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The Graph Model for Conflict Resolution: Reflections on Three Decades of Development

Author

Listed:
  • Keith W. Hipel

    (University of Waterloo
    Balsillie School of International Affairs
    Centre for International Governance Innovation)

  • Liping Fang

    (University of Waterloo
    Ryerson University)

  • D. Marc Kilgour

    (University of Waterloo
    Wilfrid Laurier University)

Abstract

The fundamental design and inherent capabilities of the Graph Model for Conflict Resolution (GMCR) to address a rich range of complex real world conflict situations are put into perspective by tracing its historical development over a period spanning more than 30 years, and highlighting great opportunities for meaningful future expansions within an era of artificial intelligence (AI) and intensifying conflict in an over-crowded world. By constructing a sound theoretical foundation for GMCR based upon assumptions reflecting what actually occurs in reality, a fascinating story is narrated on how GMCR was able to expand in bold new directions as well as take advantage of many important legacy decision technologies built within the earlier Metagame Analysis and later Conflict Analysis paradigms. From its predecessors, for instance, GMCR could benefit by the employment of option form put forward within Metagame Analysis for effectively recording a conflict, as well as preference elicitation techniques and solution concepts for defining chess-like behavior when calculating stability of states from the realm of Conflict Analysis. The key ideas outlined in the paper underlying the current and projected capabilities of GMCR include the development of four different ways to handle preference uncertainty in the presence of either transitive or intransitive preferences; a wide range of solution concepts for describing many kinds of human behavior under conflict; unique coalition analysis algorithms for determining if a given decision maker can fare better in a dispute via cooperation; tracing the evolution of a conflict over time; and the matrix formulation of GMCR for computational efficiency when calculating stability and also theoretically expanding GMCR in bold new directions. Inverse engineering is mentioned as an AI extension of GMCR for computationally determining the preferences required by decision makers in order to reach a desirable state, such as a climate change agreement in which all nations significantly cut back on their greenhouse gas emissions. The basic design of a decision support system for permitting researchers and practitioners to readily apply the foregoing and other advancements in GMCR to tough real world controversies is discussed. Although GMCR has been successfully applied to challenging disputes arising in many different fields, a simple climate change negotiation conflict between the US and China is utilized to explain clearly key concepts mentioned throughout the fascinating historical journey surrounding GMCR.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:grdene:v:29:y:2020:i:1:d:10.1007_s10726-019-09648-z
    DOI: 10.1007/s10726-019-09648-z
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    References listed on IDEAS

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    1. Shawei He & Keith Hipel & D. Kilgour, 2014. "Water Diversion Conflicts in China: A Hierarchical Perspective," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 1823-1837, May.
    2. Garcia, A. & Hipel, K.W., 2017. "Inverse engineering preferences in simple games," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 184-194.
    3. Amer Obeidi & Keith W. Hipel & D. Marc Kilgour, 2005. "The Role of Emotions in Envisioning Outcomes in Conflict Analysis," Group Decision and Negotiation, Springer, vol. 14(6), pages 481-500, November.
    4. Kaveh Madani & Keith Hipel, 2011. "Non-Cooperative Stability Definitions for Strategic Analysis of Generic Water Resources Conflicts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(8), pages 1949-1977, June.
    5. D. Marc Kilgour & Keith W. Hipel & Liping Fang & Xiaoyong (John) Peng, 2001. "Coalition Analysis in Group Decision Support," Group Decision and Negotiation, Springer, vol. 10(2), pages 159-175, March.
    6. K W Li & D M Kilgour & K W Hipel, 2005. "Status quo analysis in the graph model for conflict resolution," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(6), pages 699-707, June.
    7. Inohara, Takehiro, 2016. "State transition time analysis in the Graph Model for Conflict Resolution," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 372-382.
    8. Rami A. Kinsara & D. Marc Kilgour & Keith W. Hipel, 2018. "Communication features in a DSS for conflict resolution based on the graph model," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 10(1), pages 39-56.
    9. D. Marc Kilgour & Keith W. Hipel, 2005. "The Graph Model for Conflict Resolution: Past, Present, and Future," Group Decision and Negotiation, Springer, vol. 14(6), pages 441-460, November.
    10. 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.
    11. Takahashi, Masao Allyn & Fraser, Niall M. & Hipel, Keith W., 1984. "A procedure for analyzing hypergames," European Journal of Operational Research, Elsevier, vol. 18(1), pages 111-122, October.
    12. Wang, Junjie & Hipel, Keith W. & Fang, Liping & Dang, Yaoguo, 2018. "Matrix representations of the inverse problem in the graph model for conflict resolution," European Journal of Operational Research, Elsevier, vol. 270(1), pages 282-293.
    13. M. Abul Bashar & Keith W. Hipel & D. Marc Kilgour & Amer Obeidi, 2018. "Interval fuzzy preferences in the graph model for conflict resolution," Fuzzy Optimization and Decision Making, Springer, vol. 17(3), pages 287-315, September.
    14. Haiyan Xu & D. Marc Kilgour & Keith W. Hipel, 2011. "Matrix Representation of Conflict Resolution in Multiple-Decision-Maker Graph Models with Preference Uncertainty," Group Decision and Negotiation, Springer, vol. 20(6), pages 755-779, November.
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    Cited by:

    1. Shawei He & Xiaohui Liu & Xianmei Li, 2023. "A Graph Model for Conflict Resolution with Time-varying Attitudes and Its Application to China-US Trade Disputes," Group Decision and Negotiation, Springer, vol. 32(3), pages 603-631, June.
    2. M. Nassereddine & M. A. Ellakkis & A. Azar & M. D. Nayeri, 2021. "Developing a Multi-methodology for Conflict Resolution: Case of Yemen’s Humanitarian Crisis," Group Decision and Negotiation, Springer, vol. 30(2), pages 301-320, April.
    3. Ricardo Lopes Andrade & Maísa Mendonça Silva & Leandro Chaves Rêgo, 2023. "A Scientometric and Social Network Analysis of the Literature on the Graph Model for Conflict Resolution," Group Decision and Negotiation, Springer, vol. 32(5), pages 1061-1082, October.
    4. Shafi, Ahsan & Wang, Zhanqi & Ehsan, Muhsan & Riaz, Faizan Ahmed & Ali, Muhammad Rashid & Xu, Feng, 2023. "A game theory approach to land acquisition conflicts in Pakistan," Land Use Policy, Elsevier, vol. 132(C).
    5. Leandro Chaves Rêgo & France E. G. Oliveira, 2020. "Higher-order Sequential Stabilities in the Graph Model for Conflict Resolution for Bilateral Conflicts," Group Decision and Negotiation, Springer, vol. 29(4), pages 601-626, August.
    6. Inohara, Takehiro, 2023. "Similarities, differences, and preservation of efficiencies, with application to attitude analysis, within the Graph Model for Conflict Resolution," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1330-1348.
    7. He, Shawei, 2022. "A time sensitive graph model for conflict resolution with application to international air carbon negotiation," European Journal of Operational Research, Elsevier, vol. 302(2), pages 652-670.
    8. Yasuhiro Asa & Takeshi Kato & Ryuji Mine, 2022. "Composite Consensus-Building Process: Permissible Meeting Analysis and Compromise Choice Exploration," Papers 2211.08593, arXiv.org.
    9. Liangyan Tao & Xuebi Su & Saad Ahmed Javed, 2021. "Inverse Preference Optimization in the Graph Model for Conflict Resolution based on the Genetic Algorithm," Group Decision and Negotiation, Springer, vol. 30(5), pages 1085-1112, October.
    10. Peng, Benhong & Zhao, Yinyin & Elahi, Ehsan & Wan, Anxia, 2023. "Can third-party market cooperation solve the dilemma of emissions reduction? A case study of energy investment project conflict analysis in the context of carbon neutrality," Energy, Elsevier, vol. 264(C).
    11. Huang, Yuming & Ge, Bingfeng & Hipel, Keith W. & Fang, Liping & Zhao, Bin & Yang, Kewei, 2023. "Solving the inverse graph model for conflict resolution using a hybrid metaheuristic algorithm," European Journal of Operational Research, Elsevier, vol. 305(2), pages 806-819.
    12. Sabino, Emerson Rodrigues & Rêgo, Leandro Chaves, 2023. "Optimism pessimism stability in the graph model for conflict resolution for multilateral conflicts," European Journal of Operational Research, Elsevier, vol. 309(2), pages 671-682.
    13. 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.

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