2024-03-29T14:33:37Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/63342022-11-30T10:39:30Zcom_10259_6336com_10259_6335com_10259.4_106com_10259_2604com_10259_3830com_10259_5086col_10259_6337col_10259_3832
Let’s go fishing: A quantitative analysis of subsistence choices with a special focus on mixed economies among small-scale societies
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