Cournot games with linear regression expectations in oligopolistic markets

Authors:
Howra Kamalinejad;Vahid Johari Majd;Hamed Kebriaei;Ashkan Rahimi-Kian
Affiliations:
Intelligent Control Systems Laboratory, Electrical Engineering School, Tarbiat Modares University, Tehran, Iran;Intelligent Control Systems Laboratory, Electrical Engineering School, Tarbiat Modares University, Tehran, Iran;Control and Intelligent Processing Center of Excellence, School of ECE, University of Tehran, Tehran, Iran;Control and Intelligent Processing Center of Excellence, School of ECE, University of Tehran, Tehran, Iran
Venue:
Mathematics and Computers in Simulation
Year:
2010

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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.