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Perfectly competitive capacity expansion games with risk-averse participants

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Abstract

This paper presents Nash equilibrium models of perfectly competitive capacity expansion involving risk-averse participants in the presence of state uncertainty and pricing mechanisms. Existence of solutions to such models is established based on the nonlinear complementarity formulations of the models to which a general existence result is applicable. This study extends two recent papers (Fan et al. in J Environ Econ Manag 60:193–208, 2010; Zhao et al. in Oper Res 58:529–548, 2010) pertaining to special cases of our models, complements the extensive work on games with strategic players, and provides an extended treatment of games with price-taking players whose feasible sets may be unbounded. The latter aspect generalizes much of the classical analysis of such models for which price boundedness and feasible region compactness are essential assumptions needed for a fixed-point existence proof.

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Acknowledgments

The work of Dane A. Schiro and Jong-Shi Pang was based on research partially supported by the U.S. National Science Foundation Grant CMMI 0969600. The work of Benjamin F. Hobbs was supported by the U.S. National Science Foundation Grants OISE 1243482 and ECCS 1230788.

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Author notes

  1. Dane A. Schiro

    Present address: ISO New England, Holyoke, MA, 01040, USA

  2. Jong-Shi Pang

    Present address: Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, 90089-0193, USA

Authors and Affiliations

  1. Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Dane A. Schiro & Jong-Shi Pang

  2. Department of Geography and Environmental Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA

    Benjamin F. Hobbs

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  1. Dane A. Schiro
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Correspondence to Jong-Shi Pang.

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Schiro, D.A., Hobbs, B.F. & Pang, JS. Perfectly competitive capacity expansion games with risk-averse participants. Comput Optim Appl 65, 511–539 (2016). https://doi.org/10.1007/s10589-015-9798-5

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