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Impulse Response Dynamics in Weakest Link Games

Author

Listed:
  • Goerg Sebastian J.

    (Florida State University,Tallahassee, United States of America)

  • Sadrieh Abdolkarim

    (University of Magdeburg,Magdeburg, Germany)

  • Neugebauer Tibor

    (University of Luxembourg,Esch-sur-Alzette, Luxemburg)

Abstract

In a recent paper, Croson et al. (2015) experimentally study three weakest link games with multiple symmetric equilibria. They demonstrate that static concepts based on the Nash equilibrium (including multiple Nash equilibria, quantal response equilibria, and equilibrium selection by risk and payoff dominance) cannot successfully capture the observed treatment differences. Using Reinhard Selten’s impulse response dynamics, we derive a proposition that provides a theoretical ranking of contribution levels in the weakest link games. We show that the predicted ranking of treatment outcomes is fully consistent with the observed data. In addition, we demonstrate that the impulse response dynamics perform well in tracking average contributions over time. We conclude that Reinhard Selten’s impulse response dynamics provide an extremely valuable behavioral approach that is not only capable of resolving the indecisiveness of static approaches in games with many equilibria, but that can also be used to track the development of choices over time in games with repeated interaction.

Suggested Citation

  • Goerg Sebastian J. & Sadrieh Abdolkarim & Neugebauer Tibor, 2016. "Impulse Response Dynamics in Weakest Link Games," German Economic Review, De Gruyter, vol. 17(3), pages 284-297, August.
  • Handle: RePEc:bpj:germec:v:17:y:2016:i:3:p:284-297
    DOI: 10.1111/geer.12100
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    References listed on IDEAS

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    1. John C. Harsanyi & Reinhard Selten, 1988. "A General Theory of Equilibrium Selection in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262582384.
    2. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    3. Neugebauer, Tibor & Selten, Reinhard, 2006. "Individual behavior of first-price auctions: The importance of information feedback in computerized experimental markets," Games and Economic Behavior, Elsevier, vol. 54(1), pages 183-204, January.
    4. Sebastian Goerg & Reinhard Selten, 2009. "Experimental investigation of stationary concepts in cyclic duopoly games," Experimental Economics, Springer;Economic Science Association, vol. 12(3), pages 253-271, September.
    5. Chmura, Thorsten & Goerg, Sebastian J. & Selten, Reinhard, 2014. "Generalized Impulse Balance: An Experimental Test for a Class of 3 × 3 Games," Review of Behavioral Economics, now publishers, vol. 1(1-2), pages 27-53, January.
    6. Enrique Fatas & Tibor Neugebauer & Javier Perote, 2006. "Within‐Team Competition In The Minimum Effort Coordination Game," Pacific Economic Review, Wiley Blackwell, vol. 11(2), pages 247-266, June.
    7. Kahneman, Daniel & Tversky, Amos, 1979. "Prospect Theory: An Analysis of Decision under Risk," Econometrica, Econometric Society, vol. 47(2), pages 263-291, March.
    8. Reinhard Selten & Klaus Abbink & Ricarda Cox, 2005. "Learning Direction Theory and the Winner’s Curse," Experimental Economics, Springer;Economic Science Association, vol. 8(1), pages 5-20, April.
    9. Croson, Rachel & Fatas, Enrique & Neugebauer, Tibor & Morales, Antonio J., 2015. "Excludability: A laboratory study on forced ranking in team production," Journal of Economic Behavior & Organization, Elsevier, vol. 114(C), pages 13-26.
    10. Chmura, Thorsten & Goerg, Sebastian J. & Selten, Reinhard, 2012. "Learning in experimental 2×2 games," Games and Economic Behavior, Elsevier, vol. 76(1), pages 44-73.
    11. Ho, Teck H. & Camerer, Colin F. & Chong, Juin-Kuan, 2007. "Self-tuning experience weighted attraction learning in games," Journal of Economic Theory, Elsevier, vol. 133(1), pages 177-198, March.
    12. Jieyao Ding & Andreas Nicklisch, 2013. "On the Impulse in Impulse Learning," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2013_02, Max Planck Institute for Research on Collective Goods.
    13. Selten, Reinhard & Stoecker, Rolf, 1986. "End behavior in sequences of finite Prisoner's Dilemma supergames A learning theory approach," Journal of Economic Behavior & Organization, Elsevier, vol. 7(1), pages 47-70, March.
    14. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
    15. Reinhard Selten & Thorsten Chmura, 2008. "Stationary Concepts for Experimental 2x2-Games," American Economic Review, American Economic Association, vol. 98(3), pages 938-966, June.
    16. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    17. Ockenfels, Axel & Selten, Reinhard, 2014. "Impulse balance in the newsvendor game," Games and Economic Behavior, Elsevier, vol. 86(C), pages 237-247.
    18. Ding, Jieyao & Nicklisch, Andreas, 2013. "On the impulse in impulse learning," Economics Letters, Elsevier, vol. 121(2), pages 294-297.
    19. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
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    2. Claudia Keser & Alexia Gaudeul, 2016. "Foreword: Special Issue in Honor of Reinhard Selten's 85th Birthday," German Economic Review, Verein für Socialpolitik, vol. 17(3), pages 277-283, August.
    3. Edward Cartwright & Anna Stepanova & Lian Xue, 2019. "Impulse balance and framing effects in threshold public good games," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 21(5), pages 903-922, October.
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    5. Federica Alberti & Anna Cartwright & Edward Cartwright, 2021. "Predicting Efficiency in Threshold Public Good Games: A Learning Direction Theory Approach," Working Papers in Economics & Finance 2021-01, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    6. Edward Cartwright & Anna Stepanova, 2017. "Efficiency in a forced contribution threshold public good game," International Journal of Game Theory, Springer;Game Theory Society, vol. 46(4), pages 1163-1191, November.

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