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Voluntary information acquisition in an asymmetric-Information game:comparing learning theories in the laboratory

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  • Wen, Yuanji

Abstract

This paper uses an experimental design of voluntary information acquisition to assess the information assumptions of different learning models. The design is implemented in two-stage asymmetric-information games. Subjects’ information-seeking behavior reveals that they tend to choose certain information sets that are consistent with belief-based learning theories rather than reinforcement theories. A hybrid-learning model with information acquisition that is a variant of the Generalized Experience-Weighted-Attraction (GEWA) model (Shafran, 2012) is also proposed. It successfully captures the different learning speeds of two groups of subjects (i.e., informed and uninformed subjects), and shows that once information acquisition data is added into a structural model that focuses on action data alone, the performance is enhanced. Additional individual analysis indicates that the information acquisition behavior assumed by learning models appears to suggest the learning rule subjects follow. The results suggest that tracking subjects’ voluntary information choices is a useful tool for analyzing their learning behaviors.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jeborg:v:150:y:2018:i:c:p:202-219
    DOI: 10.1016/j.jebo.2018.03.023
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    as
    1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    2. Daniel T. Knoepfle & Joseph Tao-yi Wang & Colin F. Camerer, 2009. "Studying Learning in Games Using Eye-Tracking," Journal of the European Economic Association, MIT Press, vol. 7(2-3), pages 388-398, 04-05.
    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. Vincent P. Crawford & Miguel A. Costa-Gomes, 2006. "Cognition and Behavior in Two-Person Guessing Games: An Experimental Study," American Economic Review, American Economic Association, vol. 96(5), pages 1737-1768, December.
    5. Jacquemet, Nicolas & Koessler, Frédéric, 2013. "Using or hiding private information? An experimental study of zero-sum repeated games with incomplete information," Games and Economic Behavior, Elsevier, vol. 78(C), pages 103-120.
    6. 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.
    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. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    9. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    10. Glosten, Lawrence R, 1994. "Is the Electronic Open Limit Order Book Inevitable?," Journal of Finance, American Finance Association, vol. 49(4), pages 1127-1161, September.
    11. David Danz & Dietmar Fehr & Dorothea Kübler, 2012. "Information and beliefs in a repeated normal-form game," Experimental Economics, Springer;Economic Science Association, vol. 15(4), pages 622-640, December.
    12. 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.
    13. Isabelle Brocas & Juan D. Carrillo & Stephanie W. Wang & Colin F. Camerer, 2014. "Imperfect Choice or Imperfect Attention? Understanding Strategic Thinking in Private Information Games," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(3), pages 944-970.
    14. Shafran, Aric P., 2012. "Learning in games with risky payoffs," Games and Economic Behavior, Elsevier, vol. 75(1), pages 354-371.
    15. Easley, David & O'Hara, Maureen, 1987. "Price, trade size, and information in securities markets," Journal of Financial Economics, Elsevier, vol. 19(1), pages 69-90, September.
    16. 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.
    17. 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.
    18. Tang, Fang-Fang, 2001. "Anticipatory learning in two-person games: some experimental results," Journal of Economic Behavior & Organization, Elsevier, vol. 44(2), pages 221-232, February.
    19. Andrés Salamanca & Olga Manrique Chaparro, 2016. "Some Strategic Aspects of Private Information: An Experimental Study," Working Papers hal-01305213, HAL.
    20. Feltovich, Nick, 1999. "Equilibrium and reinforcement learning in private-information games: An experimental study," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1605-1632, September.
    21. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    22. Duffy, John & Hopkins, Ed, 2005. "Learning, information, and sorting in market entry games: theory and evidence," Games and Economic Behavior, Elsevier, vol. 51(1), pages 31-62, April.
    23. Costa-Gomes, Miguel & Crawford, Vincent P & Broseta, Bruno, 2001. "Cognition and Behavior in Normal-Form Games: An Experimental Study," Econometrica, Econometric Society, vol. 69(5), pages 1193-1235, September.
    24. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
    25. TeckH. Ho & Xin Wang & ColinF. Camerer, 2008. "Individual Differences in EWA Learning with Partial Payoff Information," Economic Journal, Royal Economic Society, vol. 118(525), pages 37-59, January.
    26. 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.
    27. Robert J. Aumann, 1995. "Repeated Games with Incomplete Information," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262011476, December.
    28. Spiliopoulos, Leonidas, 2012. "Pattern recognition and subjective belief learning in a repeated constant-sum game," Games and Economic Behavior, Elsevier, vol. 75(2), pages 921-935.
    29. Martin G. Kocher & Matthias Sutter, 2005. "The Decision Maker Matters: Individual Versus Group Behaviour in Experimental Beauty-Contest Games," Economic Journal, Royal Economic Society, vol. 115(500), pages 200-223, January.
    30. 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.
    31. Yaw Nyarko & Andrew Schotter, 2002. "An Experimental Study of Belief Learning Using Elicited Beliefs," Econometrica, Econometric Society, vol. 70(3), pages 971-1005, May.
    32. Timothy Salmon, 2004. "Evidence for Learning to Learn Behavior in Normal Form Games," Theory and Decision, Springer, vol. 56(4), pages 367-404, April.
    33. Johnson, Eric J. & Camerer, Colin & Sen, Sankar & Rymon, Talia, 2002. "Detecting Failures of Backward Induction: Monitoring Information Search in Sequential Bargaining," Journal of Economic Theory, Elsevier, vol. 104(1), pages 16-47, May.
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    Cited by:

    1. Yuanji Wen & Stijn Masschelein & Anmol Ratan, 2022. "Loss aversion in asymmetric anti‐coordination games," Southern Economic Journal, John Wiley & Sons, vol. 88(4), pages 1549-1573, April.

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    More about this item

    Keywords

    Discrimination among learning models; Historical information lookups; Mixed-population learning; Two-stage asymmetric-information game;
    All these keywords.

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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