IDEAS home Printed from https://ideas.repec.org/p/igi/igierp/452.html
   My bibliography  Save this paper

The Weight of Personal Experience: an Experimental Measurement

Author

Listed:
  • Zacharias Maniadis
  • Joshua Miller

Abstract

We present an experiment to address the question of whether a piece of information is more influential if it comes from experience, rather than from another source. We employ a novel experimental design which controls for the value of information and other potentially important confounding factors present in related studies. Overall, our results show that an event that is personally experienced has a stronger influence on subsequent behavior than an observed event with equally valuable information content. Importantly, in early rounds when information is more valuable from a rational viewpoint, this overweighting of personal experience is not statistically significant. JEL Classification Numbers: C90; C91; Keywords: Experiments; Learning; Observation; Reinforcement Learning; Belief-Based Learning

Suggested Citation

  • Zacharias Maniadis & Joshua Miller, 2012. "The Weight of Personal Experience: an Experimental Measurement," Working Papers 452, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    • Handle: RePEc:igi:igierp:452
    as

    Download full text from publisher

    File URL: https://repec.unibocconi.it/igier/igi/wp/2012/452.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nick Feltovich & John Duffy, 1999. "Does observation of others affect learning in strategic environments? An experimental study," International Journal of Game Theory, Springer;Game Theory Society, vol. 28(1), pages 131-152.
    2. Uri Gneezy & Jan Potters, 1997. "An Experiment on Risk Taking and Evaluation Periods," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 631-645.
    3. Jacob K. Goeree & Thomas R. Palfrey & Brian W. Rogers & Richard D. McKelvey, 2007. "Self-Correcting Information Cascades," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(3), pages 733-762.
    4. Armantier, Olivier, 2004. "Does observation influence learning?," Games and Economic Behavior, Elsevier, vol. 46(2), pages 221-239, February.
    5. 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.
    6. 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.
    7. Merlo, Antonio & Schotter, Andrew, 2003. "Learning by not doing: an experimental investigation of observational learning," Games and Economic Behavior, Elsevier, vol. 42(1), pages 116-136, January.
    8. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    9. Simonsohn, Uri & Karlsson, Niklas & Loewenstein, George & Ariely, Dan, 2008. "The tree of experience in the forest of information: Overweighing experienced relative to observed information," Games and Economic Behavior, Elsevier, vol. 62(1), pages 263-286, January.
    10. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Oyarzun, Carlos & Sanjurjo, Adam & Nguyen, Hien, 2017. "Response functions," European Economic Review, Elsevier, vol. 98(C), pages 1-31.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shafran, Aric P., 2012. "Learning in games with risky payoffs," Games and Economic Behavior, Elsevier, vol. 75(1), pages 354-371.
    2. Ed Hopkins, 2002. "Two Competing Models of How People Learn in Games," Econometrica, Econometric Society, vol. 70(6), pages 2141-2166, November.
    3. Masiliūnas, Aidas, 2023. "Learning in rent-seeking contests with payoff risk and foregone payoff information," Games and Economic Behavior, Elsevier, vol. 140(C), pages 50-72.
    4. Wen, Yuanji, 2018. "Voluntary information acquisition in an asymmetric-Information game:comparing learning theories in the laboratory," Journal of Economic Behavior & Organization, Elsevier, vol. 150(C), pages 202-219.
    5. Mohlin, Erik & Östling, Robert & Wang, Joseph Tao-yi, 2020. "Learning by similarity-weighted imitation in winner-takes-all games," Games and Economic Behavior, Elsevier, vol. 120(C), pages 225-245.
    6. Funai, Naoki, 2022. "Reinforcement learning with foregone payoff information in normal form games," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 638-660.
    7. Dennis A. V. Dittrich & Werner Güth & Martin G. Kocher & Paul Pezanis‐Christou, 2012. "Loss Aversion and Learning to Bid," Economica, London School of Economics and Political Science, vol. 79(314), pages 226-257, April.
    8. Holger Herz & Dmitry Taubinsky, 2018. "What Makes a Price Fair? An Experimental Study of Transaction Experience and Endogenous Fairness Views," Journal of the European Economic Association, European Economic Association, vol. 16(2), pages 316-352.
    9. Yoella Bereby-Meyer & Alvin E. Roth, 2006. "The Speed of Learning in Noisy Games: Partial Reinforcement and the Sustainability of Cooperation," American Economic Review, American Economic Association, vol. 96(4), pages 1029-1042, September.
    10. Wolf Ze'ev Ehrblatt & Kyle Hyndman & Erkut Y. ÄOzbay & Andrew Schotter, 2006. "Convergence: An Experimental Study," Levine's Working Paper Archive 122247000000001148, David K. Levine.
    11. Iriberri, Nagore & García-Pola, Bernardo, 2019. "Naivete and Sophistication in Initial and Repeated Play in Games," CEPR Discussion Papers 14088, C.E.P.R. Discussion Papers.
    12. Fréchette, Guillaume R., 2009. "Learning in a multilateral bargaining experiment," Journal of Econometrics, Elsevier, vol. 153(2), pages 183-195, December.
    13. Atanasios Mitropoulos, 2001. "Learning Under Little Information: An Experiment on Mutual Fate Control," Game Theory and Information 0110003, University Library of Munich, Germany.
    14. Ido Erev & Eyal Ert & Alvin E. Roth, 2010. "A Choice Prediction Competition for Market Entry Games: An Introduction," Games, MDPI, vol. 1(2), pages 1-20, May.
    15. Cooper, David J. & Stockman, Carol Kraker, 2002. "Fairness and learning: an experimental examination," Games and Economic Behavior, Elsevier, vol. 41(1), pages 26-45, October.
    16. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    17. Camerer, Colin F. & Ho, Teck-Hua, 2015. "Behavioral Game Theory Experiments and Modeling," Handbook of Game Theory with Economic Applications,, Elsevier.
    18. Nick Feltovich & Sobei H. Oda, 2014. "Special Section: Experiments on Learning, Methods, and Voting," Pacific Economic Review, Wiley Blackwell, vol. 19(3), pages 260-277, August.
    19. Mitropoulos, Atanasios, 2001. "Learning under minimal information: An experiment on mutual fate control," Journal of Economic Psychology, Elsevier, vol. 22(4), pages 523-557, August.
    20. Erik Mohlin & Robert Ostling & Joseph Tao-yi Wang, 2014. "Learning by Imitation in Games: Theory, Field, and Laboratory," Economics Series Working Papers 734, University of Oxford, Department of Economics.

    More about this item

    Keywords

    experiments; learning; observation; reinforcement learning; belief-based learning;
    All these keywords.

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igi:igierp:452. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: http://www.igier.unibocconi.it/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.