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An Actual Application of Collective Choice Theory to the Selection of Trajectories for the Mariner Jupiter/Saturn 1977 Project

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  • James S. Dyer

    (University of California, Los Angeles, California)

  • Ralph F. Miles

    (Jet Propulsion Laboratory, Pasadena, California)

Abstract

This paper describes the use of decision analysis to facilitate a group decision-making problem in the selection of trajectories for the two spacecraft of the Mariner Jupiter/Saturn 1977 Project. This NASA project includes the participation of some eighty scientists, divided by specialization among eleven science teams. A set of thirty-two candidate trajectory pairs was developed by the Project, in collaboration with the science teams. Each science team then ordinally ranked and assigned cardinal utility function values to the trajectory pairs. The scientists used these data and statistics derived from collective choice rules in selecting the preferred trajectory pair.

Suggested Citation

  • James S. Dyer & Ralph F. Miles, 1976. "An Actual Application of Collective Choice Theory to the Selection of Trajectories for the Mariner Jupiter/Saturn 1977 Project," Operations Research, INFORMS, vol. 24(2), pages 220-244, April.
    • Handle: RePEc:inm:oropre:v:24:y:1976:i:2:p:220-244
    DOI: 10.1287/opre.24.2.220
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    Cited by:

    1. Melissa Garber & Shahram Sarkani & Thomas Mazzuchi, 2017. "A Framework for Multiobjective Decision Management with Diverse Stakeholders," Systems Engineering, John Wiley & Sons, vol. 20(4), pages 335-356, July.
    2. Salo, Ahti A., 1995. "Interactive decision aiding for group decision support," European Journal of Operational Research, Elsevier, vol. 84(1), pages 134-149, July.
    3. Bose, Utpal & Davey, Anne M. & Olson, David L., 1997. "Multi-attribute utility methods in group decision making: Past applications and potential for inclusion in GDSS," Omega, Elsevier, vol. 25(6), pages 691-706, December.
    4. Yelin Fu & Kong Xiangtianrui & Hao Luo & Lean Yu, 2020. "Constructing Composite Indicators with Collective Choice and Interval-Valued TOPSIS: The Case of Value Measure," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 117-135, November.
    5. Peter Kurrild-Klitgaard, 2014. "Empirical social choice: an introduction," Public Choice, Springer, vol. 158(3), pages 297-310, March.
    6. Ausloos, Marcel, 2024. "Hierarchy selection: New team ranking indicators for cyclist multi-stage races," European Journal of Operational Research, Elsevier, vol. 314(2), pages 807-816.
    7. John Dobra, 1983. "An approach to empirical studies of voting paradoxes: An update and extension," Public Choice, Springer, vol. 41(2), pages 241-250, January.
    8. Fan Liu & Ning Ma, 2019. "Multicriteria ABC Inventory Classification Using the Social Choice Theory," Sustainability, MDPI, vol. 12(1), pages 1-19, December.
    9. Robin L. Dillon & M. Elisabeth Paté-Cornell & Seth D. Guikema, 2003. "Programmatic Risk Analysis for Critical Engineering Systems Under Tight Resource Constraints," Operations Research, INFORMS, vol. 51(3), pages 354-370, June.
    10. Ralph F. Miles, 2004. "Risk‐Adjusted Mission Value: Trading Off Mission Risk for Mission Value," Risk Analysis, John Wiley & Sons, vol. 24(2), pages 415-424, April.
    11. Rakesh K. Sarin & L. Robin Keller, 2013. "From the Editors ---Group Decisions, Preference Elicitation, Experienced Utility, Survival Probabilities, and Portfolio Value of Information," Decision Analysis, INFORMS, vol. 10(2), pages 99-102, June.
    12. Yelin Fu & Yubing Sui & Hao Luo & Biao Han, 2020. "Application of Social Choice Theory to Modify the Value Measure of Health Systems," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 148(3), pages 1005-1019, April.
    13. Ralph L. Keeney, 2013. "Foundations for Group Decision Analysis," Decision Analysis, INFORMS, vol. 10(2), pages 103-120, June.

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