IDEAS home Printed from https://ideas.repec.org/p/jhu/papers/529.html
   My bibliography  Save this paper

Innovators, Imitators, and the Evolving Architecture of Social Networks

Author

Listed:
  • Joseph E. Harrington, Jr

Abstract

Scientific progress is driven by innovation ?which serves to produce a diversity of ideas ?and imitation through a social network ?which serves to diffuse these ideas. In this paper, we develop an agent-based computational model of this process, in which the agents in the population are heterogeneous in their abilities to innovate and imitate. The model incorporates three primary forces ?the discovery of new ideas by those with superior abilities to innovate, the observation and adoption of these ideas by those with superior abilities to communicate and imitate, and the endogenous development of social networks among heterogeneous agents. The objective is to explore the evolving architecture of social networks and the critical roles that the innovators and imitators play in the process. A central finding is that the emergent social network takes a chainstructure with the innovators as the main source of ideas and the imitators as the connectors between the innovators and the masses. The impact of agent heterogeneity and environmental volatility on the network architecture is also characterized.

Suggested Citation

  • 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.
    • Handle: RePEc:jhu:papers:529
    as

    Download full text from publisher

    File URL: http://www.econ2.jhu.edu/REPEC/papers/wp529harrington.pdf
    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.
    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. Shelley D. Dionne & Hiroki Sayama & Francis J. Yammarino, 2019. "Diversity and Social Network Structure in Collective Decision Making: Evolutionary Perspectives with Agent-Based Simulations," Complexity, Hindawi, vol. 2019, pages 1-16, March.
    2. David Goldbaum, 2008. "Follow the Leader: Simulations on a Dynamic Social Network," Working Paper Series 155, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    3. David Goldbaum, 2009. "Follow the Leader: Steady State Analysis of a Dynamic Social Network," Working Paper Series 158, Finance Discipline Group, UTS Business School, University of Technology, Sydney.

    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. 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.
    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," Sciences Po publications info:hdl:2441/53r60a8s3ku, Sciences Po.
    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.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:jhu:papers:529. 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: Humphrey Muturi (email available below). General contact details of provider: https://edirc.repec.org/data/dejhuus.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.