13 research outputs found
External bias in the model of isolation of communities
We extend a model of community isolation in the d-dimensional lattice onto
the case with an imposed imbalance between birth rates of competing
communities. We give analytical and numerical evidences that in the asymmetric
two-specie model there exists a well defined value of the asymmetry parameter
when the emergence of the isolated (blocked) subgroups is the fastest, i.e. the
characteristic time tc is minimal. This critical value of the parameter depends
only on the lattice dimensionality and is independent from the system size.
Similar phenomenon was observed in the multi-specie case with a geometric
distribution of the birth rates. We also show that blocked subgroups in the
multi-specie case are absent or very rare when either there is a strictly
dominant specie that outnumbers the others or when there is a large diversity
of species. The number of blocked species of different kinds decreases with the
dimension of the multi-specie system.Comment: 6 pages, 4 figure
Agent-based model for the h-index – exact solution
Hirsch’s h-index is perhaps the most popular citation-based
measure of scientific excellence. In 2013, Ionescu and Chopard proposed an agent-based
model describing a process for generating publications and citations in an abstract
scientific community [G. Ionescu, B. Chopard, Eur. Phys. J. B 86, 426
(2013)]. Within such a framework, one may simulate a scientist’s activity, and – by
extension – investigate the whole community of researchers. Even though the Ionescu and
Chopard model predicts the h-index quite well, the authors provided a solution
based solely on simulations. In this paper, we complete their results with exact, analytic
formulas. What is more, by considering a simplified version of the Ionescu-Chopard model,
we obtained a compact, easy to compute formula for the h-index. The derived
approximate and exact solutions are investigated on a simulated and real-world data
sets
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Modelling railway delay propagation as diffusion-like spreading
Railway systems form an important means of transport across the world. However, congestions or disruptions may significantly decrease these systems' efficiencies, making predicting and understanding the resulting train delays a priority for railway organisations. Delays are studied in a wide variety of models, which usually simulate trains as discrete agents carrying delays. In contrast, in this paper, we define a novel model for studying delays, where they spread across the railway network via a diffusion-like process. This type of modelling has various advantages such as quick computation and ease of applying various statistical tools like spectral methods, but it also comes with limitations related to the directional and discrete nature of delays and the trains carrying them. We apply the model to the Belgian railways and study its performance in simulating the delay propagation in severely disrupted railway situations. In particular, we discuss the role of spatial aggregation by proposing to cluster the Belgian railway system into sets of stations and adapt the model accordingly. We find that such aggregation significantly increases the model's performance. For some particular situations, a non-trivial optimal level of spatial resolution is found on which the model performs best. Our results show the potential of this type of delay modelling to understand large-scale properties of railway systems