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Decision Making and Problem Solving

Author

Listed:
  • Herbert A. Simon

    (Carnegie-Mellon University, Pittsburgh, Pennsylvania)

  • George B. Dantzig

    (Stanford University, Stanford, California)

  • Robin Hogarth

    (University of Chicago, Chicago, Illinois)

  • Charles R. Plott

    (California Institute of Technology, Pasadena, California)

  • Howard Raiffa

    (Harvard Business School, Boston, Massachusetts)

  • Thomas C. Schelling

    (Harvard University, Cambridge, Massachusetts)

  • Kenneth A. Shepsle

    (Washington University, St Louis, Missouri)

  • Richard Thaler

    (Cornell University, Ithaca, New York)

  • Amos Tversky

    (Stanford University, Stanford, California)

  • Sidney Winter

    (Yale University, New Haven, Connecticut)

Abstract

The MS/OR community has, as its common mission, the development of tools and procedures to improve problem solving and decision making. This report discusses the advances needed to combine human thinking with intelligent machines to achieve a more productive society. Areas of high potential include research in expert systems, conflict resolution, agenda setting, decision making in an organizational setting, and empirical studies of individual behavior. The resources currently being applied to research in decision making and problem solving are modest and are not commensurate with the opportunities or the human resources available for exploiting them.

Suggested Citation

  • Herbert A. Simon & George B. Dantzig & Robin Hogarth & Charles R. Plott & Howard Raiffa & Thomas C. Schelling & Kenneth A. Shepsle & Richard Thaler & Amos Tversky & Sidney Winter, 1987. "Decision Making and Problem Solving," Interfaces, INFORMS, vol. 17(5), pages 11-31, October.
    • Handle: RePEc:inm:orinte:v:17:y:1987:i:5:p:11-31
    DOI: 10.1287/inte.17.5.11
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    Citations

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    Cited by:

    1. Siebert, Johannes Ulrich & Kunz, Reinhard E. & Rolf, Philipp, 2021. "Effects of decision training on individuals’ decision-making proactivity," European Journal of Operational Research, Elsevier, vol. 294(1), pages 264-282.
    2. Xuecheng Tian & Yanxia Guan & Shuaian Wang, 2023. "Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under Uncertainty," Mathematics, MDPI, vol. 11(17), pages 1-12, September.
    3. William F. Shughart & Diana W. Thomas & Michael D. Thomas, 2020. "Institutional Change and the Importance of Understanding Shared Mental Models," Kyklos, Wiley Blackwell, vol. 73(3), pages 371-391, August.
    4. Ducksu Seo & Youngsang Kwon, 2017. "In-Migration and Housing Choice in Ho Chi Minh City: Toward Sustainable Housing Development in Vietnam," Sustainability, MDPI, vol. 9(10), pages 1-17, September.
    5. Hippel, Eric von., 1990. "The impact of "sticky" information on innovation and problem-solving," Working papers 3147-90., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    6. Eric W. Ford & Timothy R. Huerta & Nir Menachemi & Dmytro Babik, 2013. "Aligning Strategic Orientation with Information Resources," Journal of Management and Strategy, Journal of Management and Strategy, Sciedu Press, vol. 4(4), pages 32-43, November.
    7. Pedro Antunes & Gustavo Zurita & Nelson Baloian & Claudio Sapateiro, 2014. "Integrating Decision-Making Support in Geocollaboration Tools," Group Decision and Negotiation, Springer, vol. 23(2), pages 211-233, March.
    8. Tongkui Yu & Shu-Heng Chen, 2021. "Big Data, Scarce Attention and Decision-Making Quality," Computational Economics, Springer;Society for Computational Economics, vol. 57(3), pages 827-856, March.
    9. Fu-Sheng TSAI & Wenyi HUANG & Chih-Tsung TAI, 2017. "Change Pressure on Organizational Commitment and Identification: Knowledge and Decision-making Competencies in Government Units," Journal of Social and Administrative Sciences, KSP Journals, vol. 4(1), pages 38-47, March.
    10. Daniel Eisenberg & Thomas Seager & David L. Alderson, 2019. "Rethinking Resilience Analytics," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 1870-1884, September.

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