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Using Associative Networks To Represent Adopters' Beliefs In A Multiagent Model Of Innovation Diffusion

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  • SAMUEL THIRIOT

    (Computer Science Laboratory (LIP6), University Pierre et Marie Curie — Paris 6, 104 avenue du Président Kennedy, 75016 Paris, France)

  • JEAN-DANIEL KANT

    (Computer Science Laboratory (LIP6), University Pierre et Marie Curie — Paris 6, 104 avenue du Président Kennedy, 75016 Paris, France)

Abstract

A lot of agent-based models were built to study diffusion of innovations. In most of these models, beliefs of individuals about the innovation were not represented at all, or in a highly simplified way. In this paper, we argue that representing beliefs could help to tackle problematics identified for diffusion of innovations, like misunderstanding of information, which can lead to diffusion failure, or diffusion of linked inventions. We propose a formalization of beliefs and messages as associative networks. This representation allows one to study the social representations of innovations and to validate diffusion models against real data. It could also make models usable to analyze diffusion prior to the product launch. Our approach is illustrated by a simulation of iPod™ diffusion.

Suggested Citation

  • Samuel Thiriot & Jean-Daniel Kant, 2008. "Using Associative Networks To Represent Adopters' Beliefs In A Multiagent Model Of Innovation Diffusion," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 261-272.
    • Handle: RePEc:wsi:acsxxx:v:11:y:2008:i:02:n:s0219525908001611
    DOI: 10.1142/S0219525908001611
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    Citations

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    Cited by:

    1. Shane T. Mueller & Yin-Yin Sarah Tan, 2018. "Cognitive perspectives on opinion dynamics: the role of knowledge in consensus formation, opinion divergence, and group polarization," Journal of Computational Social Science, Springer, vol. 1(1), pages 15-48, January.
    2. Sabrina Backs & Markus Günther & Christian Stummer, 2019. "Stimulating academic patenting in a university ecosystem: an agent-based simulation approach," The Journal of Technology Transfer, Springer, vol. 44(2), pages 434-461, April.
    3. Martin Neumann & Andreas Braun & Eva-Maria Heinke & Mehdi Saqalli & Armano Srbljinovic, 2011. "Challenges in Modelling Social Conflicts: Grappling with Polysemy," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 14(3), pages 1-9.
    4. 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.
    5. Konstantinos Petridis & Nikolaos E. Petridis, 2022. "Diffusion of Innovations in Middle Eastern versus Western Markets: A Mathematical Computation Cellular Automata Simulation Model," Operational Research, Springer, vol. 22(2), pages 1597-1616, April.
    6. Carlos M. Fernández‐Márquez & Francisco J. Vázquez, 2018. "How information and communication technology affects decision‐making on innovation diffusion: An agent‐based modelling approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 25(3), pages 124-133, July.

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