IDEAS home Printed from https://ideas.repec.org/p/wzb/wzebiv/fsiv98-13.html
   My bibliography  Save this paper

Learning in Sender-Receiver Games

Author

Listed:
  • Andreas Blume
  • Douglas V. DeJong
  • George R. Neumann
  • Nathan E. Savin

Abstract

Stimulus-response (SR) and belief-based learning (BBL) models are estimated with experimental data from sender-receiver games and compared using the Davidson and MacKinnon P-test for non-nested hypotheses. Depending on a certain adjustment parameter, the P-test favors the SR model, the BBL model or neither of the models. Following Camerer and Ho, the models are also compared to a hybrid model that incorporates a mixture of both types of learning. The hybrid model is frequently not significantly better than either the SR or the BBL model. The sensitivity of the results to observations taken after learning has ceased is investigated. ZUSAMMENFASSUNG - (Lernen in Sender-Empfänger-Spielen) Reiz-Reaktions- und überzeugungsgestützte Lernmodelle werden anhand von experimentellen Daten eines Senders-Empfänger-Spiels geschätzt und anhand des Davidson und MacKinnon P-Tests für nicht eingebundene Hypothesen verglichen. In Abhängigkeit eines bestimmten Anpassungsparameters stützt der P-Test das Reiz-Reaktions-Modell, das überzeugungsgestützte Lernmodell bzw. keines der Modelle. In Anlehnung an Camerer und Ho werden die Modelle auch in Form eines hybriden Modells verglichen, das eine Mischung beider Typen des Lernens beinhaltet. Das Hybridmodell ist häufig nicht signifikant besser als das Reiz-Reaktions-Modell bzw. das überzeugungsgestützte Lernmodell. Außerdem wird die Sensitivität der Ergebnisse im Hinblick auf Beobachtungen aufgezeigt, die nach Abschluß des Lernens gemacht wurden.

Suggested Citation

  • Andreas Blume & Douglas V. DeJong & George R. Neumann & Nathan E. Savin, 1998. "Learning in Sender-Receiver Games," CIG Working Papers FS IV 98-13, Wissenschaftszentrum Berlin (WZB), Research Unit: Competition and Innovation (CIG).
    • Handle: RePEc:wzb:wzebiv:fsiv98-13
    as

    Download full text from publisher

    File URL: http://skylla.wz-berlin.de/pdf/1998/iv98-13.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Fudenberg Drew & Kreps David M., 1993. "Learning Mixed Equilibria," Games and Economic Behavior, Elsevier, vol. 5(3), pages 320-367, July.
    2. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    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. Mookherjee Dilip & Sopher Barry, 1994. "Learning Behavior in an Experimental Matching Pennies Game," Games and Economic Behavior, Elsevier, vol. 7(1), pages 62-91, July.
    5. Blume, Andreas, et al, 1998. "Experimental Evidence on the Evolution of Meaning of Messages in Sender-Receiver Games," American Economic Review, American Economic Association, vol. 88(5), pages 1323-1340, December.
    6. McKelvey Richard D. & Palfrey Thomas R., 1995. "Quantal Response Equilibria for Normal Form Games," Games and Economic Behavior, Elsevier, vol. 10(1), pages 6-38, July.
    7. Boylan Richard T. & El-Gamal Mahmoud A., 1993. "Fictitious Play: A Statistical Study of Multiple Economic Experiments," Games and Economic Behavior, Elsevier, vol. 5(2), pages 205-222, April.
    8. 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.
    9. Borgers, Tilman & Sarin, Rajiv, 1997. "Learning Through Reinforcement and Replicator Dynamics," Journal of Economic Theory, Elsevier, vol. 77(1), pages 1-14, November.
    10. Davidson, Russell & MacKinnon, James G., 1984. "Convenient specification tests for logit and probit models," Journal of Econometrics, Elsevier, vol. 25(3), pages 241-262, July.
    11. Van Huyck, John B & Battalio, Raymond C & Rankin, Frederick W, 1997. "On the Origin of Convention: Evidence from Coordination Games," Economic Journal, Royal Economic Society, vol. 107(442), pages 576-596, May.
    12. Selten, Reinhard, 1998. "Features of experimentally observed bounded rationality," European Economic Review, Elsevier, vol. 42(3-5), pages 413-436, May.
    13. Gigler, F, 1994. "Self-Enforcing Voluntary Disclosures," Journal of Accounting Research, Wiley Blackwell, vol. 32(2), pages 224-240.
    14. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, December.
    15. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    16. 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.
    17. Crawford, Vincent P & Sobel, Joel, 1982. "Strategic Information Transmission," Econometrica, Econometric Society, vol. 50(6), pages 1431-1451, November.
    18. Stein, Jeremy C, 1989. "Cheap Talk and the Fed: A Theory of Imprecise Policy Announcements," American Economic Review, American Economic Association, vol. 79(1), pages 32-42, March.
    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. George R. Neumann & Nathan E. Savin, 2000. "Learning and Communication in Sender-Receiver Games: An Econometric Investigation," Econometric Society World Congress 2000 Contributed Papers 1852, Econometric Society.
    2. Blume, A. & DeJong, D.V. & Neumann, G. & Savin, N.E., 2000. "Learning and Communication in Sender-Reciever Games : An Economic Investigation," Other publications TiSEM 138dc36b-5269-421a-9e79-b, Tilburg University, School of Economics and Management.
    3. Ed Hopkins, 2002. "Two Competing Models of How People Learn in Games," Econometrica, Econometric Society, vol. 70(6), pages 2141-2166, November.
    4. Andreas Blume & Douglas V. DeJong & George R. Neumann & N. E. Savin, 2002. "Learning and communication in sender-receiver games: an econometric investigation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(3), pages 225-247.
    5. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
    6. Pangallo, Marco & Sanders, James B.T. & Galla, Tobias & Farmer, J. Doyne, 2022. "Towards a taxonomy of learning dynamics in 2 × 2 games," Games and Economic Behavior, Elsevier, vol. 132(C), pages 1-21.
    7. Blume, Andreas & Gneezy, Uri, 2000. "An Experimental Investigation of Optimal Learning in Coordination Games," Journal of Economic Theory, Elsevier, vol. 90(1), pages 161-172, January.
    8. Battalio,R. & Samuelson,L. & Huyck,J. van, 1998. "Risk dominance, payoff dominance and probabilistic choice learning," Working papers 2, Wisconsin Madison - Social Systems.
    9. 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.
    10. Atanasios Mitropoulos, 2001. "Learning Under Little Information: An Experiment on Mutual Fate Control," Game Theory and Information 0110003, University Library of Munich, Germany.
    11. Teck H. Ho & Xin Wang & Colin F. Camerer, 2008. "Individual Differences in EWA Learning with Partial Payoff Information," Economic Journal, Royal Economic Society, vol. 118(525), pages 37-59, January.
    12. Teck H Ho & Colin Camerer & Juin-Kuan Chong, 2003. "Functional EWA: A one-parameter theory of learning in games," Levine's Working Paper Archive 506439000000000514, David K. Levine.
    13. Jaromír Kovářík & Friederike Mengel & José Gabriel Romero, 2018. "Learning in network games," Quantitative Economics, Econometric Society, vol. 9(1), pages 85-139, March.
      • Kovarik, Jaromir & Mengel, Friederike & Romero, José Gabriel, 2012. "Learning in Network Games," IKERLANAK http://www-fae1-eao1-ehu-, Universidad del País Vasco - Departamento de Fundamentos del Análisis Económico I.
    14. Ianni, A., 2002. "Reinforcement learning and the power law of practice: some analytical results," Discussion Paper Series In Economics And Econometrics 203, Economics Division, School of Social Sciences, University of Southampton.
    15. Benaïm, Michel & Hofbauer, Josef & Hopkins, Ed, 2009. "Learning in games with unstable equilibria," Journal of Economic Theory, Elsevier, vol. 144(4), pages 1694-1709, July.
    16. Erhao Xie, 2019. "Monetary Payoff and Utility Function in Adaptive Learning Models," Staff Working Papers 19-50, Bank of Canada.
    17. Cason, Timothy N. & Friedman, Daniel & Hopkins, Ed, 2010. "Testing the TASP: An experimental investigation of learning in games with unstable equilibria," Journal of Economic Theory, Elsevier, vol. 145(6), pages 2309-2331, November.
    18. 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.
    19. Hofbauer, Josef & Hopkins, Ed, 2005. "Learning in perturbed asymmetric games," Games and Economic Behavior, Elsevier, vol. 52(1), pages 133-152, July.
    20. Mengel, Friederike & Orlandi, Ludovica & Weidenholzer, Simon, 2022. "Match length realization and cooperation in indefinitely repeated games," Journal of Economic Theory, Elsevier, vol. 200(C).

    More about this item

    Keywords

    noncooperative games; laboratory; individual behavior; group behavior;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior

    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:wzb:wzebiv:fsiv98-13. 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: Jennifer Rontganger (email available below). General contact details of provider: https://edirc.repec.org/data/cicwzde.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.