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Innovate or imitate? Behavioural Technological Change

Author

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  • Cars Hommes

    (CeNDEF, University of Amsterdam)

  • Paolo Zeppini

    (School of Innovation Sciences, Eindhoven University of Technology)

Abstract

This discussion paper led to a publication in the 'Journal of Economic Dynamics and Control' , 2014, 48, 308–324. We propose a behavioural model of technological change with evolutionary switching between boundedly rational costly innovators and free imitators, and study the endogenous interplay of innovation decisions, market price dynamics and technological progress. Innovation and imitation are strategic substitutes and exhibit negative feedback. Endogenous technological change is the cumulative outcome of innovation decisions. There are three scenarios: market breakdown, Schumpeterian rents and learning curves. The latter is characterized by an increasing fraction of innovators when demand is elastic, while inelastic demand allows technological progress with shrinking innovation effort. Model simulations are compared to empirical data of two industrial sectors.

Suggested Citation

  • Cars Hommes & Paolo Zeppini, 2013. "Innovate or imitate? Behavioural Technological Change," Tinbergen Institute Discussion Papers 13-099/II, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20130099
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    Cited by:

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    2. Agliari, Anna & Naimzada, Ahmad & Pecora, Nicolò, 2017. "Dynamic effects of memory in a cobweb model with competing technologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 340-350.
    3. Shu-Heng Chen & Bin-Tzong Chie & Ying-Fang Kao & Ragupathy Venkatachalam, 2019. "Agent-Based Modeling of a Non-tâtonnement Process for the Scarf Economy: The Role of Learning," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 305-341, June.
    4. Mikhail Anufriev & Davide Radi & Fabio Tramontana, 2018. "Some reflections on past and future of nonlinear dynamics in economics and finance," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 41(2), pages 91-118, November.
    5. Duffy, John & Ralston, Jason, 2020. "Innovate versus imitate: Theory and experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 727-751.
    6. Schmitt, Noemi & Tuinstra, Jan & Westerhoff, Frank, 2017. "Side effects of nonlinear profit taxes in an evolutionary market entry model: Abrupt changes, coexisting attractors and hysteresis problems," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 15-38.
    7. Fabio Lamantia & Anghel Negriu & Jan Tuinstra, 2018. "Technology choice in an evolutionary oligopoly game," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 41(2), pages 335-356, November.
    8. Zeppini, Paolo, 2015. "A discrete choice model of transitions to sustainable technologies," Journal of Economic Behavior & Organization, Elsevier, vol. 112(C), pages 187-203.
    9. Ahmad Naimzada & Nicolò Pecora & Fabio Tramontana, 2019. "A cobweb model with elements from prospect theory," Journal of Evolutionary Economics, Springer, vol. 29(2), pages 763-778, April.
    10. Matteo G. Richiardi, 2017. "The Future of Agent-Based Modeling," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(2), pages 271-287, March.
    11. Paolo Zeppini, 2014. "A discrete choice model of transitions to sustainable technologies: speed limits and optimal monetary policies," Department of Economics Working Papers 28/14, University of Bath, Department of Economics.
    12. Lamantia, F. & Negriu, A. & Tuinstra, J., 2016. "Evolutionary Cournot competition with endogenous technology choice: (in)stability and optimal policy," CeNDEF Working Papers 16-08, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.

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    More about this item

    Keywords

    discrete choice; innovation patterns; learning curves; switching behavior;
    All these keywords.

    JEL classification:

    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • 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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