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Cournot games with linear regression expectations in oligopolistic markets

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  • Kamalinejad, Howra
  • Majd, Vahid Johari
  • Kebriaei, Hamed
  • Rahimi-Kian, Ashkan

Abstract

In this paper, a Cournot game in an oligopolistic market with incomplete information is considered. The market consists of some producers that compete for getting higher payoffs. For optimal decision making, each player needs to estimate its rivals’ behaviors. This estimation is carried out using linear regression and recursive weighted least-squares method. As the information of each player about its rivals increases during the game, its estimation of their reaction functions becomes more accurate. Here, it is shown that by choosing appropriate regressors for estimating the strategies of other players at each time-step of the market and using them for making the next step decision, the game will converge to its Nash equilibrium point. The simulation results for an oligopolistic market show the effectiveness of the proposed method.

Suggested Citation

  • Kamalinejad, Howra & Majd, Vahid Johari & Kebriaei, Hamed & Rahimi-Kian, Ashkan, 2010. "Cournot games with linear regression expectations in oligopolistic markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(9), pages 1874-1885.
    • Handle: RePEc:eee:matcom:v:80:y:2010:i:9:p:1874-1885
    DOI: 10.1016/j.matcom.2010.02.002
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    References listed on IDEAS

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    Cited by:

    1. Gao, Xing & Zhong, Weijun & Mei, Shue, 2012. "Equilibrium stability of a nonlinear heterogeneous duopoly game with extrapolative foresight," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(11), pages 2069-2078.
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    3. Junhai Ma & Zhanbing Guo, 2017. "Implications for Firms with Limited Information to Take Advantage of Reference Price Effect in Competitive Settings," Complexity, Hindawi, vol. 2017, pages 1-16, June.
    4. Gao, Xing & Zhong, Weijun & Mei, Shue, 2012. "On local stability of Cournot models with simultaneous and sequential decisions," Mathematical Social Sciences, Elsevier, vol. 63(3), pages 207-212.

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