4.5 Article

Lessons from a comprehensive validation of an agent based-model: The experience of the Pampas Model of Argentinean agricultural systems

Journal

ECOLOGICAL MODELLING
Volume 273, Issue -, Pages 284-298

Publisher

ELSEVIER
DOI: 10.1016/j.ecolmodel.2013.11.024

Keywords

Agent-based model; Validation; Agriculture; Land use; Land tenure

Categories

Funding

  1. U.S. National Science Foundation (NSF) [0709681, 1211613]
  2. Inter-American Institute for Global Change Research (IAI) [CRN-2031]
  3. NSF [GEO-0452325]
  4. Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) of Argentina
  5. University of Chicago [W-31-109-Eng-38]
  6. Direct For Social, Behav & Economic Scie
  7. Divn Of Social and Economic Sciences [0951516] Funding Source: National Science Foundation
  8. Directorate For Geosciences [1211613, 1138881] Funding Source: National Science Foundation
  9. Div Atmospheric & Geospace Sciences
  10. Directorate For Geosciences [1049109] Funding Source: National Science Foundation
  11. ICER
  12. Directorate For Geosciences [1128040] Funding Source: National Science Foundation

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There are few published examples of comprehensively validated large-scale land-use agent-based models (ABMs). We present guidelines for doing so, and provide an example in the context of the Pampas Model (PM), an ABM aimed to explore the dynamics of structural and land use changes in the agricultural systems of the Argentine Pampas. Many complementary strategies are proposed for validation of ABM's. We adopted a validation framework that relies on two main streams: (a) validation of model processes and components during model development, which involved a literature survey, design based on similar models, involvement of stakeholders, and focused test scenarios and (b) empirical validation, which involved comparisons of model outputs from multiple realistic simulations against real world data. The design process ensured a realistic model ontology and representative behavioral rules. As result, we obtained reasonable outcomes from a set of initial and simplified scenarios: the PM successfully reproduced the direction of the primary observed structural and land tenure patterns, even before calibration. The empirical validation process lead to tuning and further development of the PM. After this, the PM was able to reproduce not only the direction but also the magnitude of the observed changes. The main lesson from our validation process is the need for multiple validation strategies, including empirical validation. Approaches intended to validate model processes and components may lead to structurally realistic models. However, some kind of subsequent empirical validation is needed to assess the model's ability to reproduce observed results. (C) 2013 Elsevier B.V. All rights reserved.

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