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Heterogeneous Learning in Bertrand Competition with Differentiated Goods

In: Managing Market Complexity

Author

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  • Dávid Kopányi

    (CeNDEF, University of Amsterdam)

Abstract

This paper stresses that the coexistence of different learning methods can have a substantial effect on the convergence properties of these methods. We consider a Bertrand oligopoly with differentiated goods in which firms either use least squares learning or gradient learning for determining the price for a given period. These methods are well-established in oligopoly models but, up till now, are used mainly in homogeneous setups. We illustrate that the stability of gradient learning depends on the distribution of learning methods over firms: as the number of gradient learners increases, the method may lose stability and become less profitable. We introduce competition between the learning methods and show that a cyclical switching between the methods may occur.

Suggested Citation

  • Dávid Kopányi, 2012. "Heterogeneous Learning in Bertrand Competition with Differentiated Goods," Lecture Notes in Economics and Mathematical Systems, in: Andrea Teglio & Simone Alfarano & Eva Camacho-Cuena & Miguel Ginés-Vilar (ed.), Managing Market Complexity, edition 127, chapter 0, pages 155-166, Springer.
    • Handle: RePEc:spr:lnechp:978-3-642-31301-1_13
    DOI: 10.1007/978-3-642-31301-1_13
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