IDEAS home Printed from https://ideas.repec.org/a/gam/jadmsc/v3y2013i3p53-75d27180.html
   My bibliography  Save this article

Individual Learning and Social Learning: Endogenous Division of Cognitive Labor in a Population of Co-evolving Problem-Solvers

Author

Listed:
  • Myong-Hun Chang

    (Department of Economics, Cleveland State University, Cleveland, OH 44115, USA)

  • Joseph E. Harrington

    (Department of Business Economics and Public Policy, The Wharton School, University of Pennsylvania, Philadelphia, PA 19102, USA)

Abstract

The dynamic choice between individual and social learning is explored for a population of autonomous agents whose objective is to find solutions to a stream of related problems. The probability that an agent is in the individual learning mode, as opposed to the social learning mode, evolves over time through reinforcement learning. Furthermore, the communication network of an agent is also endogenous. Our main finding is that when agents are sufficiently effective at social learning, structure emerges in the form of specialization. Some agents focus on coming up with new ideas while the remainder of the population focuses on imitating worthwhile ideas.

Suggested Citation

  • Myong-Hun Chang & Joseph E. Harrington, 2013. "Individual Learning and Social Learning: Endogenous Division of Cognitive Labor in a Population of Co-evolving Problem-Solvers," Administrative Sciences, MDPI, vol. 3(3), pages 1-23, July.
    • Handle: RePEc:gam:jadmsc:v:3:y:2013:i:3:p:53-75:d:27180
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-3387/3/3/53/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2076-3387/3/3/53/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    2. Myong-Hun Chang, "undated". "Discovery and Diffusion of Knowledge in an Endogenous Social Network," Modeling, Computing, and Mastering Complexity 2003 01, Society for Computational Economics.
    3. Myong-Hun Chang, 2011. "Emergent Social Learning Networks In Organizations With Heterogeneous Agents," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 14(02), pages 169-199.
    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. Joseph E. Harrington, Jr, 2005. "Innovators, Imitators, and the Evolving Architecture of Social Networks," Economics Working Paper Archive 529, The Johns Hopkins University,Department of Economics.
    2. Myong-Hun Chang & Joseph E. Harrington, 2007. "Innovators, Imitators, and the Evolving Architecture of Problem-Solving Networks," Organization Science, INFORMS, vol. 18(4), pages 648-666, August.
    3. Noah Gans & George Knox & Rachel Croson, 2007. "Simple Models of Discrete Choice and Their Performance in Bandit Experiments," Manufacturing & Service Operations Management, INFORMS, vol. 9(4), pages 383-408, December.
    4. Terracol, Antoine & Vaksmann, Jonathan, 2009. "Dumbing down rational players: Learning and teaching in an experimental game," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 54-71, May.
    5. Antonio Cabrales & Rosemarie Nagel & Roc Armenter, 2007. "Equilibrium selection through incomplete information in coordination games: an experimental study," Experimental Economics, Springer;Economic Science Association, vol. 10(3), pages 221-234, September.
    6. Iftekhar, M. S. & Tisdell, J. G., 2018. "Learning in repeated multiple unit combinatorial auctions: An experimental study," Working Papers 267301, University of Western Australia, School of Agricultural and Resource Economics.
    7. Astrid Dannenberg & Carlo Gallier, 2020. "The choice of institutions to solve cooperation problems: a survey of experimental research," Experimental Economics, Springer;Economic Science Association, vol. 23(3), pages 716-749, September.
    8. Georgios Piliouras & Ryann Sim & Stratis Skoulakis, 2021. "Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update," Papers 2111.14737, arXiv.org, revised Jun 2022.
    9. 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.
    10. Philippe Jehiel, 2022. "Analogy-Based Expectation Equilibrium and Related Concepts:Theory, Applications, and Beyond," Working Papers halshs-03735680, HAL.
    11. Benaïm, Michel & Hofbauer, Josef & Hopkins, Ed, 2009. "Learning in games with unstable equilibria," Journal of Economic Theory, Elsevier, vol. 144(4), pages 1694-1709, July.
    12. Erhao Xie, 2019. "Monetary Payoff and Utility Function in Adaptive Learning Models," Staff Working Papers 19-50, Bank of Canada.
    13. Robin Nicole & Aleksandra Alori'c & Peter Sollich, 2020. "Fragmentation in trader preferences among multiple markets: Market coexistence versus single market dominance," Papers 2012.04103, arXiv.org, revised Aug 2021.
    14. Sergiu Hart, 2013. "Adaptive Heuristics," World Scientific Book Chapters, in: Simple Adaptive Strategies From Regret-Matching to Uncoupled Dynamics, chapter 11, pages 253-287, World Scientific Publishing Co. Pte. Ltd..
    15. Jean-Luc Gaffard & Mauro Napoletano, 2012. "Agent-based models and economic policy," Post-Print hal-03461120, HAL.
    16. Assenza, T. & Heemeijer, P. & Hommes, C.H. & Massaro, D., 2021. "Managing self-organization of expectations through monetary policy: A macro experiment," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 170-186.
    17. Colin Camerer & Teck Ho & Kuan Chong, 2003. "Models of Thinking, Learning, and Teaching in Games," American Economic Review, American Economic Association, vol. 93(2), pages 192-195, May.
    18. Sergiu Hart & Andreu Mas-Colell, 2013. "A Simple Adaptive Procedure Leading To Correlated Equilibrium," World Scientific Book Chapters, in: Simple Adaptive Strategies From Regret-Matching to Uncoupled Dynamics, chapter 2, pages 17-46, World Scientific Publishing Co. Pte. Ltd..
    19. Marco LiCalzi & Roland Mühlenbernd, 2022. "Feature-weighted categorized play across symmetric games," Experimental Economics, Springer;Economic Science Association, vol. 25(3), pages 1052-1078, June.
    20. Ilya R. P. Cuypers & Youtha Cuypers & Xavier Martin, 2017. "When the target may know better: Effects of experience and information asymmetries on value from mergers and acquisitions," Strategic Management Journal, Wiley Blackwell, vol. 38(3), pages 609-625, March.

    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:gam:jadmsc:v:3:y:2013:i:3:p:53-75:d:27180. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.