2026-04-20T22:50:11Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/63342024-05-10T11:49:56Zcom_10259_6336com_10259_6335com_10259.4_106com_10259_2604com_10259_3830com_10259_5086col_10259_6337col_10259_3832

Ahedo García, Virginia Zurro, Débora Caro Saiz, Jorge Galán Ordax, José Manuel The transition to agriculture is regarded as a major turning point in human history. In the present contribution we propose to look at it through the lens of ethnographic data by means of a machine learning approach. More specifically, we analyse both the subsistence economies and the socioecological context of 1290 societies documented in the Ethnographic Atlas with a threefold purpose: (i) to better understand the variability and success of human economic choices; (ii) to assess the role of environmental settings in the configuration of the different subsistence economies; and (iii) to examine the relevance of fishing in the development of viable alternatives to cultivation. All data were extracted from the publicly available cross-cultural database D-PLACE. Our results suggest that not all subsistence combinations are viable, existing just a subset of successful economic choices that appear recurrently in specific ecological systems. The subsistence economies identified are classified as either primary or mixed economies in accordance with an information-entropy-based quantitative criterion that determines their degree of diversification. Remarkably, according to our results, mixed economies are not a marginal choice, as they constitute 25% of the cases in our data sample. In addition, fishing seems to be a key element in the configuration of mixed economies, as it is present across all of them. 2022-01-18 2022-01-18 2021-08 info:eu-repo/semantics/article 1932-6203 http://hdl.handle.net/10259/6334 10.1371/journal.pone.0254539 eng PLoS ONE. 2021, V. 16, n. 8, e0254539 https://doi.org/10.1371/journal.pone.0254539 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/HAR2017-90883-REDC/ES/Simular el pasado para entender el comportamiento humano info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RED2018-102518-T/ES/SISTEMAS COMPLEJOS SOCIOTECNOLOGICOS info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/HAR2016-77672-P/ES/MODELADO DEL CULTIVO EN LA PREHISTORIA info:eu-repo/grantAgreement/AGAUR//2017 SGR 212/ http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Public Library Science