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Learning under limited information

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  • Chen, Yan
  • Khoroshilov, Yuri

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  • Chen, Yan & Khoroshilov, Yuri, 2003. "Learning under limited information," Games and Economic Behavior, Elsevier, vol. 44(1), pages 1-25, July.
  • Handle: RePEc:eee:gamebe:v:44:y:2003:i:1:p:1-25
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    References listed on IDEAS

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    1. Eric Friedman & Scott Shenker, 1998. "Learning and Implementation on the Internet," Departmental Working Papers 199821, Rutgers University, Department of Economics.
    2. 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.
    3. 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.
    4. Richard Mckelvey & Thomas Palfrey, 1998. "Quantal Response Equilibria for Extensive Form Games," Experimental Economics, Springer;Economic Science Association, vol. 1(1), pages 9-41, June.
    5. 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.
    6. Mookherjee, Dilip & Sopher, Barry, 1997. "Learning and Decision Costs in Experimental Constant Sum Games," Games and Economic Behavior, Elsevier, vol. 19(1), pages 97-132, April.
    7. 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.
    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. Yan Chen & Fang-Fang Tang, 1998. "Learning and Incentive-Compatible Mechanisms for Public Goods Provision: An Experimental Study," Journal of Political Economy, University of Chicago Press, vol. 106(3), pages 633-662, June.
    10. Teck-Hua Ho & Keith Weigelt, 1996. "Task Complexity, Equilibrium Selection, and Learning: An Experimental Study," Management Science, INFORMS, vol. 42(5), pages 659-679, May.
    11. Rubinstein, Ariel, 1988. "Similarity and decision-making under risk (is there a utility theory resolution to the Allais paradox?)," Journal of Economic Theory, Elsevier, vol. 46(1), pages 145-153, October.
    12. Friedman, Eric & Shor, Mikhael & Shenker, Scott & Sopher, Barry, 2004. "An experiment on learning with limited information: nonconvergence, experimentation cascades, and the advantage of being slow," Games and Economic Behavior, Elsevier, vol. 47(2), pages 325-352, May.
    13. 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.
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    Cited by:

    1. Ralph-C. Bayer & Elke Renner & Rupert Sausgruber, 2013. "Confusion and learning in the voluntary contributions game," Experimental Economics, Springer;Economic Science Association, vol. 16(4), pages 478-496, December.
    2. Yan Chen & Robert Gazzale, 2004. "When Does Learning in Games Generate Convergence to Nash Equilibria? The Role of Supermodularity in an Experimental Setting," American Economic Review, American Economic Association, vol. 94(5), pages 1505-1535, December.
    3. Chen, Yan & Takeuchi, Kan, 2010. "Multi-object auctions with package bidding: An experimental comparison of Vickrey and iBEA," Games and Economic Behavior, Elsevier, vol. 68(2), pages 557-579, March.
    4. Yan Chen & Laura Razzolini & Theodore Turocy, 2007. "Congestion allocation for distributed networks: an experimental study," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 33(1), pages 121-143, October.
    5. Naoki Funai, 2013. "An Adaptive Learning Model in Coordination Games," Discussion Papers 13-14, Department of Economics, University of Birmingham.
    6. Alexander Smajgl, 2007. "Modelling evolving rules for the use of common-pool resources in an agent-based model," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 5(2), pages 56-80.
    7. Ralph-C Bayer & Elke Renner & Rupert Sausgruber, 2009. "Confusion and Reinforcement Learning in Experimental Public Goods Games," NRN working papers 2009-22, The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria.
    8. Schuster, Stephan, 2010. "Network Formation with Adaptive Agents," MPRA Paper 27388, University Library of Munich, Germany.
    9. Alexander Smajgl, 2004. "Modelling the effect of learning and evolving rules on the use of common-pool resources," Computing in Economics and Finance 2004 178, Society for Computational Economics.
    10. 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.
    11. Naoki Funai, 2013. "An Adaptive Learning Model in Coordination Games," Games, MDPI, Open Access Journal, vol. 4(4), pages 1-22, November.
    12. Atanasios Mitropoulos, 2001. "On the Measurement of the Predictive Success of Learning Theories in Repeated Games," Experimental 0110001, EconWPA.
    13. Gailmard, Sean & Palfrey, Thomas R., 2005. "An experimental comparison of collective choice procedures for excludable public goods," Journal of Public Economics, Elsevier, vol. 89(8), pages 1361-1398, August.
    14. Wu, Hang & Bayer, Ralph-C, 2015. "Learning from inferred foregone payoffs," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 445-458.
    15. Atanasios Mitropoulos, 2001. "Little Information, Efficiency, and Learning - An Experimental Study," Game Theory and Information 0110002, EconWPA.
    16. Schuster, Stephan, 2012. "Applications in Agent-Based Computational Economics," MPRA Paper 47201, University Library of Munich, Germany.

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