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Title: Regularized two-stage stochastic variational inequalities for Cournot-Nash equilibrium under uncertainty
Authors: Jiang, J 
Shi, Y 
Wang, X 
Chen, X 
Issue Date: 2019
Source: Journal of computational mathematics, 2019, v. 37, no. 6, p. 813-842
Abstract: A convex two-stage non-cooperative multi-agent game under uncertainty is formulated as a two-stage stochastic variational inequality (SVI). Under standard assumptions, we provide sufficient conditions for the existence of solutions of the two-stage SVI and propose a regularized sample average approximation method for solving it. We prove the convergence of the method as the regularization parameter tends to zero and the sample size tends to infinity. Moreover, our approach is applied to a two-stage stochastic production and supply planning problem with homogeneous commodity in an oligopolistic market. Numerical results based on historical data in crude oil market are presented to demonstrate the effectiveness of the two-stage SVI in describing the market share of oil producing agents.
Keywords: Two-stage stochastic variational inequalities
Cournot-Nash equilibrium
Regularized method
Progressive hedging method
Uncertainty
Oil market share
Publisher: Global Science Press
Journal: Journal of computational mathematics 
ISSN: 0254-9409
EISSN: 1991-7139
DOI: 10.4208/jcm.1906-m2019-0025
Rights: © Global Science Press
This is the accepted version of the following article: Jie Jiang, Yun Shi, Xiaozhou Wang & Xiaojun Chen. (2019). Regularized Two-Stage Stochastic Variational Inequalities for Cournot-Nash Equilibrium Under Uncertainty. Journal of Computational Mathematics, 37(6), 813-842, which has been published in https://doi.org/10.4208/jcm.1906-m2019-0025.
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