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Hierarchical Self-Organization of Non-Cooperating Individuals
Hierarchy is one of the most conspicuous features of numerous natural, technological and social systems. The underlying structures are typically complex and their most relevant organizational principle is the ordering of the ties among the units they are made of according to a network displaying hie...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3859486/ https://www.ncbi.nlm.nih.gov/pubmed/24349070 http://dx.doi.org/10.1371/journal.pone.0081449 |
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author | Nepusz, Tamás Vicsek, Tamás |
author_facet | Nepusz, Tamás Vicsek, Tamás |
author_sort | Nepusz, Tamás |
collection | PubMed |
description | Hierarchy is one of the most conspicuous features of numerous natural, technological and social systems. The underlying structures are typically complex and their most relevant organizational principle is the ordering of the ties among the units they are made of according to a network displaying hierarchical features. In spite of the abundant presence of hierarchy no quantitative theoretical interpretation of the origins of a multi-level, knowledge-based social network exists. Here we introduce an approach which is capable of reproducing the emergence of a multi-levelled network structure based on the plausible assumption that the individuals (representing the nodes of the network) can make the right estimate about the state of their changing environment to a varying degree. Our model accounts for a fundamental feature of knowledge-based organizations: the less capable individuals tend to follow those who are better at solving the problems they all face. We find that relatively simple rules lead to hierarchical self-organization and the specific structures we obtain possess the two, perhaps most important features of complex systems: a simultaneous presence of adaptability and stability. In addition, the performance (success score) of the emerging networks is significantly higher than the average expected score of the individuals without letting them copy the decisions of the others. The results of our calculations are in agreement with a related experiment and can be useful from the point of designing the optimal conditions for constructing a given complex social structure as well as understanding the hierarchical organization of such biological structures of major importance as the regulatory pathways or the dynamics of neural networks. |
format | Online Article Text |
id | pubmed-3859486 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38594862013-12-13 Hierarchical Self-Organization of Non-Cooperating Individuals Nepusz, Tamás Vicsek, Tamás PLoS One Research Article Hierarchy is one of the most conspicuous features of numerous natural, technological and social systems. The underlying structures are typically complex and their most relevant organizational principle is the ordering of the ties among the units they are made of according to a network displaying hierarchical features. In spite of the abundant presence of hierarchy no quantitative theoretical interpretation of the origins of a multi-level, knowledge-based social network exists. Here we introduce an approach which is capable of reproducing the emergence of a multi-levelled network structure based on the plausible assumption that the individuals (representing the nodes of the network) can make the right estimate about the state of their changing environment to a varying degree. Our model accounts for a fundamental feature of knowledge-based organizations: the less capable individuals tend to follow those who are better at solving the problems they all face. We find that relatively simple rules lead to hierarchical self-organization and the specific structures we obtain possess the two, perhaps most important features of complex systems: a simultaneous presence of adaptability and stability. In addition, the performance (success score) of the emerging networks is significantly higher than the average expected score of the individuals without letting them copy the decisions of the others. The results of our calculations are in agreement with a related experiment and can be useful from the point of designing the optimal conditions for constructing a given complex social structure as well as understanding the hierarchical organization of such biological structures of major importance as the regulatory pathways or the dynamics of neural networks. Public Library of Science 2013-12-11 /pmc/articles/PMC3859486/ /pubmed/24349070 http://dx.doi.org/10.1371/journal.pone.0081449 Text en © 2013 Nepusz, Vicsek http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Nepusz, Tamás Vicsek, Tamás Hierarchical Self-Organization of Non-Cooperating Individuals |
title | Hierarchical Self-Organization of Non-Cooperating Individuals |
title_full | Hierarchical Self-Organization of Non-Cooperating Individuals |
title_fullStr | Hierarchical Self-Organization of Non-Cooperating Individuals |
title_full_unstemmed | Hierarchical Self-Organization of Non-Cooperating Individuals |
title_short | Hierarchical Self-Organization of Non-Cooperating Individuals |
title_sort | hierarchical self-organization of non-cooperating individuals |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3859486/ https://www.ncbi.nlm.nih.gov/pubmed/24349070 http://dx.doi.org/10.1371/journal.pone.0081449 |
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