Deterrence Using Peer Information

We propose a deterrence mechanism that utilizes insider information contained in criminal networks. Under this mechanism, a suspect, identified by the regulator, can nominate a connected person from the network for the crime, and only the more severe offender is penalized. We show theoretically that the equilibrium crime levels are lower with this mechanism than in the case where the first suspect is automatically punished. Crime levels depend on the network structure. Our experiment confirms the mechanism's deterrence effect, but also uncovers little sensitivity of the mechanism to network structure. Level-k reasoning helps explain these patterns