Failing to communicate current knowledge limitations, that is, epistemic uncertainty, in environmental risk assessment (ERA) may have severe consequences for decision making. Bayesian networks (BNs) have gained popularity in ERA, primarily because they can combine variables from different models and integrate data and expert judgment. This paper highlights potential gaps in the treatment of uncertainty when using BNs for ERA and proposes a consistent framework (and a set of methods) for treating epistemic uncertainty to help close these gaps. The proposed framework describes the treatment of epistemic uncertainty about the model structure, parameters, expert judgment, data, management scenarios, and the assessment's output. We identify issues related to the differentiation between aleatory and epistemic uncertainty and the importance of communicating both uncertainties associated with the assessment predictions (direct uncertainty) and the strength of knowledge supporting the assessment (indirect uncertainty). Probabilities, intervals, or scenarios are expressions of direct epistemic uncertainty. The type of BN determines the treatment of parameter uncertainty: epistemic, aleatory, or predictive. Epistemic BNs are useful for probabilistic reasoning about states of the world in light of evidence. Aleatory BNs are the most relevant for ERA, but they are not sufficient to treat epistemic uncertainty alone because they do not explicitly express parameter uncertainty. For uncertainty analysis, we recommend embedding an aleatory BN into a model for parameter uncertainty. Bayesian networks do not contain information about uncertainty in the model structure, which requires several models. Statistical models (e.g., hierarchical modeling outside the BNs) are required to consider uncertainties and variability associated with data. We highlight the importance of being open about things one does not know and carefully choosing a method to precisely communicate both direct and indirect uncertainty in ERA. Integr Environ Assess Manag 2021;17:221–232. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC)

中文翻译：

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“这是我们所不知道的”：处理贝叶斯网络中的认知不确定性以进行风险评估

无法在环境风险评估（ERA）中传达当前的知识限制，即认知不确定性，可能会对决策产生严重影响。贝叶斯网络（BNs）在ERA中获得了普及，主要是因为它们可以合并来自不同模型的变量，并整合数据和专家判断。本文重点介绍了将BN用于ERA时不确定性治疗的潜在差距，并提出了一个一致的框架（和一套方法）来治疗认知不确定性以帮助弥合这些差距。提出的框架描述了关于模型结构，参数，专家判断，数据，管理方案以及评估结果的认知不确定性的处理。我们确定了与不确定性和认知不确定性之间的区别有关的问题，以及与评估预测相关的不确定性（直接不确定性）和支持评估的知识强度（间接不确定性）的重要性。概率，间隔或场景是直接的认知不确定性的表达。BN的类型决定了对参数不确定性的处理：认知，偶然或预测。根据证据，认知BN对于世界状态的概率推理很有用。通气性BN与ERA最相关，但仅凭它们不能明确表达参数不确定性，不足以单独治疗认知不确定性。对于不确定性分析，我们建议将偶然的BN嵌入参数不确定性模型中。贝叶斯网络不包含有关模型结构不确定性的信息，这需要多个模型。需要统计模型（例如，BN之外的分层建模）来考虑与数据相关的不确定性和可变性。我们强调了对未知的事物保持开放并认真选择一种方法来精确传达ERA中直接和间接不确定性的重要性。Integr环境评估管理2021; 17：221–232。©2020作者。Wiley Periodicals LLC代表环境毒理化学协会（SETAC）发布的《综合环境评估与管理》