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Do We Learn from Our Own Experience or from Observing Others?

Author

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  • Ralph-C. Bayer

    () (School of Economics, University of Adelaide)

  • Hang Wu

    () (School of Economics, University of Adelaide)

Abstract

Learning in real life is based on different processes. Humans learn to a certain extent from their own experience but also learn by observing what non directly related others have done. In this paper we propose a generalized payoff assessment learning (GPAL) model which enables us to evaluate the relative influences of these two different models of learning. We apply GPAL to a homogeneous good Bertrand duopoly experiment with random matching and population pricing information. The model explains the observed pricing and learning behavior at least as well and often better than learning models from the literature but has the advantage that the relative influence of the learning models can be estimated. We find that the own experience overwhelmingly dominates learning, despite the useful information about behavior of potential future opponents contained in the population price distribution.

Suggested Citation

  • Ralph-C. Bayer & Hang Wu, 2013. "Do We Learn from Our Own Experience or from Observing Others?," School of Economics Working Papers 2013-21, University of Adelaide, School of Economics.
  • Handle: RePEc:adl:wpaper:2013-21
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    File URL: https://media.adelaide.edu.au/economics/papers/doc/wp2013-21.pdf
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    References listed on IDEAS

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    1. 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.
    2. Klaus Abbink & Abdolkarim Sadrieh & Shmuel Zamir, 2004. "Fairness, Public Good, and Emotional Aspects of Punishment Behavior," Theory and Decision, Springer, vol. 57(1), pages 25-57, August.
    3. Armantier, Olivier, 2004. "Does observation influence learning?," Games and Economic Behavior, Elsevier, vol. 46(2), pages 221-239, February.
    4. Huck, Steffen & Normann, Hans-Theo & Oechssler, Jorg, 2000. "Does information about competitors' actions increase or decrease competition in experimental oligopoly markets?," International Journal of Industrial Organization, Elsevier, vol. 18(1), pages 39-57, January.
    5. 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.
    6. Charles Abramson & Imran S. Currim & Rakesh Sarin, 2005. "An Experimental Investigation of the Impact of Information on Competitive Decision Making," Management Science, INFORMS, vol. 51(2), pages 195-207, February.
    7. Cheung, Yin-Wong & Friedman, Daniel, 1997. "Individual Learning in Normal Form Games: Some Laboratory Results," Games and Economic Behavior, Elsevier, vol. 19(1), pages 46-76, April.
    8. Reinhard Selten, 1998. "Axiomatic Characterization of the Quadratic Scoring Rule," Experimental Economics, Springer;Economic Science Association, vol. 1(1), pages 43-61, June.
    9. repec:kap:expeco:v:1:y:1998:i:1:p:43-62 is not listed on IDEAS
    10. Sarin, Rajiv & Vahid, Farshid, 1999. "Payoff Assessments without Probabilities: A Simple Dynamic Model of Choice," Games and Economic Behavior, Elsevier, vol. 28(2), pages 294-309, August.
    11. Ralph-C. Bayer & Hang Wu & Mickey Chan, 2013. "Explaining Price Dispersion and Dynamics in Laboratory Bertrand Markets," School of Economics Working Papers 2013-16, University of Adelaide, School of Economics.
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    Cited by:

    1. Hanaki, Nobuyuki & Kirman, Alan & Pezanis-Christou, Paul, 2018. "Observational and reinforcement pattern-learning: An exploratory study," European Economic Review, Elsevier, vol. 104(C), pages 1-21.
    2. Nobuyuki Hanaki & Alan Kirman & Paul Pezanis-Christou, 2016. "Counter Intuitive Learning: An Exploratory Study," School of Economics Working Papers 2016-12, University of Adelaide, School of Economics.

    More about this item

    Keywords

    Learning; Information; Bertrand Duopoly; Experiment;

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets

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