Skip to main content
Log in

Distributed computation of Pareto solutions inn-player games

  • Published:
Mathematical Programming Submit manuscript

Abstract

The problem of computing Pareto optimal solutions with distributed algorithms is considered inn-player games. We shall first formulate a new geometric problem for finding Pareto solutions. It involves solving joint tangents for the players' objective functions. This problem can then be solved with distributed iterative methods, and two such methods are presented. The principal results are related to the analysis of the geometric problem. We give conditions under which its solutions are Pareto optimal, characterize the solutions, and prove an existence theorem. There are two important reasons for the interest in distributed algorithms. First, they can carry computational advantages over centralized schemes. Second, they can be used in situations where the players do not know each others' objective functions.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. T. Başar, Relaxation techniques and asynchronous algorithms for on-line computation of non-cooperative equilibria,Journal of Economic Dynamics and Control 11 (1987) 531–549.

    Article  MathSciNet  Google Scholar 

  2. T. Başar, Distributed relaxation-type algorithms for noncooperative games, in:Proceedings of the IFAC Symposium on Dynamic Modelling and Control of National Economies 1, Edinburgh, UK (1989) 111–116.

  3. T. Başar and S. Li, Distributed computation of Nash equilibria in linear-quadratic stochastic differential games,SIAM Journal on Control and Optimization 27 (1989) 563–578.

    Article  MathSciNet  Google Scholar 

  4. M.S. Bazaraa and C.M. Shetty,Nonlinear Programming (John Wiley, New York, 1979).

    MATH  Google Scholar 

  5. D.P. Bertsekas and J.N. Tsitsiklis,Parallel and Distributed Computation (Prentice-Hall, Englewood Cliffs, NJ, 1989).

    MATH  Google Scholar 

  6. A.F. Daughety, ed.,Cournot Oligopoly, (Cambridge University Press, Cambridge, MA, 1988).

    MATH  Google Scholar 

  7. H. Ehtamo, M. Verkama and R.P. Hämäläinen. On distributed computation of Pareto solutions for two decision makers.IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 26(4) (1996).

  8. J.W. Friedman,Game Theory with Applications to Economics, 2nd ed. (Oxford University Press, New York, 1991).

    Google Scholar 

  9. P.T. Harker, A variational inequality approach for the determination of oligopolistic market equilibrium,Mathematical Programming 30 (1984) 105–111.

    MATH  MathSciNet  Google Scholar 

  10. C.-L. Hwang and A.S.M. Masud,Multiple Objective Decision Making, Methods and Applications (Springer-Verlag, Berlin, 1979).

    MATH  Google Scholar 

  11. S. Kakutani, A generalization of Brouwer's fixed point theorem,Duke Mathematical Journal 8 (1941) 457–459.

    Article  MATH  MathSciNet  Google Scholar 

  12. E. Kalai and E. Lehrer, Rational learning leads to Nash equilibrium,Econometrica 61 (1993) 1019–1045.

    Article  MATH  MathSciNet  Google Scholar 

  13. S. Li and T. Başar, Distributed algorithms for the computation of noncooperative equilibria,Automatica 23 (1987) 523–533.

    Article  MATH  Google Scholar 

  14. F.H. Murphy, H.D. Sherali and A.L. Soyster, A mathematical programming approach for determining oligopolistic market equilibrium,Mathematical Programming 24 (1982) 92–106.

    Article  MATH  MathSciNet  Google Scholar 

  15. J.M. Ortega and W.C. Rheinboldt,Iterative Solution of Nonlinear Equations in Several Variables (Academic Press, New York, 1970).

    MATH  Google Scholar 

  16. D.K. Osborne, Cartel problems,American Economic Review 66 (1976) 835–844.

    Google Scholar 

  17. G.P. Papavassilopoulos, Iterative techniques for the Nash solution in quadratic games with unknown parrameters,SIAM Journal on Control and Optimization 24 (1986) 821–834.

    Article  MATH  MathSciNet  Google Scholar 

  18. J. Ruusunen, H. Ehtamo and R.P. Hämäläinen, Dynamic cooperative electricity exchange in a power pool,IEEE Transactions on Systems, Man, and Cybernetics 21 (1991) 758–766.

    Article  Google Scholar 

  19. Y. Sawaragi, H. Nakayama and T. Tanino,Theory of Multiobjective Optimization (Academic Press, Orlando, FL, 1985).

    MATH  Google Scholar 

  20. J. Teich, Decision support for negotiations, Ph.D. Dissertation, State University of New York (Buffalo, 1991).

    Google Scholar 

  21. J. Teich and S. Zionts, The resource allocation multiple objective negotiation approach (RAMONA), unpublished manuscript, New Mexico State University (Las Cruces, 1993).

    Google Scholar 

  22. P.-L. Yu,Multiple-Criteria Decision Making (Plenum Press, New York, 1985).

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Markku Verkama.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Verkama, M., Ehtamo, H. & Hämäläinen, R.P. Distributed computation of Pareto solutions inn-player games. Mathematical Programming 74, 29–45 (1996). https://doi.org/10.1007/BF02592144

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02592144

Keywords