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Dynamic Behavior and Player Types in Majoritarian Multi-Battle Contests

Author

Listed:
  • Alan Gelder

    (Economic Science Institute, Chapman University)

  • Dan Kovenock

    (Economic Science Institute, Chapman University)

Abstract

In a dynamic contest where it is costly to compete, a player who is behind must decide whether to surrender or to keep fighting in the face of bleak odds. We experimentally examine the game theoretic prediction of last stand behavior in a multi-battle contest with a winning prize and losing penalty, as well as the contrasting prediction of surrendering in the corresponding contest with no penalty. We find varied evidence in support of these hypotheses in the aggregated data, but more conclusive evidence when scrutinizing individual player behavior. Players’ realized strategies tend to conform to one of several “types”. We develop a taxonomy to classify player types and study how these types interact and how their incidence varies across treatments. Contrary to the theoretical prediction, escalation is the predominant behavior, but last stand and surrendering behaviors also arise at rates responsive to the importance of losing penalties.

Suggested Citation

  • Alan Gelder & Dan Kovenock, 2016. "Dynamic Behavior and Player Types in Majoritarian Multi-Battle Contests," Working Papers 16-12, Chapman University, Economic Science Institute.
  • Handle: RePEc:chu:wpaper:16-12
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Shakun D. Mago & Roman M. Sheremeta, 2019. "New Hampshire Effect: behavior in sequential and simultaneous multi-battle contests," Experimental Economics, Springer;Economic Science Association, vol. 22(2), pages 325-349, June.
    2. Dan Kovenock & Brian Roberson & Roman M. Sheremeta, 2019. "The attack and defense of weakest-link networks," Public Choice, Springer, vol. 179(3), pages 175-194, June.
    3. Sheremeta, Roman, 2018. "Experimental Research on Contests," MPRA Paper 89327, University Library of Munich, Germany.
    4. Deck, Cary & Sheremeta, Roman M., 2019. "The tug-of-war in the laboratory," European Journal of Political Economy, Elsevier, vol. 60(C).
    5. Shakun D. Mago & Roman M. Sheremeta, 2017. "Multi‐battle Contests: An Experimental Study," Southern Economic Journal, John Wiley & Sons, vol. 84(2), pages 407-425, October.
    6. Kai A. Konrad & Florian Morath, 2020. "Escalation in conflict games: on beliefs and selection," Experimental Economics, Springer;Economic Science Association, vol. 23(3), pages 750-787, September.
    7. Kimbrough, Erik O. & Laughren, Kevin & Sheremeta, Roman, 2020. "War and conflict in economics: Theories, applications, and recent trends," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 998-1013.
    8. Vladimir Petkov, 2023. "Prize formation and sharing in multi-stage contests," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 75(1), pages 259-289, January.
    9. Xue, Lian & Sitzia, Stefania & Turocy, Theodore L., 2023. "Concord and contention in a dynamic unstructured bargaining experiment with costly conflict," Journal of Economic Psychology, Elsevier, vol. 97(C).
    10. Arne Lauber & Christoph March & Marco Sahm, 2022. "Optimal and Fair Prizing in Sequential Round-Robin Tournaments: Experimental Evidence," CESifo Working Paper Series 9651, CESifo.
    11. Kai A. Konrad & Florian Morath, 2017. "Escalation in Dynamic Conflict: On Beliefs and Selection," Working Papers tax-mpg-rps-2017-05, Max Planck Institute for Tax Law and Public Finance.
    12. Daniel Houser & Jian Song, 2021. "Costly Waiting in Dynamic Contests: Theory and Experiment," Working Papers 1082, George Mason University, Interdisciplinary Center for Economic Science.
    13. Lauber, Arne & March, Christoph & Sahm, Marco, 2023. "Optimal and fair prizing in sequential round-robin tournaments: Experimental evidence," Games and Economic Behavior, Elsevier, vol. 141(C), pages 30-51.

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    More about this item

    Keywords

    Dynamic Contest; Multi-Battle Contest; Player Type; Experiment; All-Pay Auction; Escalation; Last Stand; Maximin;
    All these keywords.

    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • D74 - Microeconomics - - Analysis of Collective Decision-Making - - - Conflict; Conflict Resolution; Alliances; Revolutions

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