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Behavioral variation in Tullock contests

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  • Masiliunas, Aidas
  • Mengel, Friederike
  • Reiss, J. Philipp

Abstract

We conduct an experiment to uncover the reasons behind the typically large behavioral variation and low explanatory power of Nash equilibrium observed in Tullock contests. In our standard contest treatment, only 7% of choices are consistent with Nash equilibrium which is in line with the literature and roughly what random (uniform) choice would predict (6.25%). We consider a large class of social, risk and some other non-standard preferences and show that heterogeneity in preferences cannot explain these results. We then systematically vary the complexity of both components of Nash behaviour: (I) the difficulty to form correct beliefs and (II) the difficulty to formulate best responses. In treatments where both the difficulty of forming correct beliefs and of formulating best responses is reduced behavioural variation decreases substantially and the explanatory behaviour of Nash equilibrium increases dramatically (explaining 65% of choices with a further 20% being close to NE). Our results show that bounded rationality rather than heterogeneity in preferences is the reason behind the huge behavioral variation typically observed in Tullock contests.

Suggested Citation

  • Masiliunas, Aidas & Mengel, Friederike & Reiss, J. Philipp, 2014. "Behavioral variation in Tullock contests," Working Paper Series in Economics 55, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
  • Handle: RePEc:zbw:kitwps:55
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    Cited by:

    1. Chen, Zhuoqiong (Charlie) & Ong, David & Sheremeta, Roman M., 2015. "The gender difference in the value of winning," Economics Letters, Elsevier, vol. 137(C), pages 226-229.
    2. Masiliūnas, Aidas & Nax, Heinrich H., 2020. "Framing and repeated competition," Games and Economic Behavior, Elsevier, vol. 124(C), pages 604-619.
    3. Emmanuel Dechenaux & Dan Kovenock & Roman Sheremeta, 2015. "A survey of experimental research on contests, all-pay auctions and tournaments," Experimental Economics, Springer;Economic Science Association, vol. 18(4), pages 609-669, December.
    4. Subhasish M. Chowdhury & Anwesha Mukherjee & Theodore L. Turocy, 2020. "That’s the ticket: explicit lottery randomisation and learning in Tullock contests," Theory and Decision, Springer, vol. 88(3), pages 405-429, April.
    5. Bettina Rockenbach & Sebastian Schneiders & Marcin Waligora, 2018. "Pushing the bad away: reverse Tullock contests," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 4(1), pages 73-85, July.
    6. Oliver Kirchkamp & J. Philipp Reiß, 2019. "Heterogeneous bids in auctions with rational and boundedly rational bidders: theory and experiment," International Journal of Game Theory, Springer;Game Theory Society, vol. 48(4), pages 1001-1031, December.
    7. Cason, Timothy N. & Masters, William A. & Sheremeta, Roman M., 2020. "Winner-take-all and proportional-prize contests: Theory and experimental results," Journal of Economic Behavior & Organization, Elsevier, vol. 175(C), pages 314-327.
    8. Alcocer, Christian Diego & Jeitschko, Thomas D. & Shupp, Robert, 2020. "Naive and sophisticated mixing: Experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 170(C), pages 157-173.
    9. Kenan Huremovic, 2015. "A Noncooperative Model of Contest Network Formation," AMSE Working Papers 1521, Aix-Marseille School of Economics, France, revised Feb 2015.
    10. Lian Jian & Zheng Li & Tracy Xiao Liu, 2017. "Simultaneous versus sequential all-pay auctions: an experimental study," Experimental Economics, Springer;Economic Science Association, vol. 20(3), pages 648-669, September.
    11. John Morgan & Henrik Orzen & Martin Sefton & Dana Sisak, 2016. "Strategic and Natural Risk in Entrepreneurship: An Experimental Study," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 25(2), pages 420-454, April.

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    More about this item

    Keywords

    rent-seeking; contests; behavioural variation; Nash equilibrium; complexity;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D71 - Microeconomics - - Analysis of Collective Decision-Making - - - Social Choice; Clubs; Committees; Associations
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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