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Scenario Discovery with Multiple Criteria: An Evaluation of the Robust Decision-Making Framework for Climate Change Adaptation

机译：具有多个条件的场景发现：对气候变化适应的鲁棒决策框架的评估

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摘要

There is increasing concern over deep uncertainty in the risk analysis field as probabilistic models of uncertainty cannot always be confidently determined or agreed upon for many of our most pressing contemporary risk challenges. This is particularly true in the climate change adaptation field, and has prompted the development of a number of frameworks aiming to characterize system vulnerabilities and identify robust alternatives. One such methodology is robust decision making (RDM), which uses simulation models to assess how strategies perform over many plausible conditions and then identifies and characterizes those where the strategy fails in a process termed scenario discovery. While many of the problems to which RDM has been applied are characterized by multiple objectives, research to date has provided little insight into how treatment of multiple criteria impacts the failure scenarios identified. In this research, we compare different methods for incorporating multiple objectives into the scenario discovery process to evaluate how they impact the resulting failure scenarios. We use the Lake Tana basin in Ethiopia as a case study, where climatic and environmental uncertainties could impact multiple planned water infrastructure projects, and find that failure scenarios may vary depending on the method used to aggregate multiple criteria. Common methods used to convert multiple attributes into a single utility score can obscure connections between failure scenarios and system performance, limiting the information provided to support decision making. Applying scenario discovery over each performance metric separately provides more nuanced information regarding the relative sensitivity of the objectives to different uncertain parameters, leading to clearer insights on measures that could be taken to improve system robustness and areas where additional research might prove useful.
机译：风险分析领域中的深层不确定性越来越引起人们的关注，因为不确定性的概率模型无法始终可靠地确定或同意我们许多最紧迫的当代风险挑战。在气候变化适应领域尤其如此，并促使开发了旨在表征系统漏洞并确定可靠替代方案的许多框架。一种这样的方法是鲁棒决策（RDM），它使用仿真模型来评估策略在许多合理条件下的执行方式，然后识别并表征那些在称为场景发现的过程中策略失败的策略。尽管已应用RDM的许多问题具有多个目标，但迄今为止的研究仍未深入了解如何处理多个标准会影响已确定的故障情况。在这项研究中，我们比较了将多个目标合并到方案发现过程中的不同方法，以评估它们如何影响最终的故障方案。我们以埃塞俄比亚的塔纳湖流域为案例研究，其中气候和环境的不确定性可能影响多个计划中的水利基础设施项目，并发现失败的情况可能会根据汇总多个标准的方法而有所不同。用于将多个属性转换为单个效用得分的常用方法可能会使故障场景和系统性能之间的联系模糊，从而限制了为支持决策而提供的信息。将场景发现分别应用于每个性能指标可提供有关目标对不同不确定参数的相对敏感性的更细微差别的信息，从而使人们对可用于提高系统健壮性的措施以及可能需要进行更多研究的领域的见解更为清晰。

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