Harvester-Enforcer Game of Corruption: How to Suppress Illegal Logging
Corruption is one of the most serious obstacles for ecosystem management and biodiversity conservation, as demonstrated by that more than half of the loss of forested area in many tropical countries were done illegally. Here we study an evolutionary game model to analyze the illegal harvesting of trees in forests coupled with corruption of rule enforcers. We consider several types of harvesters who may or may not be committed to supporting enforcer service and who may cooperate (invest to maintain the forest) or defect (clear up the forest illegally). We also consider two types of rule enforcers: honest and corrupt. Corrupt enforcer receives bribery from defecting harvesters and refrains from charging fine to them. The system is bistable --- there is a defective segment of equilibria (DSE) composed of defecting harvesters and a low fraction of honest enforcers with the forest lost, and there is also a cooperative segment of equilibria (CSE) composed of cooperative harvesters and a high fraction of honest enforcers. Both attract trajectories starting from their neighborhood. When mutation among strategies is considered, the bias of mutation on enforcers plays a strong effect on the outcome, suggesting the importance of education to enforcers. We next consider the availability of information on the honesty of enforcers. If the information is free of charge, it enlarges the domain of attraction for cooperative outcomes. If the information is costly, there may appear a long term cycle, in which the system maintains cooperation for a long time, but occasionally suffers dominance of defecting harvesters and corrupt enforcers, returning back to the quasi-stable cooperative state. We discuss policy implications of these results.
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