IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Little Information, Efficiency, and Learning - An Experimental Study

  • Atanasios Mitropoulos

Earlier experiments have shown that under little information subjects are hardly able to coordinate even though there are no conflicting interests and subjects are organised in fixed pairs. This is so, even though a simple adjustment process would lead the subjects into the efficient, fair and individually payoff maximising outcome. We draw on this finding and design an experiment in which subjects re-peatedly play 4 simple games within 4 sets of 40 rounds under little information. This way we are able to investigate (i) the coordination abilities of the subjects depending on the underlying game, (ii) the resulting efficiency loss, and (iii) the adjustment of the learning rule.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://econwpa.repec.org/eps/game/papers/0110/0110002.pdf
Download Restriction: no

Paper provided by EconWPA in its series Game Theory and Information with number 0110002.

as
in new window

Length: 49 pages
Date of creation: 18 Oct 2001
Date of revision:
Handle: RePEc:wpa:wuwpga:0110002
Note: Type of Document - Acrobat PDF; prepared on IBM PC - MS-Word; to print on HP A4-Size; pages: 49; figures: included
Contact details of provider: Web page: http://econwpa.repec.org

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. T. Borgers & R. Sarin, 2010. "Learning Through Reinforcement and Replicator Dynamics," Levine's Working Paper Archive 380, David K. Levine.
  2. Cross, John G, 1973. "A Stochastic Learning Model of Economic Behavior," The Quarterly Journal of Economics, MIT Press, vol. 87(2), pages 239-66, May.
  3. Guth, Werner, 1995. "On ultimatum bargaining experiments -- A personal review," Journal of Economic Behavior & Organization, Elsevier, vol. 27(3), pages 329-344, August.
  4. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
  5. Rosemarie Nagel & Nicolaas J. Vriend, 1997. "An experimental study of adaptive behavior in an oligopolistic market game," Economics Working Papers 230, Department of Economics and Business, Universitat Pompeu Fabra.
  6. Fudenberg, D. & Kreps, D.M., 1992. "Learning Mixed Equilibria," Working papers 92-13, Massachusetts Institute of Technology (MIT), Department of Economics.
  7. Steffen Huck & Hans-Theo Normann & Joerg Oechssler, 1997. "Learning in Cournot Oligopoly - An Experiment," Game Theory and Information 9707009, EconWPA, revised 22 Jul 1997.
  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. Chen, Yan & Khoroshilov, Yuri, 2003. "Learning under limited information," Games and Economic Behavior, Elsevier, vol. 44(1), pages 1-25, July.
  10. Brown, James N & Rosenthal, Robert W, 1990. "Testing the Minimax Hypothesis: A Re-examination of O'Neill's Game Experiment," Econometrica, Econometric Society, vol. 58(5), pages 1065-81, September.
  11. A. Roth & I. Er’ev, 2010. "Learning in Extensive Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Run," Levine's Working Paper Archive 387, David K. Levine.
  12. Gilboa, Itzhak & Schmeidler, David, 1996. "Case-Based Optimization," Games and Economic Behavior, Elsevier, vol. 15(1), pages 1-26, July.
  13. Tilman Slembeck, 1999. "Low Information Games - Experimental Evidence on Learning in Ultimatum Bargaining," Experimental 9905001, EconWPA.
  14. 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.
  15. Selten, Reinhard & Joachim Buchta, 1994. "Experimental Sealed Bid First Price Auctions with Directly Observed Bid Functions," Discussion Paper Serie B 270, University of Bonn, Germany.
  16. 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.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:wpa:wuwpga:0110002. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA)

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.