IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v24y2005i4p383-408.html
   My bibliography  Save this article

Population Learning in a Model with Random Payoff Landscapes and Endogenous Networks

Author

Listed:
  • Giorgio Fagiolo

    ()

  • Luigi Marengo
  • Marco Valente

Abstract

Population learning in dynamic economies with endogenous network formation has been traditionally studied in basic settings where agents face quite simple and predictable strategic situations (e.g. coordination). In this paper, we start instead to explore economies where the payoff landscape is very complicated (rugged). We propose a model where the payoff to any agent changes in an unpredictable way as soon as any small variation in the strategy configuration within its network occurs. We study population learning where agents: (i) are allowed to periodically adjust both the strategy they play in the game and their interaction network; (ii) employ some simple criteria (e.g. statistics such as MIN, MAX, MEAN, etc.) to myopically form expectations about their payoff under alternative strategy and network configurations. Computer simulations show that: (i) allowing for endogenous networks implies higher average payoff as compared to static networks; (ii) populations learn by employing network updating as a “global learning” device, while strategy updating is used to perform “fine tuning”; (iii) the statistics employed to evaluate payoffs strongly affect the efficiency of the system, i.e. convergence to a unique (multiple) steady-state(s); (iv) for some class of statistics (e.g. MIN or MAX), the likelihood of efficient population learning strongly depends on whether agents are change-averse in discriminating between options associated to the same expected payoff. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • Giorgio Fagiolo & Luigi Marengo & Marco Valente, 2005. "Population Learning in a Model with Random Payoff Landscapes and Endogenous Networks," Computational Economics, Springer;Society for Computational Economics, vol. 24(4), pages 383-408, June.
  • Handle: RePEc:kap:compec:v:24:y:2005:i:4:p:383-408
    DOI: 10.1007/s10614-005-6160-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10614-005-6160-5
    Download Restriction: Access to full text is restricted to subscribers.

    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. Goyal, S. & Vega-Redondo, F., 2000. "Learning, Network Formation and Coordination," Econometric Institute Research Papers EI 9954-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Ellison, Glenn, 1993. "Learning, Local Interaction, and Coordination," Econometrica, Econometric Society, vol. 61(5), pages 1047-1071, September.
    3. Jackson, Matthew O. & Watts, Alison, 2002. "On the formation of interaction networks in social coordination games," Games and Economic Behavior, Elsevier, vol. 41(2), pages 265-291, November.
    4. Fagiolo, Giorgio, 2005. "Endogenous neighborhood formation in a local coordination model with negative network externalities," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 297-319, January.
    5. Alan Kirman, 1997. "The economy as an evolving network," Journal of Evolutionary Economics, Springer, vol. 7(4), pages 339-353.
    6. Edward Droste & Robert P. Gilles & Cathleen Johnson, 2000. "Evolution of Conventions in Endogenous Social Networks," Econometric Society World Congress 2000 Contributed Papers 0594, Econometric Society.
    7. William A. Brock & Steven N. Durlauf, 2001. "Discrete Choice with Social Interactions," Review of Economic Studies, Oxford University Press, vol. 68(2), pages 235-260.
    8. Blume Lawrence E., 1993. "The Statistical Mechanics of Strategic Interaction," Games and Economic Behavior, Elsevier, vol. 5(3), pages 387-424, July.
    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. Sylvie Geisendorf, 2010. "Searching NK Fitness Landscapes: On the Trade Off Between Speed and Quality in Complex Problem Solving," Computational Economics, Springer;Society for Computational Economics, vol. 35(4), pages 395-406, April.

    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:24:y:2005:i:4:p:383-408. 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: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: http://www.springer.com .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.