网络威慑

IF 1 3区 经济学 Q3 ECONOMICS
Leo Bao , Lata Gangadharan , C. Matthew Leister
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引用次数: 0

摘要

我们提出了一种利用犯罪分子通过惯例获得的内幕信息的威慑机制。在这一机制下,犯罪嫌疑人被抓获时,可以举荐一名犯有类似罪行的同辈,只有情节较重的罪犯才会受到处罚。理论分析表明,在一般情况下,与通常只惩罚第一个嫌疑人的监管做法相比,我们的机制会推动最佳反应动态向下。实验数据证实了该机制的威慑作用,但揭示了与均衡预测的偏差:威慑作用弱于预期,并且对汇总内幕知识的网络结构不敏感。为了理解这一点,我们分析了实验后的问卷回答,并发现一些参与者采用水平k策略而不是纳什策略的证据。结构估计证实,水平k规范比纳什更适合数据。这些发现使决策者了解了同行知情审计机制的潜在用途和限制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deterrence in networks
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.
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来源期刊
CiteScore
1.90
自引率
9.10%
发文量
148
期刊介绍: Games and Economic Behavior facilitates cross-fertilization between theories and applications of game theoretic reasoning. It consistently attracts the best quality and most creative papers in interdisciplinary studies within the social, biological, and mathematical sciences. Most readers recognize it as the leading journal in game theory. Research Areas Include: • Game theory • Economics • Political science • Biology • Computer science • Mathematics • Psychology
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