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The Evolution of Price Competition Game on Complex Networks

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  • Feng Jie Xie
  • Jing Shi

Abstract

The well-known “Bertrand paradox” describes a price competition game in which two competing firms reach an outcome where both charge a price equal to the marginal cost. The fact that the Bertrand paradox often goes against empirical evidences has intrigued many researchers. In this work, we study the game from a new theoretical perspective—an evolutionary game on complex networks. Three classic network models, square lattice, WS small-world network, and BA scale-free network, are used to describe the competitive relations among the firms which are bounded rational. The analysis result shows that full price keeping is one of the evolutionary equilibriums in a well-mixed interaction situation. Detailed experiment results indicate that the price-keeping phenomenon emerges in a square lattice, small-world network and scale-free network much more frequently than in a complete network which represents the well-mixed interaction situation. While the square lattice has little advantage in achieving full price keeping, the small-world network and the scale-free network exhibit a stronger capability in full price keeping than the complete network. This means that a complex competitive relation is a crucial factor for maintaining the price in the real world. Moreover, competition scale, original price, degree of cutting price, and demand sensitivity to price show a significant influence on price evolution on a complex network. The payoff scheme, which describes how each firm’s payoff is calculated in each round game, only influences the price evolution on the scale-free network. These results provide new and important insights for understanding price competition in the real world.

Suggested Citation

  • Feng Jie Xie & Jing Shi, 2018. "The Evolution of Price Competition Game on Complex Networks," Complexity, Hindawi, vol. 2018, pages 1-13, July.
  • Handle: RePEc:hin:complx:9649863
    DOI: 10.1155/2018/9649863
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    Cited by:

    1. Wu, Bin & Cheng, Jing & Qi, Yuqing, 2020. "Tripartite evolutionary game analysis for “Deceive acquaintances” behavior of e-commerce platforms in cooperative supervision," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).

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