Adaptive Robust Design under deep uncertainty
Adaptive Robust Design under deep uncertainty
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DOI：
10.1016/j.techfore.2012.10.004
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发表时间：
2013-03-01
影响因子：
12
通讯作者：
Pruyt, Erik
中科院分区：
文献类型：
--
作者：
Hamarat, Caner;Kwakkel, Jan H.;Pruyt, Erik
制定战略或政策，自动适应不断变化的条件被称为适应性决策，分别为适应性决策。本文提出了一种基于迭代计算模型的深度不确定性自适应决策支持方法。该方法将自适应决策框架与计算方法相结合，使用模拟模型、数据挖掘技术和鲁棒优化来生成和探索数千种合理的场景。该方法对面向未来的技术分析（FTA）研究非常有用，并通过与能源转型相关的决策案例进行了说明。本案例演示了如何迭代地改进策略的性能，方法是在数千个可能的场景中探索其性能，识别需要改进的问题子集，识别需要扩展自适应策略的自适应高杠杆操作，直到为整个可能的场景集合找到令人满意的动态自适应策略。该方法不仅适用于能量转换；它也适用于任何具有动态复杂性和深度不确定性的长期结构和系统转型。(C) 2012爱思唯尔公司版权所有。
Developing strategies, or policies, that automatically adapt to changing conditions is called adaptive decision-making, respectively adaptive policy-making. In this paper, we propose an iterative computational model-based approach to support adaptive decision-making under deep uncertainty. This approach combines an adaptive policy-making framework with a computational approach to generate and explore thousands of plausible scenarios using simulation models, data mining techniques, and robust optimization. The proposed approach, which is very useful for Future-Oriented Technology Analysis (FTA) studies, is illustrated on a policy-making case related to energy transitions. This case demonstrates how the performance of a policy can be improved iteratively by exploring its performance across thousands of plausible scenarios, identifying problematic subsets that require improvement, identifying adaptive high leverage actions with which the adaptive policy needs to be extended until a satisfying dynamic adaptive policy is found for the entire ensemble of plausible scenarios. The approach is not only appropriate for energy transitions; it is also appropriate for any long-term structural and systematic transformation characterized by dynamic complexity and deep uncertainty. (C) 2012 Elsevier Inc. All rights reserved.