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arXiv · 1404.0856

Continuous transition from the extensive to the non-extensive statistics in an agent-based herding model

Aleksejus Kononovicius·Julius Ruseckas
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Abstract

Systems with long-range interactions often exhibit power-law distributions and can by described by the non-extensive statistical mechanics framework proposed by Tsallis. In this contribution we consider a simple model reproducing continuous transition from the extensive to the non-extensive statistics. The considered model is composed of agents interacting among themselves on a certain network topology. To generate the underlying network we propose a new network formation algorithm, in which the mean degree scales sub-linearly with a number of nodes in the network (the scaling depends on a single parameter). By changing this parameter we are able to continuously transition from short-range to long-range interactions in the agent-based model.

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Aleksejus Kononovicius, Julius Ruseckas. 2014-09-06. Continuous transition from the extensive to the non-extensive statistics in an agent-based herding model. https://doi.org/10.1140/epjb%2Fe2014-50349-0

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