Phase transition of the 3-majority opinion dynamics with noisy interactions

Communication noise is a common feature in several real-world scenarios where systems of agents need to communicate in order to pursue some collective task. Indeed, many biologically inspired systems that try to achieve agreements on some opinion must implement resilient dynamics, i.e. that are not strongly affected by noisy communications. In this work, we study the 3-Majority dynamics, an opinion dynamics that has been shown to be an efficient protocol for the majority consensus problem, in which we introduce a simple feature of uniform communication noise, following D'Amore et al. (2022). We prove that, in the fully connected communication network of n agents and in the binary opinion case, the process induced by the 3-Majority dynamics exhibits a phase transition. For a noise probability p < 1 / 3 , the dynamics reach in logarithmic time an almost-consensus metastable phase which lasts for a polynomial number of rounds with high probability. We characterize this phase by showing that there exists an attractive equilibrium value s eq ∈ [ n ] for the bias of the system, i.e. the difference between the majority community size and the minority one. Moreover, we show that the agreement opinion is the initial majority one if the bias towards it is of magnitude Ω ( n log ⁡ n ) in the initial configuration. If, instead, p > 1 / 3 , we show that no form of consensus is possible, and any information regarding the initial majority opinion is lost in logarithmic time with high probability. Despite more communications per-round being allowed, the 3-Majority dynamics surprisingly turns out to be less resilient to noise than the Undecided-State dynamics, whose noise threshold value is p = 1 / 2 .

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Publication Details

Journal
Theoretical Computer Science
Published
2024-12-11
DOI
https://doi.org/10.1016/j.tcs.2024.115030
Citations
1
Primary Topic
Opinion Dynamics and Social Influence
Type
article
Field-Weighted Citation Impact
0.28

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article

Phase transition of the 3-majority opinion dynamics with noisy interactions

Francesco d’Amore, Isabella Ziccardi
1 citations
Theoretical Computer Science
Opinion Dynamics and Social Influence
0.28
article

Phase transition of the 3-majority opinion dynamics with noisy interactions

Francesco d’Amore, Isabella Ziccardi
article en
1 citations

Abstract

Communication noise is a common feature in several real-world scenarios where systems of agents need to communicate in order to pursue some collective task. Indeed, many biologically inspired systems that try to achieve agreements on some opinion must implement resilient dynamics, i.e. that are not strongly affected by noisy communications. In this work, we study the 3-Majority dynamics, an opinion dynamics that has been shown to be an efficient protocol for the majority consensus problem, in which we introduce a simple feature of uniform communication noise, following D'Amore et al. (2022). We prove that, in the fully connected communication network of n agents and in the binary opinion case, the process induced by the 3-Majority dynamics exhibits a phase transition. For a noise probability p < 1 / 3 , the dynamics reach in logarithmic time an almost-consensus metastable phase which lasts for a polynomial number of rounds with high probability. We characterize this phase by showing that there exists an attractive equilibrium value s eq ∈ [ n ] for the bias of the system, i.e. the difference between the majority community size and the minority one. Moreover, we show that the agreement opinion is the initial majority one if the bias towards it is of magnitude Ω ( n log ⁡ n ) in the initial configuration. If, instead, p > 1 / 3 , we show that no form of consensus is possible, and any information regarding the initial majority opinion is lost in logarithmic time with high probability. Despite more communications per-round being allowed, the 3-Majority dynamics surprisingly turns out to be less resilient to noise than the Undecided-State dynamics, whose noise threshold value is p = 1 / 2 .

Theoretical Computer ScienceVol. 1028
Bocconi University (IT)
H2020 European Research Council
Openalex Percentile: Top 42%
Opinion Dynamics and Social Influence
0.28
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