Factors of success in common pool resources

This project investigates human willingness to cooperate on the basis of so-called “common-goods-dilemmas”. These often lead to a “tragedy of the commons”, when egoistical exploitation of a shared resource (e.g. over-fishing) seems more profitable for the individual than cooperation. This overexploitation can lead to reduced efficacy and damage or even collapse of potentially cooperative collectives.

The overwhelming significance of such common-goods-dilemmas becomes apparent once it is realized that any sustainable use of natural resources confronts us with such problems: be it collective use of water, woods or fish; or even international climate protection. Our project seeks to answer the question which institutional factors (e.g. graduated sanctions) contribute significantly to the persistent sustainable use of common goods and how these factors interact. Most empirical studies are limited to few or even one cooperation community which in turn is only investigated in terms of very few parameters. Therefore, the complexity of the subject makes it impossible to ascertain comparability, generalization or interdependence of factors. Through the use of a new methodology we hope to overcome these limitations.

Previous studies have isolated around 30 influencing factors relevant to success, e.g.: well defined boundaries for resources and user group, locally devised rules of usage, rights of codetermination, supervision, tiered sanctions for breach of rules, mechanisms for conflict-management and more.

The content of the few greater data bases available for common goods problems will be fed into neural networks – programs excellently suited to search large amounts of data for patterns that would otherwise remain unnoticed. This will enable us to determine and model precisely and quantitatively the importance of factors as well as the success or failure of common goods projects.

After the so-called “training” of the neural network is complete, it will be confronted with unfamiliar data. If the trained neural net is able to produce accurate predictions of success or failure in these cases, it should be able to calculate valid forecasts for projects with unknown outcomes as well.


Globally, a high percentage of fishery, forest cultivation and irrigation systems is used collectively – depending on country and resource the numbers vary from 30 to 100%. Thus the potential world-wide benefit of this project can hardly be overstated: especially considering the failure of many collaborative communities, it becomes clear that a practice-oriented elucidation of relevant factors is of the highest interest.

Our model could be the first to map the inherent complexity of common goods problems – thus enabling current and future commons projects to be planned more efficiently or improved or even saved. A complete analysis need not take longer than a few days (excluding independent data acquisition).

Ultimately it is our main goal to improve the situation of those people on site, who suffer from problems caused by poorly organized institutions.