%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture non répertoriées par l'AERES
%A Houssou, N.LJ.
%A Cordero, J.D.
%A Bouadjio-Boulic, A.
%A Morin, L.
%A Maestripieri, N.
%A Ferrant, S.
%A Belem, M.
%A Pelaez Sanchez, J.I.
%A Saenz, M.
%A Lerigoleur, E.
%A Elger, A.
%A Gaudou, B.
%A Maurice, Laurence
%A Saqalli, M.
%T Synchronizing histories of exposure and demography : the construction of an agent-based model of the Ecuadorian Amazon colonization and exposure to oil pollution hazards
%D 2019
%L fdi:010075920
%G ENG
%J Journal of Artificial Societies and Social Simulation
%K EQUATEUR ; AMAZONIE
%N 2
%P art. no 1 [23  en ligne]
%R 10.18564/jasss.3957
%U https://www.documentation.ird.fr/hor/fdi:010075920
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers19-04/010075920.pdf
%V 22
%W Horizon (IRD)
%X Since the 1970s, the northern part of the Amazonian region of Ecuador has been colonized with the support of intensive oil extraction that has opened up roads and supported the settlement of people from Outside Amazonia. These dynamics have caused important forest cuttings but also regular oil leaks and spills, contaminating both soil and water. The PASHAMAMA Model seeks to simulate these dynamics on both environment and population by examining exposure and demography over time thanks to a retro-prospective and spatially explicit agent-based approach. The aim of the present paper is to describe this model, which integrates two dynamics: (a) Oil companies build roads and oil infrastructures and generate spills, inducing leaks and pipeline ruptures affecting rivers, soils and people. This infrastructure has a probability of leaks, ruptures and other accidents that produce oil pollution affecting rivers, soils and people. (b) New colonists settled in rural areas mostly as close as possible to roads and producing food and/or cash crops. The innovative aspect of this work is the presentation of a qualitative-quantitative approach explicitly addressed to formalize interdisciplinary modeling when data contexts are almost always incomplete.
%$ 032 ; 020 ; 108