From equality to diversity -- bottom-up approach for hierarchy growth
July 04, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Agnieszka Czaplicka, Janusz A. HoΕyst
arXiv ID
1707.00985
Category
physics.soc-ph
Cross-listed
cs.SI
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The hierarchical topology is a common property of many complex systems. Here we introduce a simple but generic model of hierarchy growth from the bottom to the top. Therein, two dynamical processes are accounted for: agent's promotions to next hierarchy levels when local speakers are elected and followed by other agents and agent's degradations to the lowest hierarchy. Following the initial stage when all agents are at the bottom level in the course of time the system approaches a stationary state where new hierarchies no longer emerge and the distribution of agents at different levels is exponential. In the stationary state the average hierarchy level and the fraction of agents at the lowest level are independent from the system size however the height of hierarchy, i.e. maximal number of observed hierarchy levels grows logarithmically along the total number of agents. The average number of followers of an agent in the stationary state is much smaller than the number of followers he possessed at the promotion moment. Results from numerical simulations are confirmed by an analytical treatment based on the rate equation.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β physics.soc-ph
π
π
The Cartographer
R.I.P.
π»
Ghosted
Networks beyond pairwise interactions: structure and dynamics
R.I.P.
π»
Ghosted
Statistical physics of human cooperation
R.I.P.
π»
Ghosted
Vital nodes identification in complex networks
R.I.P.
π»
Ghosted
Influence maximization in complex networks through optimal percolation
R.I.P.
π»
Ghosted
Scale-free networks are rare
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted