on a network recently introduced in [6], [7]. In this multi-agent, multi-option framework, opinion formation is treated as a nonlinear process in which agents update their real-valued opinions in continuous time in response to saturated information exchanges from their communication network.…”

Section: Introductionmentioning

confidence: 99%

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Control of Agreement and Disagreement Cascades with Distributed Inputs

Bizyaeva¹,

Sorochkin²,

Franci³

et al. 2021

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For a group of autonomous communicating agents, the ability to distinguish a meaningful input from disturbance, and come to collective agreement or disagreement in response to that input, is paramount for carrying out coordinated objectives. In this work we study how a cascade of opinion formation spreads 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 show how the triggering of an opinion cascade and the collective decision itself depend on both the distributed input and the node agreement and disagreement centrality, determined by the spectral properties of the network graph. We further 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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Section: Opinion Dynamics Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Control of Agreement and Disagreement Cascades with Distributed Inputs

Bizyaeva¹,

Sorochkin²,

Franci³

et al. 2021

Preprint

Self Cite

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show abstract

“…We study a nonlinear model of N a agents forming opinions about two options, which is an instance of the general multioption model introduced in [1], [2]. The opinion of each agent i is captured by a real-valued variable x i ∈ R. When x i = 0 agent i has a neutral opinion, and when x i > 0 (< 0) agent i favors option A (option B).…”

Section: Opinion Dynamics Modelmentioning

confidence: 99%

“…In [1, Section VI] a state feedback law is introduced for the attention parameter u i in (2), which enables the group to become opinionated in response to external input b i . Each agent's attention parameter u i tracks a saturated norm of the system state observed by agent i, with dynamics defined by…”

Section: Agent Sensitivity and Opinion Cascadesmentioning

confidence: 99%

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Patterns of Nonlinear Opinion Formation on Networks

Bizyaeva¹,

Matthews²,

Franci³

et al. 2020

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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.

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Analysis and control of agreement and disagreement opinion cascades

et al. 2021

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We introduce and analyze a continuous time and state-space model of opinion cascades on networks of large numbers of agents that form opinions about two or more options. By leveraging our recent results on the emergence of agreement and disagreement states, we introduce novel tools to analyze and control agreement and disagreement opinion cascades. New notions of agreement and disagreement centrality, which depend only on network structure, are shown to be key to characterizing the nonlinear behavior of agreement and disagreement opinion formation and cascades. Our results are relevant for the analysis and control of opinion cascades in real-world networks, including biological, social, and artificial networks, and for the design of opinion-forming behaviors in robotic swarms. We illustrate an application of our model to a multi-robot task-allocation problem and discuss extensions and future directions opened by our modeling framework.

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A model-independent theory of consensus and dissensus decision making

Cited by 7 publications

References 0 publications

Control of Agreement and Disagreement Cascades with Distributed Inputs

Control of Agreement and Disagreement Cascades with Distributed Inputs

Patterns of Nonlinear Opinion Formation on Networks

Analysis and control of agreement and disagreement opinion cascades

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