Harvester-enforcer game: How to suppress illegal logging

Background/Question/Methods

Corruption is one of the most serious obstacles for ecosystem management and biodiversity conservation. More than half of the loss of forested area in many tropical countries is due to illegal logging. Here we study an evolutionary game model to analyze the illegal harvesting of trees in forests, coupled with the corruption of rule enforcers. We consider several types of harvesters, who may or may not be committed towards supporting an enforcer service, and who may cooperate (i.e., invest to maintain the forest) or defect (i.e., clear up the forest illegally). We also consider two types of rule enforcers: honest and corrupt. Corrupt enforcers accept bribes from defecting harvesters and refrain from fining them. 

Results/Conclusions

The system is bistable: There is a line segment of equilibria consisting of defecting harvesters and a low fraction of honest enforcers; and there is also a line segment of equilibria consisting of cooperating harvesters and a high fraction of honest enforcers. Both line segments attract near-by trajectories. Even a small rate of exploration (or ‘mutation’) among strategies can produce a totally different outcome: the system is globally stable. Depending on the relative rates of exploration among enforcers, most harvesters cooperate, or most all defect. This suggests that education of enforcers can be very important. Furthermore, if information on the honesty of enforcers is available, and players react opportunistically, then the domain of attraction for cooperative outcomes becomes considerably larger. We discuss policy implications of these results.