Abstract
Corruption frequently occurs in many aspects of multi-party interaction between private agencies and government employees. Past works studying corruption in a lab context have explicitly included covert or illegal activities in participants’ strategy space or have relied on surveys like the Corruption Perception Index (CPI). This paper studies corruption in ecologically realistic settings in which corruption is not suggested to the players a priori but evolves during repeated interaction. We ran studies involving hundreds of subjects in three countries: China, Israel, and the United States. Subjects interacted using a four-player board game in which three bidders compete to win contracts by submitting bids in repeated auctions, and a single auctioneer determines the winner of each auction. The winning bid was paid to an external “government” entity, and was not distributed among the players. The game logs were analyzed posthoc for cases in which the auctioneer was bribed to choose a bidder who did not submit the highest bid. We found that although China exhibited the highest corruption level of the three countries, there were surprisingly more cases of corruption in the U.S. than in Israel, despite the higher PCI in Israel as compared to the U.S. We also found that bribes in the U.S. were at times excessively high, resulting in bribing players not being able to complete their winning contracts. We were able to predict the occurrence of corruption in the game using machine learning. The significance of this work is in providing a novel paradigm for investigating covert activities in the lab without priming subjects, and it represents a first step in the design of intelligent agents for detecting and reducing corruption activities in such settings.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
United nations handbook on practical anti-corruption measures for prosecutors and investigators (2012)
Abbink, K., Irlenbusch, B., Renner, E.: An experimental bribery game. Journal of Law, Economics, and Organization 18(2), 428–454 (2002)
Chalamish, M., Sarne, D., Lin, R.: The effectiveness of peer-designed agents in agent-based simulations. Multiagent and Grid Systems 8(4), 349–372 (2012)
Falk, A., Fischbacher, U.: “Crime” in the lab-detecting social interaction. European Economic Review 46(4), 859–869 (2002)
Fehr, E., Gächter, S., Kirchsteiger, G.: Reciprocity as a contract enforcement device: Experimental evidence. Econometrica 65(4), 833–860 (1997)
Fehr, E., Kirchsteiger, G., Riedl, A.: Does fairness prevent market clearing? an experimental investigation. The Quarterly Journal of Economics 108(2), 437–459 (1993)
Gal, Y., Grosz, B., Kraus, S., Pfeffer, A., Shieber, S.: Agent decision-making in open mixed networks. Artificial Intelligence 174(18), 1460–1480 (2010)
Transparency international. Corruption perceptions index (2011)
Treisman, D.: The causes of corruption: a cross-national study. Journal of Public Economics 76(3), 399–457 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Gal, Y.(., Rosenfeld, A., Kraus, S., Gelfand, M., An, B., Lin, J. (2014). A New Paradigm for the Study of Corruption in Different Cultures. In: Kennedy, W.G., Agarwal, N., Yang, S.J. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2014. Lecture Notes in Computer Science, vol 8393. Springer, Cham. https://doi.org/10.1007/978-3-319-05579-4_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-05579-4_32
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05578-7
Online ISBN: 978-3-319-05579-4
eBook Packages: Computer ScienceComputer Science (R0)