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Evolutionary robotics simulations help explain why reciprocity is rare in nature

The relative rarity of reciprocity in nature, contrary to theoretical predictions that it should be widespread, is currently one of the major puzzles in social evolution theory. Here we use evolutionary robotics to solve this puzzle. We show that models based on game theory are misleading because th...

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Detalles Bibliográficos
Autores principales: André, Jean-Baptiste, Nolfi, Stefano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5018820/
https://www.ncbi.nlm.nih.gov/pubmed/27616139
http://dx.doi.org/10.1038/srep32785
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author André, Jean-Baptiste
Nolfi, Stefano
author_facet André, Jean-Baptiste
Nolfi, Stefano
author_sort André, Jean-Baptiste
collection PubMed
description The relative rarity of reciprocity in nature, contrary to theoretical predictions that it should be widespread, is currently one of the major puzzles in social evolution theory. Here we use evolutionary robotics to solve this puzzle. We show that models based on game theory are misleading because they neglect the mechanics of behavior. In a series of experiments with simulated robots controlled by artificial neural networks, we find that reciprocity does not evolve, and show that this results from a general constraint that likely also prevents it from evolving in the wild. Reciprocity can evolve if it requires very few mutations, as is usually assumed in evolutionary game theoretic models, but not if, more realistically, it requires the accumulation of many adaptive mutations.
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spelling pubmed-50188202016-09-19 Evolutionary robotics simulations help explain why reciprocity is rare in nature André, Jean-Baptiste Nolfi, Stefano Sci Rep Article The relative rarity of reciprocity in nature, contrary to theoretical predictions that it should be widespread, is currently one of the major puzzles in social evolution theory. Here we use evolutionary robotics to solve this puzzle. We show that models based on game theory are misleading because they neglect the mechanics of behavior. In a series of experiments with simulated robots controlled by artificial neural networks, we find that reciprocity does not evolve, and show that this results from a general constraint that likely also prevents it from evolving in the wild. Reciprocity can evolve if it requires very few mutations, as is usually assumed in evolutionary game theoretic models, but not if, more realistically, it requires the accumulation of many adaptive mutations. Nature Publishing Group 2016-09-12 /pmc/articles/PMC5018820/ /pubmed/27616139 http://dx.doi.org/10.1038/srep32785 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
André, Jean-Baptiste
Nolfi, Stefano
Evolutionary robotics simulations help explain why reciprocity is rare in nature
title Evolutionary robotics simulations help explain why reciprocity is rare in nature
title_full Evolutionary robotics simulations help explain why reciprocity is rare in nature
title_fullStr Evolutionary robotics simulations help explain why reciprocity is rare in nature
title_full_unstemmed Evolutionary robotics simulations help explain why reciprocity is rare in nature
title_short Evolutionary robotics simulations help explain why reciprocity is rare in nature
title_sort evolutionary robotics simulations help explain why reciprocity is rare in nature
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5018820/
https://www.ncbi.nlm.nih.gov/pubmed/27616139
http://dx.doi.org/10.1038/srep32785
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