IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v54y2019i1d10.1007_s10614-017-9721-5.html
   My bibliography  Save this article

Agent-Based Modeling of a Non-tâtonnement Process for the Scarf Economy: The Role of Learning

Author

Listed:
  • Shu-Heng Chen

    (National Chengchi University)

  • Bin-Tzong Chie

    (Tamkang University)

  • Ying-Fang Kao

    (National Chengchi University)

  • Ragupathy Venkatachalam

    (Goldsmiths, University of London)

Abstract

In this paper, we propose a meta-learning model to hierarchically integrate individual learning and social learning schemes. This meta-learning model is incorporated into an agent-based model to show that Herbert Scarf’s famous counterexample on Walrasian stability can become stable in some cases under a non-tâtonnement process when both learning schemes are involved, a result previously obtained by Herbert Gintis. However, we find that the stability of the competitive equilibrium depends on how individuals learn—whether they are innovators (individual learners) or imitators (social learners), and their switching frequency (mobility) between the two. We show that this endogenous behavior, apart from the initial population of innovators, is mainly determined by the agents’ intensity of choice. This study grounds the Walrasian competitive equilibrium based on the view of a balanced resource allocation between exploitation and exploration. This balance, achieved through a meta-learning model, is shown to be underpinned by a behavioral/psychological characteristic.

Suggested Citation

  • Shu-Heng Chen & Bin-Tzong Chie & Ying-Fang Kao & Ragupathy Venkatachalam, 2019. "Agent-Based Modeling of a Non-tâtonnement Process for the Scarf Economy: The Role of Learning," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 305-341, June.
  • Handle: RePEc:kap:compec:v:54:y:2019:i:1:d:10.1007_s10614-017-9721-5
    DOI: 10.1007/s10614-017-9721-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-017-9721-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10614-017-9721-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Apesteguia, Jose & Huck, Steffen & Oechssler, Jorg, 2007. "Imitation--theory and experimental evidence," Journal of Economic Theory, Elsevier, vol. 136(1), pages 217-235, September.
    2. Hommes, Cars & Zeppini, Paolo, 2014. "Innovate or Imitate? Behavioural technological change," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 308-324.
    3. Bossan, Benjamin & Jann, Ole & Hammerstein, Peter, 2015. "The evolution of social learning and its economic consequences," Journal of Economic Behavior & Organization, Elsevier, vol. 112(C), pages 266-288.
    4. Amos Tversky & Daniel Kahneman, 1991. "Loss Aversion in Riskless Choice: A Reference-Dependent Model," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(4), pages 1039-1061.
    5. Antoine Mandel, 2012. "Agent-based dynamics in the general equilibrium model," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00732823, HAL.
    6. 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.
    7. K. Vela Velupillai, 2015. "Iteration, tâtonnement, computation and economic dynamics," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 39(6), pages 1551-1567.
    8. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    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. William A. Brock & Cars H. Hommes, 1997. "A Rational Route to Randomness," Econometrica, Econometric Society, vol. 65(5), pages 1059-1096, September.
    11. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
    12. Thomas Brenner, 1998. "Can evolutionary algorithms describe learning processes?," Journal of Evolutionary Economics, Springer, vol. 8(3), pages 271-283.
    13. Robert Axelrod, 1997. "Advancing the Art of Simulation in the Social Sciences," Working Papers 97-05-048, Santa Fe Institute.
    14. William A. Brock & Cars H. Hommes, 2001. "A Rational Route to Randomness," Chapters, in: W. D. Dechert (ed.), Growth Theory, Nonlinear Dynamics and Economic Modelling, chapter 16, pages 402-438, Edward Elgar Publishing.
    15. Mandel, Antoine & Landini, Simone & Gallegati, Mauro & Gintis, Herbert, 2015. "Price dynamics, financial fragility and aggregate volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 257-277.
    16. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    17. Robert Axtell, 2005. "The Complexity of Exchange," Economic Journal, Royal Economic Society, vol. 115(504), pages 193-210, June.
    18. Albin, Peter & Foley, Duncan K., 1992. "Decentralized, dispersed exchange without an auctioneer : A simulation study," Journal of Economic Behavior & Organization, Elsevier, vol. 18(1), pages 27-51, June.
    19. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    20. Kitti, Mitri, 2010. "Convergence of iterative tâtonnement without price normalization," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1077-1091, June.
    21. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    22. 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.
    23. Arthur, W Brian, 1993. "On Designing Economic Agents That Behave Like Human Agents," Journal of Evolutionary Economics, Springer, vol. 3(1), pages 1-22, February.
    24. Anderson, Christopher M. & Plott, Charles R. & Shimomura, K.-I.Ken-Ichi & Granat, Sander, 2004. "Global instability in experimental general equilibrium: the Scarf example," Journal of Economic Theory, Elsevier, vol. 115(2), pages 209-249, April.
    25. Arrow, Kenneth J, 1974. "General Economic Equilibrium: Purpose, Analytic Techniques, Collective Choice," American Economic Review, American Economic Association, vol. 64(3), pages 253-272, June.
    26. Hommes, Cars, 2011. "The heterogeneous expectations hypothesis: Some evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 1-24, January.
    27. Ellison, Glenn & Fudenberg, Drew, 1993. "Rules of Thumb for Social Learning," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 612-643, August.
    28. Gintis Herbert, 2006. "The Emergence of a Price System from Decentralized Bilateral Exchange," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 6(1), pages 1-17, December.
    29. H. Uzawa, 1960. "Walras' Tâtonnement in the Theory of Exchange," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 27(3), pages 182-194.
    30. Herbert Gintis, 2007. "The Dynamics of General Equilibrium," Economic Journal, Royal Economic Society, vol. 117(523), pages 1280-1309, October.
    31. repec:hal:pseose:halshs-01152302 is not listed on IDEAS
    32. Shu-Heng Chen & Ragupathy Venkatachalam, 2017. "Information aggregation and computational intelligence," Evolutionary and Institutional Economics Review, Springer, vol. 14(1), pages 231-252, June.
    33. Erev, Ido & Rapoport, Amnon, 1998. "Coordination, "Magic," and Reinforcement Learning in a Market Entry Game," Games and Economic Behavior, Elsevier, vol. 23(2), pages 146-175, May.
    34. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
    35. repec:hal:pseose:halshs-00732823 is not listed on IDEAS
    36. Larry Samuelson, 1998. "Evolutionary Games and Equilibrium Selection," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262692198, April.
    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. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    2. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046, October.
    4. Hommes, Cars, 2011. "The heterogeneous expectations hypothesis: Some evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 1-24, January.
    5. Dziubiński, Marcin & Roy, Jaideep, 2012. "Popularity of reinforcement-based and belief-based learning models: An evolutionary approach," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 433-454.
    6. Mikhail Anufriev & Cars Hommes, 2012. "Evolutionary Selection of Individual Expectations and Aggregate Outcomes in Asset Pricing Experiments," American Economic Journal: Microeconomics, American Economic Association, vol. 4(4), pages 35-64, November.
    7. Cars Hommes, 2010. "The heterogeneous expectations hypothesis: some evidence from the lab," Post-Print hal-00753041, HAL.
    8. Schuster, Stephan, 2012. "Applications in Agent-Based Computational Economics," MPRA Paper 47201, University Library of Munich, Germany.
    9. Waltman, Ludo & Kaymak, Uzay, 2008. "Q-learning agents in a Cournot oligopoly model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3275-3293, October.
    10. Narine Udumyan & Juliette Rouchier & Dominique Ami, 2014. "Integration of Path-Dependency in a Simple Learning Model: The Case of Marine Resources," Computational Economics, Springer;Society for Computational Economics, vol. 43(2), pages 199-231, February.
    11. Tongkui Yu & Shu-Heng Chen, 2021. "Realizable Utility Maximization as a Mechanism for the Stability of Competitive General Equilibrium in a Scarf Economy," Computational Economics, Springer;Society for Computational Economics, vol. 58(1), pages 133-167, June.
    12. Hommes, Cars, 2018. "Behavioral & experimental macroeconomics and policy analysis: a complex systems approach," Working Paper Series 2201, European Central Bank.
    13. Pascal Seppecher & Isabelle Salle & Dany Lang, 2019. "Is the market really a good teacher?," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 299-335, March.
    14. Frijns, Bart & Lehnert, Thorsten & Zwinkels, Remco C.J., 2010. "Behavioral heterogeneity in the option market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2273-2287, November.
    15. Anufriev, Mikhail & Panchenko, Valentyn, 2009. "Asset prices, traders' behavior and market design," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1073-1090, May.
    16. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    17. Anufriev, Mikhail & Dindo, Pietro, 2010. "Wealth-driven selection in a financial market with heterogeneous agents," Journal of Economic Behavior & Organization, Elsevier, vol. 73(3), pages 327-358, March.
    18. Hommes, Cars & Kiseleva, Tatiana & Kuznetsov, Yuri & Verbic, Miroslav, 2012. "Is More Memory In Evolutionary Selection (De)Stabilizing?," Macroeconomic Dynamics, Cambridge University Press, vol. 16(3), pages 335-357, June.
    19. Raquel Almeida Ramos & Federico Bassi & Dany Lang, 2020. "Bet against the trend and cash in profits," CEPN Working Papers halshs-02956879, HAL.
    20. Edoardo Gaffeo & Mauro Gallegati & Umberto Gostoli, 2015. "An agent-based “proof of principle” for Walrasian macroeconomic theory," Computational and Mathematical Organization Theory, Springer, vol. 21(2), pages 150-183, June.

    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:kap:compec:v:54:y:2019:i:1:d:10.1007_s10614-017-9721-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.