IDEAS home Printed from https://ideas.repec.org/p/col/000092/003907.html
   My bibliography  Save this paper

An artificial economy based on reinforcement learning and agent based modeling

Author

Listed:
  • Fernando Lozano
  • Jaime Lozano
  • Mario García

Abstract

In this paper we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on convention. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions meaning that a firm is likely to behave as it neighbors if it observes that their actions lead to a good pay-off. On the other hand, we propose the use of reinforcement learning as a computational model for the role of goverment in the economy, as the agent that determines the fiscal policy, and whose objective is to maximize economy growth. We present the implementation of a simulator of the proposed model based on SWARM, that employs the SARSA algotithm combined wiht a multilayer perceptron as the function approximation for the action value function

Suggested Citation

  • Fernando Lozano & Jaime Lozano & Mario García, 2007. "An artificial economy based on reinforcement learning and agent based modeling," Documentos de Trabajo 3907, Universidad del Rosario.
    • Handle: RePEc:col:000092:003907
    as

    Download full text from publisher

    File URL: http://repository.urosario.edu.co/bitstream/handle/10336/10893/3907.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Cooper,Russell, 1999. "Coordination Games," Cambridge Books, Cambridge University Press, number 9780521578967, January.
    3. Hopkins, Ed, 1999. "Learning, Matching, and Aggregation," Games and Economic Behavior, Elsevier, vol. 26(1), pages 79-110, January.
    4. Brenner, Thomas, 2006. "Agent Learning Representation: Advice on Modelling Economic Learning," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 18, pages 895-947, Elsevier.
    5. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    6. Kenneth Judd & Scott E. Page, 2004. "Computational Public Economics," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 6(2), pages 195-202, May.
    7. Thomas J. Sargent & Francois R. Velde, 1998. "Optimal Fiscal Policy in a Linear Stochastic Economy," QM&RBC Codes 130, Quantitative Macroeconomics & Real Business Cycles.
    8. Borgers, Tilman & Sarin, Rajiv, 1997. "Learning Through Reinforcement and Replicator Dynamics," Journal of Economic Theory, Elsevier, vol. 77(1), pages 1-14, November.
    9. 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.
    10. Lane, David A, 1993. "Artificial Worlds and Economics, Part II," Journal of Evolutionary Economics, Springer, vol. 3(3), pages 177-197, August.
    11. Erev, Ido & Bereby-Meyer, Yoella & Roth, Alvin E., 1999. "The effect of adding a constant to all payoffs: experimental investigation, and implications for reinforcement learning models," Journal of Economic Behavior & Organization, Elsevier, vol. 39(1), pages 111-128, May.
    12. Lane, David A, 1993. "Artificial Worlds and Economics, Part I," Journal of Evolutionary Economics, Springer, vol. 3(2), pages 89-107, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Brini, Alessio & Tedeschi, Gabriele & Tantari, Daniele, 2023. "Reinforcement learning policy recommendation for interbank network stability," Journal of Financial Stability, Elsevier, vol. 67(C).

    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. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Gotts, Nicholas M. & Polhill, J. Gary, 2007. "Transient and asymptotic dynamics of reinforcement learning in games," Games and Economic Behavior, Elsevier, vol. 61(2), pages 259-276, November.
    2. Segismundo S. Izquierdo & Luis R. Izquierdo & Nicholas M. Gotts, 2008. "Reinforcement Learning Dynamics in Social Dilemmas," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(2), pages 1-1.
    3. Sieg, Gernot, 2001. "A political business cycle with boundedly rational agents," European Journal of Political Economy, Elsevier, vol. 17(1), pages 39-52, March.
    4. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    5. Martino Banchio & Giacomo Mantegazza, 2022. "Artificial Intelligence and Spontaneous Collusion," Papers 2202.05946, arXiv.org, revised Sep 2023.
    6. Ed Hopkins & Robert M. Seymour, 2002. "The Stability of Price Dispersion under Seller and Consumer Learning," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(4), pages 1157-1190, November.
    7. Albert Banal-Estañol & Augusto Rupérez-Micola, 2010. "Are agent-based simulations robust? The wholesale electricity trading case," Economics Working Papers 1214, Department of Economics and Business, Universitat Pompeu Fabra.
    8. Schuster, Stephan, 2012. "Applications in Agent-Based Computational Economics," MPRA Paper 47201, University Library of Munich, Germany.
    9. Ed Hopkins, 2002. "Two Competing Models of How People Learn in Games," Econometrica, Econometric Society, vol. 70(6), pages 2141-2166, November.
    10. 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.
    11. Panayotis Mertikopoulos & William H. Sandholm, 2016. "Learning in Games via Reinforcement and Regularization," Mathematics of Operations Research, INFORMS, vol. 41(4), pages 1297-1324, November.
    12. Giorgio Fagiolo & Paul Windrum & Alessio Moneta, 2006. "Empirical Validation of Agent Based Models: A Critical Survey," LEM Papers Series 2006/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    13. Geoffrey M. Hodgson & Thorbjørn Knudsen, 2008. "The Complex Evolution of a Simple Traffic Convention: the Functions and Implications of Habit," International Economic Association Series, in: János Kornai & László Mátyás & Gérard Roland (ed.), Institutional Change and Economic Behaviour, chapter 9, pages 178-199, Palgrave Macmillan.
    14. Paul Windrum & Giorgio Fagiolo & Alessio Moneta, 2007. "Empirical Validation of Agent-Based Models: Alternatives and Prospects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-8.
    15. Lahkar, Ratul & Seymour, Robert M., 2013. "Reinforcement learning in population games," Games and Economic Behavior, Elsevier, vol. 80(C), pages 10-38.
    16. Martino Banchio & Andrzej Skrzypacz, 2022. "Artificial Intelligence and Auction Design," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
    17. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    18. Atanasios Mitropoulos, 2001. "On the Measurement of the Predictive Success of Learning Theories in Repeated Games," Experimental 0110001, University Library of Munich, Germany.
    19. Mitropoulos, Atanasios, 2001. "Learning under minimal information: An experiment on mutual fate control," Journal of Economic Psychology, Elsevier, vol. 22(4), pages 523-557, August.
    20. 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.

    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:col:000092:003907. 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: Facultad de Economía (email available below). General contact details of provider: https://edirc.repec.org/data/ferosco.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.