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Punishment Deters Crime Because Humans Are Bounded in Their Strategic Decision-Making

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Is it rational to reduce criminal activities if punishments are increased? While intuition might suggest so, game theory concludes differently. From the game theoretical perspective, inspectors anticipate the effect of increased punishments on criminal behavior and reduce their inspection activities accordingly. This implies that higher punishments reduce inspections and do not affect crime rates. We present two laboratory experiments, which challenge this perspective by demonstrating that both, criminals and inspectors, are affected by punishment levels. Thereupon, we investigate with agent-based simulations, whether models of bounded rationality can explain our empirical data. We differentiate between two kinds of bounded rationality; the first considers bounded learning from social interaction, the second bounded decision-making. Our results suggest that humans show both kinds of bounded rationality in the strategic situation of crime, control and punishment. We conclude that it is not the rationality but the bounded rationality in humans that makes punishment effective.

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  • Heiko Rauhut & Marcel Junker, 2009. "Punishment Deters Crime Because Humans Are Bounded in Their Strategic Decision-Making," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(3), pages 1-1.
    • Handle: RePEc:jas:jasssj:2008-82-2
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    Cited by:

    1. Christoph Engel, 2016. "Experimental Criminal Law. A Survey of Contributions from Law, Economics and Criminology," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2016_07, Max Planck Institute for Research on Collective Goods.
    2. Maria Fonoberova & Vladimir A. Fonoberov & Igor Mezic & Jadranka Mezic & P. Jeffrey Brantingham, 2012. "Nonlinear Dynamics of Crime and Violence in Urban Settings," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-2.
    3. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    4. Ammar Malik & Andrew Crooks & Hilton Root & Melanie Swartz, 2015. "Exploring Creativity and Urban Development with Agent-Based Modeling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-12.
    5. Riccardo Boero & Giangiacomo Bravo & Marco Castellani & Francesco Laganà & Flaminio Squazzoni, 2009. "Pillars of Trust: An Experimental Study on Reputation and Its Effects," Sociological Research Online, , vol. 14(5), pages 49-67, November.
    6. Patrick Groeber & Heiko Rauhut, 2010. "Does ignorance promote norm compliance?," Computational and Mathematical Organization Theory, Springer, vol. 16(1), pages 1-28, March.
    7. DeAngelo, Gregory & Gee, Laura Katherine, 2018. "Peers or Police? Detection and Sanctions in the Provision of Public Goods," IZA Discussion Papers 11540, Institute of Labor Economics (IZA).
    8. Susumu Shikano & Michael F Stoffel & Markus Tepe, 2017. "Information accuracy in legislative oversight: Theoretical implications and experimental evidence," Rationality and Society, , vol. 29(2), pages 226-254, May.
    9. Heiko Rauhut, 2015. "Stronger inspection incentives, less crime? Further experimental evidence on inspection games," Rationality and Society, , vol. 27(4), pages 414-454, November.
    10. Christine Horne & Heiko Rauhut, 2013. "Using laboratory experiments to study law and crime," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(3), pages 1639-1655, April.
    11. Christine Horne & Heiko Rauhut, "undated". "Using Laboratory Experiments to Study Law and Crime," Working Papers CCSS-10-010, ETH Zurich, Chair of Systems Design.
    12. Frank M. A. Klingert & Matthias Meyer, 2012. "Effectively combining experimental economics and multi-agent simulation: suggestions for a procedural integration with an example from prediction markets research," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 63-90, March.

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