Deterrence Using Peer Information

We propose a mechanism for crime deterrence that utilizes insider information contained in social networks. Regulators may possess limited information regarding a crime but happen to identify a suspect. Under our mechanism, this suspect can re-direct the penalty to another person from the network who is deemed to be more responsible for the crime. The regulator examines the criminal activities of both and obtains two noisy signals regarding their actions. The one with the higher signal is punished and the other goes free. We show theoretically that, for a given probability and magnitude of the penalty, crime levels are lower with this mechanism than in the case where the first suspect is automatically punished. In equilibrium, crime levels depend on the given criminal's position in the network and the network structure. Our experiment confirms that this mechanism effectively deters crime but the magnitude is above the Nash equilibrium predictions and is less sensitive to changes in the network structure than theory predicts. Level-k reasoning helps to explain these patterns.