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Transitioning From Dynamic Decision Support to Context-Aware Multi-Party Coordination: A Case for Emergency Response

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  • Steven Way

    (McMaster University)

  • Yufei Yuan

    (McMaster University)

Abstract

In this paper, a general framework of context-aware multi-party coordination systems is proposed which extends the concept of dynamic decision making and functionality of dynamic decision making support systems in response to large-scale complex events. Using disaster responses as illustrative examples, event responders and decision makers face dynamic and complex problems with environmental, organizational, and activity-based issues. These issues are addressed by incorporating context-awareness, multi-party relationship management, and task-based coordination components into a framework for context-aware multi-party coordination systems.

Suggested Citation

  • Steven Way & Yufei Yuan, 2014. "Transitioning From Dynamic Decision Support to Context-Aware Multi-Party Coordination: A Case for Emergency Response," Group Decision and Negotiation, Springer, vol. 23(4), pages 649-672, July.
    • Handle: RePEc:spr:grdene:v:23:y:2014:i:4:d:10.1007_s10726-013-9365-3
    DOI: 10.1007/s10726-013-9365-3
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    References listed on IDEAS

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    1. Nobuo Mimura & Kazuya Yasuhara & Seiki Kawagoe & Hiromune Yokoki & So Kazama, 2011. "Damage from the Great East Japan Earthquake and Tsunami - A quick report," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 16(7), pages 803-818, October.
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    4. N C Simpson & P G Hancock, 2009. "Fifty years of operational research and emergency response," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 126-139, May.
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    Cited by:

    1. Terri L. Griffith & John E. Sawyer & M. Scott Poole, 2019. "Systems Savvy: Practical Intelligence for Transformation of Sociotechnical Systems," Group Decision and Negotiation, Springer, vol. 28(3), pages 475-499, June.
    2. Xiang, Liu, 2020. "Energy emergency supply chain collaboration optimization with group consensus through reinforcement learning considering non-cooperative behaviours," Energy, Elsevier, vol. 210(C).

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