Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty
Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty
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DOI：
10.1016/j.envsoft.2016.09.017
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
J. Kwakkel;M. Haasnoot;W. Walker
中科院分区：
文献类型：
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作者：
J. Kwakkel;M. Haasnoot;W. Walker
已经提出了各种基于模型的方法来支持深度不确定性下的决策，但它们很少进行比较和对比。在本文中，我们比较了鲁棒决策与动态自适应策略路径。我们适用于一个假设的情况下，灵感来自河流到达荷兰的莱茵河三角洲，并比较它们所需的工具，由此产生的决策相关的见解，以及由此产生的计划。结果表明，这两种方法是互补的。稳健的决策提供了对问题发生条件的洞察，并使权衡透明。动态自适应策略路径方法强调随着时间的推移动态适应，从而为处理通过稳健决策确定的漏洞提供了一种自然的方法。该应用程序还表明，稳健决策的分析过程是路径依赖和开放式的：分析师必须做出许多选择，稳健决策没有提供直接的指导。
A variety of model-based approaches for supporting decision-making under deep uncertainty have been suggested, but they are rarely compared and contrasted. In this paper, we compare Robust Decision-Making with Dynamic Adaptive Policy Pathways. We apply both to a hypothetical case inspired by a river reach in the Rhine Delta of the Netherlands, and compare them with respect to the required tooling, the resulting decision relevant insights, and the resulting plans. The results indicate that the two approaches are complementary. Robust Decision-Making offers insights into conditions under which problems occur, and makes trade-offs transparent. The Dynamic Adaptive Policy Pathways approach emphasizes dynamic adaptation over time, and thus offers a natural way for handling the vulnerabilities identified through Robust Decision-Making. The application also makes clear that the analytical process of Robust Decision-Making is path-dependent and open ended: an analyst has to make many choices, for which Robust Decision-Making offers no direct guidance.