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Distributed Welfare Games

Author

Listed:
  • Jason R. Marden

    (Department of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, Colorado 80309)

  • Adam Wierman

    (Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California 91125)

Abstract

Game-theoretic tools are becoming a popular design choice for distributed resource allocation algorithms. A central component of this design choice is the assignment of utility functions to the individual agents. The goal is to assign each agent an admissible utility function such that the resulting game possesses a host of desirable properties, including scalability, tractability, and existence and efficiency of pure Nash equilibria. In this paper we formally study this question of utility design on a class of games termed distributed welfare games. We identify several utility design methodologies that guarantee desirable game properties irrespective of the specific application domain. Lastly, we illustrate the results in this paper on two commonly studied classes of resource allocation problems: “coverage” problems and “coloring” problems.

Suggested Citation

  • Jason R. Marden & Adam Wierman, 2013. "Distributed Welfare Games," Operations Research, INFORMS, vol. 61(1), pages 155-168, February.
    • Handle: RePEc:inm:oropre:v:61:y:2013:i:1:p:155-168
    DOI: 10.1287/opre.1120.1137
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    References listed on IDEAS

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    Cited by:

    1. Charlotte Roman & Paolo Turrini, 2023. "Fighting for Routes: Resource Allocation among Competing Planners in Transportation Networks," Games, MDPI, vol. 14(3), pages 1-19, April.
    2. Marden, Jason R. & Shamma, Jeff S., 2015. "Game Theory and Distributed Control****Supported AFOSR/MURI projects #FA9550-09-1-0538 and #FA9530-12-1-0359 and ONR projects #N00014-09-1-0751 and #N0014-12-1-0643," Handbook of Game Theory with Economic Applications,, Elsevier.
    3. Zhao, Rui & Zhou, Xiao & Han, Jiaojie & Liu, Chengliang, 2016. "For the sustainable performance of the carbon reduction labeling policies under an evolutionary game simulation," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 262-274.
    4. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
    5. Key, Peter & Steinberg, Richard, 2020. "Pricing, competition and content for internet service providers," LSE Research Online Documents on Economics 107008, London School of Economics and Political Science, LSE Library.
    6. Alexandre Belloni & Changrong Deng & Saša Pekeč, 2017. "Mechanism and Network Design with Private Negative Externalities," Operations Research, INFORMS, vol. 65(3), pages 577-594, June.
    7. Vasilis Gkatzelis & Konstantinos Kollias & Tim Roughgarden, 2016. "Optimal Cost-Sharing in General Resource Selection Games," Operations Research, INFORMS, vol. 64(6), pages 1230-1238, December.

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