Glassy nature of hierarchical organizations
The question of why and how animal and human groups form temporarily stable hierarchical organizations has long been a great challenge from the point of quantitative interpretations. The prevailing observation/consensus is that a hierarchical social or technological structure is optimal considering...
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2017
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edit-10831-667242022-08-23T08:56:23Z Glassy nature of hierarchical organizations Zamani, M Vicsek, T The question of why and how animal and human groups form temporarily stable hierarchical organizations has long been a great challenge from the point of quantitative interpretations. The prevailing observation/consensus is that a hierarchical social or technological structure is optimal considering a variety of aspects. Here we introduce a simple quantitative interpretation of this situation using a statistical mechanics-type approach. We look for the optimum of the efficiency function E-eff = 1/N Sigma(ij)J(ij)a(i)a(j) with J(ij) denoting the nature of the interaction between the units i and j and a(i) standing for the ability of member i to contribute to the efficiency of the system. Notably, this expression for Eeff has a similar structure to that of the energy as defined for spin-glasses. Unconventionally, we assume that J(ij)-s can have the values 0 (no interaction), +1 and -1; furthermore, a direction is associated with each edge. The essential and novel feature of our approach is that instead of optimizing the state of the nodes of a pre-defined network, we search for extrema for given a(i)-s in the complex efficiency landscape by finding locally optimal network topologies for a given number of edges of the subgraphs considered. 2017 info:eu-repo/semantics/article http://hdl.handle.net/10831/66724 doi:10.1038/s41598-017-01503-y elte:000400490600007 elte:85018768871 elte:3238806 elte:SCI REP elte:SCIENTIFIC REPORTS elte:7 elte:28469242 elte:10082783 info:eu-repo/semantics/openAccess application/pdf LOMS: https://edit.elte.hu/xmlui/bitstream/10831/66724/1/3238806.pdf |
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The question of why and how animal and human groups form temporarily stable hierarchical organizations has long been a great challenge from the point of quantitative interpretations. The prevailing observation/consensus is that a hierarchical social or technological structure is optimal considering a variety of aspects. Here we introduce a simple quantitative interpretation of this situation using a statistical mechanics-type approach. We look for the optimum of the efficiency function E-eff = 1/N Sigma(ij)J(ij)a(i)a(j) with J(ij) denoting the nature of the interaction between the units i and j and a(i) standing for the ability of member i to contribute to the efficiency of the system. Notably, this expression for Eeff has a similar structure to that of the energy as defined for spin-glasses. Unconventionally, we assume that J(ij)-s can have the values 0 (no interaction), +1 and -1; furthermore, a direction is associated with each edge. The essential and novel feature of our approach is that instead of optimizing the state of the nodes of a pre-defined network, we search for extrema for given a(i)-s in the complex efficiency landscape by finding locally optimal network topologies for a given number of edges of the subgraphs considered. |
| format |
Journal Article |
| physical |
application/pdf |
| author |
Zamani, M |
| spellingShingle |
Zamani, M Glassy nature of hierarchical organizations |
| author_facet |
Zamani, M Vicsek, T |
| author2 |
Vicsek, T |
| title |
Glassy nature of hierarchical organizations |
| title_short |
Glassy nature of hierarchical organizations |
| title_full |
Glassy nature of hierarchical organizations |
| title_fullStr |
Glassy nature of hierarchical organizations |
| title_full_unstemmed |
Glassy nature of hierarchical organizations |
| title_sort |
glassy nature of hierarchical organizations |
| publishDate |
2017 |
| publishDateSort |
2017 |
| url |
http://hdl.handle.net/10831/66724 |
| first_indexed |
2022-08-23T08:56:23Z |
| last_indexed |
2023-09-01T11:04:43Z |
| _version_ |
1775825409679556608 |
| score |
13,3564 |