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Título

Analytical and numerical treatment of continuous ageing in the voter model

AutorBaron, Joseph W. CSIC ORCID; Peralta, Antonio F.; Galla, Tobias CSIC ORCID; Toral, Raúl CSIC ORCID
Fecha de publicación8-ago-2022
EditorarXiv
CitaciónBaron, Joseph W.; Peralta, Antonio F.; Galla, Tobias; Toral, Raúl; 2022; Analytical and numerical treatment of continuous ageing in the voter model [Preprint]; arXiv; https://doi.org/10.48550/arXiv.2208.04264
ResumenThe conventional voter model is modified so that an agent's switching rate depends on the `age' of the agent, that is, the time since the agent last switched opinion. In contrast to previous work, age is continuous in the present model. We show how the resulting individual-based system with non-Markovian dynamics and concentration-dependent rates can be handled both computationally and analytically. Lewis' thinning algorithm can be modified in order to provide an efficient simulation method. Analytically, we demonstrate how the asymptotic approach to an absorbing state (consensus) can be deduced. We discuss three special cases of the age dependent switching rate: one in which the concentration of voters can be approximated by a fractional differential equation, another for which the approach to consensus is exponential in time, and a third case in which the system reaches a frozen state instead of consensus. Finally, we include the effects of spontaneous change of opinion, i.e., we study a noisy voter model with continuous ageing. We demonstrate that this can give rise to a continuous transition between coexistence and consensus phases. We also show how the stationary probability distribution can be approximated, despite the fact that the system cannot be described by a conventional master equation.
Versión del editorhttps://doi.org/10.48550/arXiv.2208.04264
URIhttp://hdl.handle.net/10261/305128
DOI10.48550/arXiv.2208.04264
ReferenciasBaron, Joseph W.; Peralta, Antonio F.; Galla, Tobias; Toral, Raúl. Analytical and numerical treatment of continuous ageing in the voter model. Entropy 24(10): 1331 (2022). https://doi.org/10.3390/e24101331 . http://hdl.handle.net/10261/305129
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