I hope you already had a try with simple experiment mentioned in previous post.
How did it go?
What were your decisions?
Were you maximizing your individual profit or outcome for the whole group?
What about others?
In A Common Dilemma Nash equilibrium is connected with maximizing individual profit, but do not have to mean maximizing player’s decision. In most cases, it is a situation when all participants harvest the same amount and none of them have the incentive to deviate. If they make a lower decision, they get less profit. On the other hand, higher decision means that the costs increase so much, that new profit is not able to cover them. Therefore, all players should stay with their harvest. Still this set of decisions is always higher than social optimum that represents the maximum production of the resource. If all the players make decisions that sums up to this value they earn the most as a community.
Let’s quickly go through the main results that were obtained from experimental research conducted using this and similar design1:
People, generally, do not follow the Nash equilibrium, but in the same are not so good at cooperating to reach the social optimum.
Open communication enables members of a community to create their own rules and norms. These regulations increase probability of using joint CPR management strategies.
Monitoring is an important factor in establishing cooperation. It enables players to check if other people follow the established rules.
Sanctioning further improves cooperation as it is a method of punishing players who do not adhere to the norms formulated.
These are most important findings from the experimental research. Still, they hold up when checked against the reality. There are many communities across the world that are able to sustain their common-pool resources. More information about the topic can be found in the book dedicated to this very subject written by Ostrom, Gardner and Walker.
Still, there are many differences between experimental designs as presented in this post and reality. The complexity of actual ecosystems has to be reduced to create simple models used in research.