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Modeling Warfare in Social Animals: A "Chemical" Approach

We present here a general method for modelling the dynamics of battles among social animals. The proposed method exploits the procedures widely used to model chemical reactions, but still uncommon in behavioural studies. We applied this methodology to the interpretation of experimental observations...

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Autores principales: Santarlasci, Alisa, Martelloni, Gianluca, Frizzi, Filippo, Santini, Giacomo, Bagnoli, Franco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4219847/
https://www.ncbi.nlm.nih.gov/pubmed/25369269
http://dx.doi.org/10.1371/journal.pone.0111310
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author Santarlasci, Alisa
Martelloni, Gianluca
Frizzi, Filippo
Santini, Giacomo
Bagnoli, Franco
author_facet Santarlasci, Alisa
Martelloni, Gianluca
Frizzi, Filippo
Santini, Giacomo
Bagnoli, Franco
author_sort Santarlasci, Alisa
collection PubMed
description We present here a general method for modelling the dynamics of battles among social animals. The proposed method exploits the procedures widely used to model chemical reactions, but still uncommon in behavioural studies. We applied this methodology to the interpretation of experimental observations of battles between two species of ants (Lasius neglectus and Lasius paralienus), but this scheme may have a wider applicability and can be extended to other species as well. We performed two types of experiment labelled as interaction and mortality. The interaction experiments are designed to obtain information on the combat dynamics and lasted one hour. The mortality ones provide information on the casualty rates of the two species and lasted five hours. We modelled the interactions among ants using a chemical model which considers the single ant individuals and fighting groups analogously to atoms and molecules. The mean-field behaviour of the model is described by a set of non-linear differential equations. We also performed stochastic simulations of the corresponding agent-based model by means of the Gillespie event-driven integration scheme. By fitting the stochastic trajectories with the deterministic model, we obtained the probability distribution of the reaction parameters. The main result that we obtained is a dominance phase diagram, that gives the average trajectory of a generic battle, for an arbitrary number of opponents. This phase diagram was validated with some extra experiments. With respect to other war models (e.g., Lanchester's ones), our chemical model considers all phases of the battle and not only casualties. This allows a more detailed description of the battle (with a larger number of parameters), allowing the development of more sophisticated models (e.g., spatial ones), with the goal of distinguishing collective effects from the strategic ones.
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spelling pubmed-42198472014-11-12 Modeling Warfare in Social Animals: A "Chemical" Approach Santarlasci, Alisa Martelloni, Gianluca Frizzi, Filippo Santini, Giacomo Bagnoli, Franco PLoS One Research Article We present here a general method for modelling the dynamics of battles among social animals. The proposed method exploits the procedures widely used to model chemical reactions, but still uncommon in behavioural studies. We applied this methodology to the interpretation of experimental observations of battles between two species of ants (Lasius neglectus and Lasius paralienus), but this scheme may have a wider applicability and can be extended to other species as well. We performed two types of experiment labelled as interaction and mortality. The interaction experiments are designed to obtain information on the combat dynamics and lasted one hour. The mortality ones provide information on the casualty rates of the two species and lasted five hours. We modelled the interactions among ants using a chemical model which considers the single ant individuals and fighting groups analogously to atoms and molecules. The mean-field behaviour of the model is described by a set of non-linear differential equations. We also performed stochastic simulations of the corresponding agent-based model by means of the Gillespie event-driven integration scheme. By fitting the stochastic trajectories with the deterministic model, we obtained the probability distribution of the reaction parameters. The main result that we obtained is a dominance phase diagram, that gives the average trajectory of a generic battle, for an arbitrary number of opponents. This phase diagram was validated with some extra experiments. With respect to other war models (e.g., Lanchester's ones), our chemical model considers all phases of the battle and not only casualties. This allows a more detailed description of the battle (with a larger number of parameters), allowing the development of more sophisticated models (e.g., spatial ones), with the goal of distinguishing collective effects from the strategic ones. Public Library of Science 2014-11-04 /pmc/articles/PMC4219847/ /pubmed/25369269 http://dx.doi.org/10.1371/journal.pone.0111310 Text en © 2014 Santarlasci et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Santarlasci, Alisa
Martelloni, Gianluca
Frizzi, Filippo
Santini, Giacomo
Bagnoli, Franco
Modeling Warfare in Social Animals: A "Chemical" Approach
title Modeling Warfare in Social Animals: A "Chemical" Approach
title_full Modeling Warfare in Social Animals: A "Chemical" Approach
title_fullStr Modeling Warfare in Social Animals: A "Chemical" Approach
title_full_unstemmed Modeling Warfare in Social Animals: A "Chemical" Approach
title_short Modeling Warfare in Social Animals: A "Chemical" Approach
title_sort modeling warfare in social animals: a "chemical" approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4219847/
https://www.ncbi.nlm.nih.gov/pubmed/25369269
http://dx.doi.org/10.1371/journal.pone.0111310
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