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Game Theory and Operations Research: Some Musings 50 Years Later

Author

Listed:
  • Martin Shubik

    (School of Management)

Abstract

A discussion of the utilization of the methods of game theory in operations research is given together with a consideration of the future developments. The language and analogies provided by game theory are now broadly accepted. It is suggested that the deep successes of game theory have illuminated its limitations and have helped to open up investigations in computer science, behavioral economics and simulation where the limitations of intelligence and of data processing are taken into account.

Suggested Citation

  • Martin Shubik, 2001. "Game Theory and Operations Research: Some Musings 50 Years Later," Yale School of Management Working Papers ysm191, Yale School of Management.
    • Handle: RePEc:ysm:somwrk:ysm191
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    Cited by:

    1. Wynn C Stirling & Teppo Felin, 2013. "Game Theory, Conditional Preferences, and Social Influence," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-11, February.
    2. Theodore T. Allen & Olivia K. Hernand & Abdullah Alomair, 2020. "Optimal Off-line Experimentation for Games," Decision Analysis, INFORMS, vol. 17(4), pages 277-298, December.
    3. Ruttan, Vernon W., 2007. "Imperialism, Colonialism and Collaboration in the Social Sciences," Staff Papers 7356, University of Minnesota, Department of Applied Economics.
    4. 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.
    5. Nan Zhang & Heng Xu, 2024. "Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning," Information Systems Research, INFORMS, vol. 35(2), pages 469-488, June.
    6. Tapiero, Charles S., 2007. "Consumers risk and quality control in a collaborative supply chain," European Journal of Operational Research, Elsevier, vol. 182(2), pages 683-694, October.
    7. Ensthaler, Ludwig & Giebe, Thomas, 2014. "Bayesian optimal knapsack procurement," European Journal of Operational Research, Elsevier, vol. 234(3), pages 774-779.
    8. Wenke Wang & Xiaoqiong You & Kebei Liu & Yenchun Jim Wu & Daming You, 2020. "Implementation of a Multi-Agent Carbon Emission Reduction Strategy under the Chinese Dual Governance System: An Evolutionary Game Theoretical Approach," IJERPH, MDPI, vol. 17(22), pages 1-21, November.
    9. Thomas Kamalakis & Georgia Dede & Christos Michalakelis & Mara Nikolaidou, 2024. "Game-theoretic analysis of competition between access service providers utilizing a Nash genetic algorithm," Operational Research, Springer, vol. 24(2), pages 1-30, June.
    10. Toraubally, Waseem A., 2023. "Comparative advantage with many goods: New treatment and results," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1188-1201.

    More about this item

    JEL classification:

    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory

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