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

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.