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Bounded Memory, Inertia, Sampling and Weighting Model for Market Entry Games

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  • Wei Chen

    ()
    (Department of Economics, National Taiwan University, No.21 Hsu-Chow Rd., Taipei, Taiwan)

  • Shu-Yu Liu

    (Department of Economics, National Taiwan University, No.21 Hsu-Chow Rd., Taipei, Taiwan)

  • Chih-Han Chen

    (Department of Economics, National Taiwan University, No.21 Hsu-Chow Rd., Taipei, Taiwan)

  • Yi-Shan Lee

    (Department of Economics, National Taiwan University, No.21 Hsu-Chow Rd., Taipei, Taiwan)

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    Abstract

    This paper describes the “Bounded Memory, Inertia, Sampling and Weighting” (BI-SAW) model, which won the http://sites.google.com/site/gpredcomp/Market Entry Prediction Competition in 2010. The BI-SAW model refines the I-SAW Model (Erev et al. [1]) by adding the assumption of limited memory span. In particular, we assume when players draw a small sample to weight against the average payoff of all past experience, they can only recall 6 trials of past experience. On the other hand, we keep all other key features of the I-SAW model: (1) Reliance on a small sample of past experiences, (2) Strong inertia and recency effects, and (3) Surprise triggers change. We estimate this model using the first set of experimental results run by the competition organizers, and use it to predict results of a second set of similar experiments later ran by the organizers. We find significant improvement in out-of-sample predictability (against the I-SAW model) in terms of smaller mean normalized MSD, and such result is robust to resampling the predicted game set and reversing the role of the sets of experimental results. Our model’s performance is the best among all the participants.

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    Bibliographic Info

    Article provided by MDPI, Open Access Journal in its journal Games.

    Volume (Year): 2 (2011)
    Issue (Month): 1 (March)
    Pages: 187-199

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    Handle: RePEc:gam:jgames:v:2:y:2011:i:1:p:187-199:d:11753

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    Related research

    Keywords: learning; market entry game; prediction competition;

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    References

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    1. 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 848-81, September.
    2. Ido Erev & Eyal Ert & Alvin E. Roth, 2010. "Erev, I. et al . A Choice Prediction Competition for Market Entry Games: An Introduction. Games 2010, 1 , 117-136," Games, MDPI, Open Access Journal, vol. 1(3), pages 221-225, July.
    3. Timothy C. Salmon, 2001. "An Evaluation of Econometric Models of Adaptive Learning," Econometrica, Econometric Society, vol. 69(6), pages 1597-1628, November.
    4. Ido Erev & Eyal Ert & Alvin E. Roth, 2010. "A Choice Prediction Competition for Market Entry Games: An Introduction," Games, MDPI, Open Access Journal, vol. 1(2), pages 117-136, May.
    5. Tyran, Jean-Robert, 2003. "Behavioral Game Theory. Experiments in Strategic Interaction: Colin F. Camerer, Princeton University Press, Princeton, New Jersey, 2003, p. 550, Price $65.00/[UK pound]42.95, ISBN 0-691-09039-4," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 32(6), pages 717-720, December.
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    Cited by:
    1. 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.
    2. Thorsten Chmura & Sebastian Goerg & Reinhard Selten, 2011. "Learning in experimental 2 x 2 games," Working Paper Series of the Max Planck Institute for Research on Collective Goods 2011_26, Max Planck Institute for Research on Collective Goods.

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