Law enforcement in norm-governed learning agents

We study law enforcement mechanisms within a population of norm-governed learning agents. We show that a traditional analysis based on expected utility can be misleading, because learning agents tend to comply even though their surveillance is stopped. This has significant implications for the design of self-organising institutions with endogenous resources, where the cost of monitoring and norm enforcement has to be taken into consideration.