严重不确定性和模糊权衡下的鲁棒决策分析：入侵物种案例研究
Robust decision analysis under severe uncertainty and ambiguous tradeoffs: an invasive species case study
摘要：
贝叶斯决策分析是一种有效的风险管理决策方法，但它在考虑知识的严重不确定性和管理目标的价值模糊性方面的能力有限。我们研究使用强大的贝叶斯决策分析来处理这些问题中的一个或两个出现的问题。鲁棒贝叶斯方法通过对概率分布的界限对严重的不确定性进行建模，并且通过对效用函数的界限对值模糊性进行建模。为了合并数据，将标准贝叶斯更新应用于整个分布集。为了引出我们的专家的效用代表不同的管理目标的价值，我们使用了一个修改后的版本的摆动加权程序，可以应付严重的价值模糊。我们证明了这些方法的环境管理问题，以消除外来入侵marmorkrebs最近在瑞典发现，这需要一个快速的反应，尽管大量的知识差距，如果物种仍然存在（即严重的不确定性）和需要困难的权衡和竞争的利益（即价值模糊性）。我们确定的决策替代排水系统，并删除个人结合疏浚和筛分有或没有可降解的杀生物剂，或增加pH值，是一贯坏的概率和效用界限的整个范围内。这个案例研究表明，如何强大的贝叶斯决策分析提供了一个透明的方法，在风险管理问题中的信息集成的数据很少，和/或在权衡模糊。
英文摘要：
Bayesian decision analysis is a useful method for risk management decisions, but is limited in its ability to consider severe uncertainty in knowledge, and value ambiguity in management objectives. We study the use of robust Bayesian decision analysis to handle problems where one or both of these issues arise. The robust Bayesian approach models severe uncertainty through bounds on probability distributions, and value ambiguity through bounds on utility functions. To incorporate data, standard Bayesian updating is applied on the entire set of distributions. To elicit our expert's utility representing the value of different management objectives, we use a modified version of the swing weighting procedure that can cope with severe value ambiguity. We demonstrate these methods on an environmental management problem to eradicate an alien invasive marmorkrebs recently discovered in Sweden, which needed a rapid response despite substantial knowledge gaps if the species was still present (i.e. severe uncertainty) and the need for difficult tradeoffs and competing interests (i.e. value ambiguity). We identify that the decision alternatives to drain the system and remove individuals in combination with dredging and sieving with or without a degradable biocide, or increasing pH, are consistently bad under the entire range of probability and utility bounds. This case study shows how robust Bayesian decision analysis provides a transparent methodology for integrating information in risk management problems where little data are available and/or where the tradeoffs ambiguous.
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