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Reflexivity in a Diffusion of Innovations Model

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Reflexive phenomena are usually understood in the social sciences as processes that affect themselves recursively. This stems from the mutual altering relationship between participants and the social process they belong to: participants can change the course of the process with their actions and a new state during the evolution of the process can lead to a change in its participants' behavior. This article proposes an agent-based model of diffusion of innovations in a social network to study reflexivity. In this model, agents decide to adopt a new product according to a utility function that depends on two kinds of social influences. First, there is a local influence exerted on an agent by her closest neighbors that have already adopted, and also by herself if she feels the product suits her personal needs. Second, there is a global influence which leads agents to adopt when they become aware of emerging trends happening in the system. For this, we endow agents with a reflexive capacity that allows them to recognize a trend, even if they can not perceive a significant change in their neighborhood. Results reveal the appearance of slowdown periods along the adoption rate curve, in contrast with the classic stylized bell-shaped behavior. Results also show that network structure plays an important role in the effect of reflexivity: while some structures (e.g., scale-free networks) may amplify it, others (e.g., small-world structure) weaken such an effect. The contribution of this work lies in the inclusion of evolving cognitive distinctions as agents decide product adoption in diffusion processes.

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  • Carlos Córdoba & César García-Díaz, 2020. "Reflexivity in a Diffusion of Innovations Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(3), pages 1-9.
  • Handle: RePEc:jas:jasssj:2018-107-3
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    1. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    2. Arthur, W Brian, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, American Economic Association, vol. 84(2), pages 406-411, May.
    3. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    4. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    5. Stremersch, S. & Tellis, G.J. & Franses, Ph.H.B.F. & Binken, J.L.G., 2007. "Indirect Network Effects in New Product Growth," ERIM Report Series Research in Management ERS-2007-019-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    6. W. Brian Arthur & Paul Tayler, "undated". "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Computing in Economics and Finance 1997 57, Society for Computational Economics.
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