A New Paradigm for the Study of Corruption in Different Cultures

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8393)


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.


Olympic Game Board Game Corruption Perception Index Winning Bidder Corrupt Activity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    United nations handbook on practical anti-corruption measures for prosecutors and investigators (2012)Google Scholar
  2. 2.
    Abbink, K., Irlenbusch, B., Renner, E.: An experimental bribery game. Journal of Law, Economics, and Organization 18(2), 428–454 (2002)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    Falk, A., Fischbacher, U.: “Crime” in the lab-detecting social interaction. European Economic Review 46(4), 859–869 (2002)CrossRefGoogle Scholar
  5. 5.
    Fehr, E., Gächter, S., Kirchsteiger, G.: Reciprocity as a contract enforcement device: Experimental evidence. Econometrica 65(4), 833–860 (1997)CrossRefzbMATHGoogle Scholar
  6. 6.
    Fehr, E., Kirchsteiger, G., Riedl, A.: Does fairness prevent market clearing? an experimental investigation. The Quarterly Journal of Economics 108(2), 437–459 (1993)CrossRefGoogle Scholar
  7. 7.
    Gal, Y., Grosz, B., Kraus, S., Pfeffer, A., Shieber, S.: Agent decision-making in open mixed networks. Artificial Intelligence 174(18), 1460–1480 (2010)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Transparency international. Corruption perceptions index (2011)Google Scholar
  9. 9.
    Treisman, D.: The causes of corruption: a cross-national study. Journal of Public Economics 76(3), 399–457 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Information Systems EngineeringBen-Gurion UniversityIsrael
  2. 2.Department of Industrial EngineeringJerusalem College of TechnologyIsrael
  3. 3.Department of Computer ScienceBar-Ilan UniversityIsrael
  4. 4.Institute for Advanced Computer StudiesUniversity of MarylandUSA
  5. 5.Department of EngineeringNanyang Technological UniversitySingapore
  6. 6.Department of Engineering and ManagementBeihang UniversityChina

Personalised recommendations