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Trade-In Programs in the Context of Technological Innovation with Herding

In: Advances in Artificial Economics

Author

Listed:
  • Paolo Pellizzari

    (Ca’ Foscari University)

  • Elena Sartori

    (University of Padua)

  • Marco Tolotti

    (Ca’ Foscari University)

Abstract

We study optimal pricing strategies and consequent market shares’ dynamics in a transition from an old and established technology to a new one. We simulate an agent-based model, in which a large population of possible buyers decide whether to adopt or not depending on prices, private signals and herding behavior. The firm, on its part, sets prices to maximize revenues. We show that trade-in programs, in practice comparable to very aggressive discounts, are supported by a rational attitude.

Suggested Citation

  • Paolo Pellizzari & Elena Sartori & Marco Tolotti, 2015. "Trade-In Programs in the Context of Technological Innovation with Herding," Lecture Notes in Economics and Mathematical Systems, in: Frédéric Amblard & Francisco J. Miguel & Adrien Blanchet & Benoit Gaudou (ed.), Advances in Artificial Economics, edition 127, pages 219-230, Springer.
    • Handle: RePEc:spr:lnechp:978-3-319-09578-3_18
    DOI: 10.1007/978-3-319-09578-3_18
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    References listed on IDEAS

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    1. William A. Brock & Steven N. Durlauf, 2001. "Discrete Choice with Social Interactions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 68(2), pages 235-260.
    2. 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.
    3. Jean-Pierre Nadal & Denis Phan & Mirta Gordon & Jean Vannimenus, 2005. "Multiple equilibria in a monopoly market with heterogeneous agents and externalities," Quantitative Finance, Taylor & Francis Journals, vol. 5(6), pages 557-568.
    4. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    5. Dawid, Herbert, 2006. "Agent-based Models of Innovation and Technological Change," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 25, pages 1235-1272, Elsevier.
    6. Ron Adner & Daniel Levinthal, 2001. "Demand Heterogeneity and Technology Evolution: Implications for Product and Process Innovation," Management Science, INFORMS, vol. 47(5), pages 611-628, May.
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    Cited by:

    1. Pierfrancesco Dotta & Marco Tolotti & Jorge Yepez, 2017. "Measuring Brand Awareness In A Random Utility Model," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 20(02n03), pages 1-11, March.
    2. Paolo Pellizzari & Elena Sartori & Marco Tolotti, 2015. "Optimal Policies In Two-Step Binary Games Under Social Pressure And Limited Resources," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 18(05n06), pages 1-16, August.

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    More about this item

    Keywords

    Market Share; Optimal Price; Private Signal; Potential Adopter; Price Schedule;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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