Skip to main content
Log in

Two-stage non-cooperative games with risk-averse players

  • Full Length Paper
  • Series B
  • Published:
Mathematical Programming Submit manuscript

Abstract

This paper formally introduces and studies a non-cooperative multi-agent game under uncertainty. The well-known Nash equilibrium is employed as the solution concept of the game. While there are several formulations of a stochastic Nash equilibrium problem, we focus mainly on a two-stage setting of the game wherein each agent is risk-averse and solves a rival-parameterized stochastic program with quadratic recourse. In such a game, each agent takes deterministic actions in the first stage and recourse decisions in the second stage after the uncertainty is realized. Each agent’s overall objective consists of a deterministic first-stage component plus a second-stage mean-risk component defined by a coherent risk measure describing the agent’s risk aversion. We direct our analysis towards a broad class of quantile-based risk measures and linear-quadratic recourse functions. For this class of non-cooperative games under uncertainty, the agents’ objective functions can be shown to be convex in their own decision variables, provided that the deterministic component of these functions have the same convexity property. Nevertheless, due to the non-differentiability of the recourse functions, the agents’ objective functions are at best directionally differentiable. Such non-differentiability creates multiple challenges for the analysis and solution of the game, two principal ones being: (1) a stochastic multi-valued variational inequality is needed to characterize a Nash equilibrium, provided that the players’ optimization problems are convex; (2) one needs to be careful in the design of algorithms that require differentiability of the objectives. Moreover, the resulting (multi-valued) variational formulation cannot be expected to be of the monotone type in general. The main contributions of this paper are as follows: (a) Prior to addressing the main problem of the paper, we summarize several approaches that have existed in the literature to deal with uncertainty in a non-cooperative game. (b) We introduce a unified formulation of the two-stage SNEP with risk-averse players and convex quadratic recourse functions and highlight the technical challenges in dealing with this game. (c) To handle the lack of smoothness, we propose smoothing schemes and regularization that lead to differentiable approximations. (d) To deal with non-monotonicity, we impose a generalized diagonal dominance condition on the players’ smoothed objective functions that facilitates the application and ensures the convergence of an iterative best-response scheme. (e) To handle the expectation operator, we rely on known methods in stochastic programming that include sampling and approximation. (f) We provide convergence results for various versions of the best-response scheme, particularly for the case of private recourse functions. Overall, this paper lays the foundation for future research into the class of SNEPs that provides a constructive paradigm for modeling and solving competitive decision making problems with risk-averse players facing uncertainty; this paradigm is very much at an infancy stage of research and requires extensive treatment in order to meet its broad applications in many engineering and economics domains.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  1. Aghassi, M., Bertsimas, D.: Robust game theory. Math. Program. 107, 231–273 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  2. Bensoussan, A.: Points de Nash dans le cas de fontionnelles quadratiques et jeux differentiels linèaires a \(N\) personnes. SIAM J. Control 12, 460–499 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  3. Ben-Tal, A., El Ghaoui, L., Nemirovsky, A.: Robust Optimization. Princeton University Press, Princeton (2009)

    Book  Google Scholar 

  4. Birge, J.R., Louveaux, F.: Introduction to Stochastic Programming. Springer Series in Operations Research. Springer, New York (1997)

    MATH  Google Scholar 

  5. Birge, J.R., Pollock, S.M., Qi, L.: A quadratic recourse function for the two-stage stochastic program. In: Yang, X., Mees, A.I., Fisher, M., Jennings, L. (eds.) Progress in Optimization. Applied Optimization, Kluwer Academic Publishers, Netherlands vol. 39, pp. 109–121 (2000)

  6. Bonnans, J.F., Shapiro, A.: Perturbation Analysis of Optimization Problems. Springer, New York (2000)

    Book  MATH  Google Scholar 

  7. Bunn, D., Oliveira, F.: Modeling the impact of market interventions on the strategic evolution of electricity markets. Oper. Res. 56, 1116–1130 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  8. Chen, X., Fukushima, M.: Expected residual minimization method for stochastic linear complementarity problems. Math. Oper. Res. 30, 1022–1038 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  9. Chen, X., Wets, R.J.B., Zhang, Y.: Stochastic variational inequalities: residual minimization smoothing/sample average approximations. SIAM J. Optim. 22, 649–673 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  10. Chen, X., Qi, L.Q., Womersley, R.S.: Newton’s method for quadratic stochastic programs with recourse. J. Comput. Appl. Math. 60(1995), 29–46 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  11. Chen, X., Womersley, R.S.: Random test problems and parallel methods for quadratic programs and quadratic stochastic programs. Optim. Methods Softw. 13, 275–306 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  12. Cottle, R.W., Pang, J.S., Stone, R.E.: The Linear Complementarity Problem. SIAM Classics in Applied Mathematics, vol. 60, Philadelphia (2009) [Originally published by Academic Press, Boston (1992)]

  13. Dantzig, G.B.: Linear programming under uncertainty. Manag. Sci. 1, 197–206 (1955)

    Article  MathSciNet  MATH  Google Scholar 

  14. Ehrenmann, A., Smeers, Y.: Generation capacity expansion in a risky environment: a stochastic equilibrium analysis. Oper. Res. 59, 1332–1346 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  15. Facchinei, F., Kanzow, C.: Generalized Nash equilibrium problems. Ann. Oper. Res. 175, 177–211 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  16. Facchinei, F., Pang, J.S.: Finite-Dimensional Variational Inequalities and Complementarity Problems. Springer, New York (2003)

    MATH  Google Scholar 

  17. Facchinei, F., Pang, J.S.: Nash equilibria: the variational approach. In: Eldar, Y., Palomar, D. (eds.) Convex Optimization in Signal Processing and Communications, pp. 443–493. Cambridge University Press, Cambridge (2009)

    Chapter  Google Scholar 

  18. Facchinei, F., Pang, J.S., Scutari, G.: Non-cooperative games with minmax objectives. Comput. Optim. Appl. 59, 85–112 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  19. Fang, H., Chen, X., Fukushima, M.: Stochastic \(\text{ R }_0\) matrix linear complementarity problems. SIAM J. Optim. 18, 482–506 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  20. Gabriel, S.A., Fuller, J.D.: A Benders decomposition method for solving stochastic complementarity problems with an application in energy. Comput. Econ. 35, 301–329 (2010)

    Article  MATH  Google Scholar 

  21. Genc, T.S., Reynolds, S.S., Sen, S.: Dynamic oligopolistic games under uncertainty: a stochastic programming approach. J. Econ. Dyn. Control 31, 55–80 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  22. Guddat, J.: Stability in convex quadratic parametric programming. Mathematische Operationsforschung und Statistik 7, 223–245 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  23. Gürkan, G., Ozdemir, O., Smeers, Y.: Generation capacity investments in electricity markets: perfect competition. CentER Discussion Paper, Vol. 2013–045. Tilburg: Econometrics

  24. Gürkan, G., Özge, A.Y., Robinson, S.M.: Sample-path solution of stochastic variational inequalities. Math. Program. 84, 313–333 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  25. Gürkan, G., Pang, J.S.: Approximations of Nash equilibria. Math. Program. Ser. B 117, 223–253 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  26. Harker, P.T., Pang, J.S.: Finite-dimensional variational inequality and nonlinear complementarity problems: a survey of theory, algorithms and applications. Math. Program. Ser. B 48, 161–220 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  27. Hayashi, S., Yamashita, N., Fukushima, M.: Robust Nash equilibria and second-order cone complementarity problems. J. Nonlinear Convex Anal. 6, 283–296 (2005)

    MathSciNet  MATH  Google Scholar 

  28. Higle, J., Sen, S.: Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming. Springer, New York (1996)

    Book  MATH  Google Scholar 

  29. Jiang, H., Xu, H.: Stochastic approximation approaches to the stochastic variational inequality problem. IEEE Trans. Autom. Control 53, 1462–1475 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  30. Jofré, A., Wets, R.J.B.: Variational convergence of bivariate functions: lopsided convergence. Math. Program. B 116, 275–295 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  31. Jofré, A., Wets, R.J.B.: Variational convergence of bivariate functions: motivating applications. SIAM J. Optim. 24, 1952–1979 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  32. Kall, P.: Stochastic Linear Programming. Springer, Berlin (1976)

    Book  MATH  Google Scholar 

  33. Kall, P., Mayer, J.: Stochastic Linear Programming. International Series in Operations Research & Management Science, vol. 156. Springer, New York (2011)

    Google Scholar 

  34. Kall, P., Wallace, S.W.: Stochastic Programming. Wiley, Chichester (1994)

    MATH  Google Scholar 

  35. Kannan, A., Shanbhag, U.V., Kim, H.M.: Strategic behavior in power markets under uncertainty. Energy Syst. 2, 115–141 (2011)

    Article  Google Scholar 

  36. Kannan, A., Shanbhag, U.V., Kim, H.M.: Addressing supply-side risk in uncertain power markets: stochastic Nash models, scalable algorithms and error analysis. Optim. Methods Softw. 28, 1095–1138 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  37. Koshal, J., Nedic, A., Shanbhag, U.V.: Regularized iterative stochastic approximation methods for stochastic variational inequality problems. IEEE Trans. Autom. Control 58, 594–609 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  38. Kulkarni, A.A., Shanbhag, U.V.: Recourse-based stochastic nonlinear programming: properties and Benders-SQP algorithms. Comput. Optim. Appl. 51, 77–123 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  39. King, A.J., Rockafellar, R.T.: Asymptotic theory for solutions in statistical estimation and stochastic programming. Math. Oper. Res. 18, 148–162 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  40. Lau, K.K., Womersley, R.S.: Multistage quadratic stochastic programming. J. Comput. Appl. Math. 129, 105–138 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  41. Lee, G.M., Tam, N.N., Yen, N.D.: Quadratic Programming and Affine Variational Inequalities: A Qualitative Study. Springer e-book, New York (2005)

    MATH  Google Scholar 

  42. Lin, G.H., Fukushima, M.: Stochastic equilibrium problems and stochastic mathematical programs with equilibrium constraints: a survey. Pac. J. Optim. 6, 455–482 (2010)

    MathSciNet  MATH  Google Scholar 

  43. Louveaux, F.V.: Piecewise convex programs. Math. Program. 15, 53–62 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  44. Luna, J.P., Sagastizábal, C., Solodov, M.: Complementarity and game-theoretical models for equilibria in energy markets: deterministic and risk-averse formulations. Chapter 10. In: Kovacevic, R., Pflug, G., Vespucci, M. (eds.) International Series in Operations Research and Management Science, vol. 199, pp. 237–264. Springer, Berlin (2014)

    Google Scholar 

  45. Luna, J.P., Sagastizábal, C., Solodov, M.: An approximation scheme for a class of risk-averse stochastic equilibrium problems. Math. Program. 157, 451–481 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  46. Meng, F., Sun, J., Goh, M.: A smoothing sample average approximation method for stochastic optimization problems with CVaR risk measure. Comput. Optim. Appl. 50, 379–401 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  47. Nash, J.F.: Equilibrium points in n-person games. Proc. Natl. Acad. Sci. 36, 48–49 (1950)

    Article  MathSciNet  MATH  Google Scholar 

  48. Nash, J.F.: Non-cooperative games. Ann. Math. 54, 286–295 (1951)

    Article  MathSciNet  MATH  Google Scholar 

  49. Nishimura, R., Hayashi, S., Fukushima, M.: Robust Nash equilibria in N-person noncooperative games: uniqueness and reformulation. Pac. J. Optim. 5, 237–259 (2009)

    MathSciNet  MATH  Google Scholar 

  50. Nishimura, R., Hayashi, S., Fukushima, M.: Semidefinite complementarity reformulation for robust Nash equilibrium problems with Euclidean uncertainty sets. J. Glob. Optim. 53, 107–120 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  51. Ogryczak, W., Ruszczyński, A.: Dual stochastic dominance and related mean-risk models. SIAM J. Optim. 13, 60–78 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  52. Pang, J.S., Fukushima, M.: Quasi-variational inequalities, generalized Nash equilibria, and multi-leader-follower games. Comput. Manag. Sci. 1, 21–56 (2005). (with erratum)

    Article  MathSciNet  MATH  Google Scholar 

  53. Polyak, B.T.: Introduction to Optimisation. Optimization Software Inc, New York (1987)

    Google Scholar 

  54. Ravat, U.: On the Analysis of Stochastic Optimization and Variational Inequality Problems. Ph.D. dissertation. Department of Industrial and Systems Engineering, University of Illinois at Urbana-Champaign (2014)

  55. Ravat, U., Shanbhag, U.V.: On the characterization of solution sets of smooth and nonsmooth convex stochastic Nash games. SIAM J. Optim. 21, 1168–1199 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  56. Rockafellar, R.T., Royset, J.O.: Measures of residual risk with connections to regression, risk tracking, surrogate models, and ambiguity. SIAM J. Optim. 25, 1179–1208 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  57. Rockafellar, R.T., Uryasev, S.: Optimization of conditional value-at-risk. J. Risk 2, 21–41 (2000)

    Article  Google Scholar 

  58. Rockafellar, R.T., Uryasev, S.: Conditional value-at-risk for general loss distributions. J. Bank. Financ. 7, 1143–1471 (2002)

    Google Scholar 

  59. Rockafellar, R.T., Uryasev, S., Zabarankin, M.: Optimality conditions in portfolio analysis with general deviation measures. Math. Program. Ser. B 108, 515–540 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  60. Rockafellar, R.T., Uryasev, S., Zabarankin, M.: Generalized deviations in risk analysis. Financ. Stoch. 10, 51–74 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  61. Rockafellar, R.T., Wets, R.J.-B.: Stochastic variational inequalities: single-stage to multistage. Math. Program. Ser. B (2016). doi:10.1007/s10107-016-0995-5

  62. Rockafellar, R.T., Wets, R.J.-B.: On the interchange of subdifferentiation and conditional expectation for convex functionals. Stochastics 7, 173–182 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  63. Ruszczynski, A., Shapiro, A. (eds.): Handbooks in Operations Research and Management Science: Stochastic Programming, vol. 10. Elsevier, Amsterdam (2003)

    Google Scholar 

  64. Ruszczynski, A., Shapiro, A.: Optimization of convex risk functions. Math. Oper. Res. 31, 433–452 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  65. Schiro, D.A., Hobbs, B.F., Pang, J.S.: Perfectly competitive capacity expansion games with risk-averse participants. Comput. Optim. Appl. 65, 511–539 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  66. Schiro, D.A., Pang, J.S., Shanbhag, U.V.: On the solution of affine generalized Nash equilibrium problems with shared constraints by Lemke’s method. Math. Program. Ser. A 146, 1–46 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  67. Scutari, G., Facchinei, F., Palomar, D.P., Pang, J.S.: Flexible design of cognitive radio wireless systems: from game theory to variational inequality theory. IEEE Signal Process. Mag. 26, 107–123 (2009)

    Article  Google Scholar 

  68. Shanbhag, U.V., Pang, J.S., Sen, S.: Inexact best-response schemes for stochastic Nash games: linear convergence and Iteration complexity analysis. In: Proceedings of the IEEE Conference on Decision and Control, pp. 3591–3596 (2016)

  69. Shanbhag, U.V.: Decomposition and Sampling Methods for Stochastic Equilibrium Problems. Ph.D. thesis. Department of Management Science and Engineering (Operations Research), Stanford University (2006)

  70. Shanbhag, U.V.: Stochastic Variational Inequality Problems: Applications, Analysis, and Algorithms. INFORMS Tutorials in Operations Research, pp. 71–107 (2013)

  71. Shapiro, A., Dentcheva, D., Ruszczynski, A.: Lectures on Stochastic Programming: Modeling and Theory. SIAM Publications, Philadelphia (2009)

    Book  MATH  Google Scholar 

  72. Walkup, D., Wets, R.: Stochastic program with recourse. SlAM Appl. Math. 15, 1299–1314 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  73. Wang, J.: Lipschitz continuity of objective functions in stochastic programs with fixed recourse and its applications. Math. Program. Study 27, 145–152 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  74. Wang, W.: Sample Average Approximation of Risk-averse Stochastic Programs. Ph.D. thesis. Department of Industrial and Systems Engineering. Georgia Institute of Technology (2007)

  75. Wets, R.J.B.: Programming under uncertainty: the equivalent convex program. SIAM J. Appl. Math. 14, 89–105 (1966)

    Article  MathSciNet  MATH  Google Scholar 

  76. Wets, R.J.B.: Characterization theorems for stochastic programs. Math. Program. 2, 166–175 (1972)

    Article  MathSciNet  MATH  Google Scholar 

  77. Yao, J., Adler, I., Oren, S.S.: Modeling and computing two-settlement oligopolistic equilibrium in a congested electricity network. Oper. Res. 56, 34–47 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  78. Yousefian, F., Nedich, A., Shanbhag, U.V.: Self-tuned stochastic approximation schemes for non-Lipschitzian stochastic multi-user optimization and Nash games. IEEE Trans. Autom. Control. 61, 1753–1766 (2016). doi:10.1109/TAC.2015.2478124

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Suvrajeet Sen.

Additional information

The work of Jong-Shi Pang and Suvrajeet Sen was based on research partially supported by the U.S. National Science Foundation Grant CMMI-1538605. In addition, the work of Pang was also based on research partially supported by the U.S. National Science Foundation Grant CMMI-1402052. The work of Uday V. Shanbhag was based on research partially supported by the U.S. National Science Foundation CAREER Award CMMI-1246887.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pang, JS., Sen, S. & Shanbhag, U.V. Two-stage non-cooperative games with risk-averse players. Math. Program. 165, 235–290 (2017). https://doi.org/10.1007/s10107-017-1148-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10107-017-1148-1

Mathematics Subject Classification

Navigation