Linkov Igor, Burmistrov Dmitriy
ICF Consulting, Lexington, MA 02421, USA.
Risk Anal. 2003 Dec;23(6):1297-308. doi: 10.1111/j.0272-4332.2003.00402.x.
The treatment of uncertainties associated with modeling and risk assessment has recently attracted significant attention. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte Carlo modeling are often recommended. However, the issue of model uncertainty is still rarely addressed in practical applications of risk assessment. The use of several alternative models to derive a range of model outputs or risks is one of a few available techniques. This article addresses the often-overlooked issue of what we call "modeler uncertainty," i.e., difference in problem formulation, model implementation, and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit Working Group, which was created under the International Atomic Energy Agency (IAEA) BIOMASS program (BIOsphere Modeling and ASSessment). Model-model and model-data intercomparisons reviewed in this study were conducted by the working group for a total of three different scenarios. The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that parameter uncertainty (as evaluated by a probabilistic Monte Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in risk characterization.
与建模和风险评估相关的不确定性处理问题近来备受关注。处理参数不确定性的方法和指南已相当完善，常推荐使用诸如蒙特卡洛建模等定量工具。然而，在风险评估的实际应用中，模型不确定性问题仍很少被提及。使用多种替代模型来得出一系列模型输出或风险是少数可用技术之一。本文探讨了一个常被忽视的问题，即我们所谓的“建模者不确定性”，也就是由于对当前问题的主观解读而在问题表述、模型实施和参数选择上存在的差异。本研究采用了国际原子能机构（IAEA）生物质计划（生物球体建模与评估，BIOMASS）下成立的水果工作组的结果。本研究中所回顾的模型 - 模型及模型 - 数据比对是由该工作组针对总共三种不同情景进行的。结果发现，最大的不确定性源自建模者对情景的解读以及建模者所做的近似处理。在建模者不清楚的情景中，模型预测的初始差异高达七个数量级。只有在就具体假设进行了几次会议和讨论之后，各种模型预测的差异才趋于一致。我们的研究表明，参数不确定性（通过概率蒙特卡洛评估）可能对整体建模不确定性贡献了一个以上数量级。最终的模型预测范围在一到三个数量级之间，具体取决于特定情景。本研究说明了问题表述以及在风险特征描述中实施分析 - 审议过程的重要性。
