Collective Memory, Consensus, and Learning explained by Network Connectivity
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| Publication date | 24-11-2023 |
| Edition | v2 |
| Number of pages | 12 |
| Publisher | ArXiv |
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| Abstract | Humans cluster in social groups where they discuss their shared past, problems, and potential solutions, learn collectively when they repeat activities, synchronize when they sing or dance together, and bond through social cohesion. By representing a group network by a Laplacian matrix, the outcomes of these activities, as well as group's cohesion, can be predicted by its second smallest eigenvalue, called algebraic connectivity. It predicts well when processes converge towards a consensus or focal activity, but it cannot predict divergence, such as division of labor or polarization. |
| Document type | Preprint |
| Note | Version v1 (24 Nov 2023) also available at ArXiv, with title: Collective Memory, Consensus, and Learning explained by Network Connectivity. |
| Language | English |
| Published at | https://doi.org/10.48550/arXiv.2311.14386 |
| Downloads |
2311.14386v2
(Final published version)
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