IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/28007.html
   My bibliography  Save this paper

Learning, Generalization and the Perception of Information: an Experimental Study

Author

Listed:
  • Novarese, Marco
  • Lanteri, Alessandro
  • Tibaldeschi, Cesare

Abstract

This article experimentally explores the way in which human agents learn how to process and manage new information. In an abstract setting, players should perform an everyday task: selecting information, making generalizations, distinguishing contexts. The tendency to generalize is common to all participants, but in a different way. Best players have a stringer tendency to generalise rules. A high score is, in fact, associated with low entropy for mistakes, that is with a tendency to repeat the same mistakes over and over. Though the repetition of mistakes might be considered a failure to properly employ feedback or a bias, it may instead turn out as a viable and successful procedure. This result is connected to the literature on learning.

Suggested Citation

  • Novarese, Marco & Lanteri, Alessandro & Tibaldeschi, Cesare, 2010. "Learning, Generalization and the Perception of Information: an Experimental Study," MPRA Paper 28007, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:28007
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/28007/1/MPRA_paper_28007.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
    2. Salvatore Rizzello & Margherita Turvani, 2002. "Subjective Diversity and Social Learning: A Cognitive Perspective for Understanding Institutional Behavior," Constitutional Political Economy, Springer, vol. 13(2), pages 197-210, June.
    3. 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.
    4. Novarese, Marco & Lanteri, Alessandro, 2007. "Individual learning: theory formation, and feedback in a complex task," MPRA Paper 3049, University Library of Munich, Germany.
    5. Heiner, Ronald A, 1983. "The Origin of Predictable Behavior," American Economic Review, American Economic Association, vol. 73(4), pages 560-595, September.
    Full references (including those not matched with items on IDEAS)

    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. Xie, Erhao, 2021. "Empirical properties and identification of adaptive learning models in behavioral game theory," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 798-821.
    2. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    3. Kyle Hyndman & Antoine Terracol & Jonathan Vaksmann, 2009. "Learning and sophistication in coordination games," Experimental Economics, Springer;Economic Science Association, vol. 12(4), pages 450-472, December.
    4. Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Joseph E. Stiglitz & Tania Treibich, 2020. "Rational Heuristics? Expectations And Behaviors In Evolving Economies With Heterogeneous Interacting Agents," Economic Inquiry, Western Economic Association International, vol. 58(3), pages 1487-1516, July.
    5. Tilman Slembeck, 1999. "A Behavioral Approach to Learning in Economics - Towards an Economic Theory of Contingent Learning," Microeconomics 9905001, University Library of Munich, Germany.
    6. Victor Aguirregabiria & Jihye Jeon, 2020. "Firms’ Beliefs and Learning: Models, Identification, and Empirical Evidence," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 56(2), pages 203-235, March.
    7. Hommes, Cars, 2018. "Behavioral & experimental macroeconomics and policy analysis: a complex systems approach," Working Paper Series 2201, European Central Bank.
    8. repec:hal:spmain:info:hdl:2441/31dhti786q9k0q2i04klh6no54 is not listed on IDEAS
    9. Élise PAYZAN LE NESTOUR, 2010. "Bayesian Learning in UnstableSettings: Experimental Evidence Based on the Bandit Problem," Swiss Finance Institute Research Paper Series 10-28, Swiss Finance Institute.
    10. Sieg, Gernot, 2001. "A political business cycle with boundedly rational agents," European Journal of Political Economy, Elsevier, vol. 17(1), pages 39-52, March.
    11. Orphanides, Athanasios & Williams, John C., 2008. "Learning, expectations formation, and the pitfalls of optimal control monetary policy," Journal of Monetary Economics, Elsevier, vol. 55(Supplemen), pages 80-96, October.
    12. Noah Gans & George Knox & Rachel Croson, 2007. "Simple Models of Discrete Choice and Their Performance in Bandit Experiments," Manufacturing & Service Operations Management, INFORMS, vol. 9(4), pages 383-408, December.
    13. Terry E. Daniel & Eyran J. Gisches & Amnon Rapoport, 2009. "Departure Times in Y-Shaped Traffic Networks with Multiple Bottlenecks," American Economic Review, American Economic Association, vol. 99(5), pages 2149-2176, December.
    14. Iftekhar, M. S. & Tisdell, J. G., 2018. "Learning in repeated multiple unit combinatorial auctions: An experimental study," Working Papers 267301, University of Western Australia, School of Agricultural and Resource Economics.
    15. Atanas Christev, 2006. "Learning Hyperinflations," Computing in Economics and Finance 2006 475, Society for Computational Economics.
    16. Jieming Zhu, 2005. "A Transitional Institution for the Emerging Land Market in Urban China," Urban Studies, Urban Studies Journal Limited, vol. 42(8), pages 1369-1390, July.
    17. Giuseppe Ferrero, 2004. "Monetary Policy and the Transition to Rational Expectations," Econometric Society 2004 North American Summer Meetings 101, Econometric Society.
    18. Stefano Eusepi & Bruce Preston, 2008. "Stabilizing Expectations under Monetary and Fiscal Policy Coordination," NBER Working Papers 14391, National Bureau of Economic Research, Inc.
    19. Peter Wheale & David Hinton, 2007. "Ethical consumers in search of markets," Business Strategy and the Environment, Wiley Blackwell, vol. 16(4), pages 302-315, May.
    20. Wieland, Volker, 2000. "Monetary policy, parameter uncertainty and optimal learning," Journal of Monetary Economics, Elsevier, vol. 46(1), pages 199-228, August.
    21. Ennis, Huberto M. & Keister, Todd, 2005. "Government policy and the probability of coordination failures," European Economic Review, Elsevier, vol. 49(4), pages 939-973, May.

    More about this item

    Keywords

    behavioural entropy; cognitive economics; complexity; experiments; feedback; heuristics; learning;
    All these keywords.

    JEL classification:

    • A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • 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:pra:mprapa:28007. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.