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Using Artificial Neural Networks for the Analysis of Social-Ecological Systems

Ulrich J. Frey, Center for Philosophy and the Foundations of Science, Justus Liebig University
Hannes Rusch, Center for Philosophy and the Foundations of Science, Justus Liebig University


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The literature on common pool resource (CPR) governance lists numerous factors that influence whether a given CPR system achieves ecological long-term sustainability. Up to now there is no comprehensive model to integrate these factors or to explain success within or across cases and sectors. Difficulties include the absence of large-N studies, the incomparability of single case studies, and the interdependence of factors. We propose (1) a synthesis of 24 success factors based on the current social-ecological systems (SES) framework and a literature review and (2) the application of neural networks on a database of CPR management case studies in an attempt to test the viability of this synthesis.

This method allows us to obtain an implicit quantitative and rather precise model of the interdependencies in CPR systems. Given such a model, every success factor in each case can be manipulated separately, yielding different predictions for success. This could become a fast and inexpensive way to analyze, predict, and optimize performance for communities worldwide facing CPR challenges. Existing theoretical frameworks could be improved as well.

Key words

common pool resource; design principles; natural resource management; neural networks; social-ecological systems framework; success factors

Copyright © 2013 by the author(s). Published here under license by The Resilience Alliance. This article is under a Creative Commons Attribution-NonCommercial 4.0 International License. You may share and adapt the work for noncommercial purposes provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license.

Ecology and Society. ISSN: 1708-3087