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Heterogeneous Learning in Bertrand Competition with Differentiated Goods

  • Dávid Kopányi
Chapter
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 662)

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.

Keywords

Nash Equilibrium Learning Method Price Vector Nash Equilibrium Point Bertrand Competition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.CeNDEF, University of AmsterdamAmsterdamThe Netherlands

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