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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
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    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.
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