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Inverse engineering preferences in simple games

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  • Garcia, A.
  • Hipel, K.W.

Abstract

A method for inverse engineering decision-makers’ preferences based on observable behaviour is designed. This technique allows analysts to narrow down the list of potential preference rankings of possible states in a conflict for each decision-maker using probabilities and expected values. During the inverse engineering procedure, the list of all possible preference rankings is narrowed as decision-makers move and counter-move. Accurate preference information is key to building quality conflict and game models; however, preference rankings for decision-makers are often difficult to obtain directly. A simple two decision-maker, four-state game is used to demonstrate the applicability of the method and to illustrate the insights it provides.

Suggested Citation

  • Garcia, A. & Hipel, K.W., 2017. "Inverse engineering preferences in simple games," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 184-194.
  • Handle: RePEc:eee:apmaco:v:311:y:2017:i:c:p:184-194
    DOI: 10.1016/j.amc.2017.05.016
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    References listed on IDEAS

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    1. Luai Hamouda & D. Marc Kilgour & Keith W. Hipel, 2004. "Strength of Preference in the Graph Model for Conflict Resolution," Group Decision and Negotiation, Springer, vol. 13(5), pages 449-462, September.
    2. Andrea Galeotti & Sanjeev Goyal & Matthew O. Jackson & Fernando Vega-Redondo & Leeat Yariv, 2010. "Network Games," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(1), pages 218-244.
    3. John Conlisk, 1996. "Why Bounded Rationality?," Journal of Economic Literature, American Economic Association, vol. 34(2), pages 669-700, June.
    4. Haiyan Xu & Keith Hipel & D. Kilgour & Ye Chen, 2010. "Combining strength and uncertainty for preferences in the graph model for conflict resolution with multiple decision makers," Theory and Decision, Springer, vol. 69(4), pages 497-521, October.
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    Cited by:

    1. Garcia, Amanda & Obeidi, Amer & Hipel, Keith W., 2018. "Strategic advice for decision-making under conflict based on observed behaviour," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 96-104.
    2. Keith W. Hipel & Liping Fang & D. Marc Kilgour, 2020. "The Graph Model for Conflict Resolution: Reflections on Three Decades of Development," Group Decision and Negotiation, Springer, vol. 29(1), pages 11-60, February.
    3. Yu Han & Haiyan Xu & Liping Fang & Keith W. Hipel, 2022. "An Integer Programming Approach to Solving the Inverse Graph Model for Conflict Resolution with Two Decision Makers," Group Decision and Negotiation, Springer, vol. 31(1), pages 23-48, February.
    4. Mengjie Yang & Kai Yang & Yue Che & Shiqiang Lu & Fengyun Sun & Ying Chen & Mengting Li, 2021. "Resolving Transboundary Water Conflicts: Dynamic Evolutionary Analysis Using an Improved GMCR Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3321-3338, August.

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