 Deterrence in networksLeo Bao, Lata Gangadharan and C. Matthew Leister Games and Economic Behavior, 2025, vol. 150, issue C, 501-517 Abstract: We propose a deterrence mechanism that utilizes insider information acquired by criminals through customary practices. Under this mechanism, a suspect caught committing a criminal act can nominate a peer who has committed a similar offense, with only the more severe offender facing penalties. Theoretical analyses indicate that, under general conditions, our mechanism drives the best-response dynamic downwards compared to the commonly used regulatory practice of penalizing only the first suspect. Experimental data confirms the mechanism's deterrence effect, but unveils deviations from equilibrium predictions: the deterrence effect is weaker than anticipated and insensitive to network structures summarizing insider knowledge. To understand this, we analyze post-experiment questionnaire responses and find evidence that some participants employ level-k rather than Nash strategies. Structural estimation confirms that the level-k specification better fits the data than Nash. These findings inform policymakers of the potential usefulness and constraints of the peer-informed audit mechanism. Keywords: Deterrence; Network; Experiment; Level-k (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:150:y:2025:i:c:p:501-517 DOI: 10.1016/j.geb.2025.02.001 Access Statistics for this article Games and Economic Behavior is currently edited by E. Kalai More articles in Games and Economic Behavior from Elsevier |
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