In emergencies, high stake decisions often have to be made under time pressure and strain. In order to support such decisions, information from various sources needs to be collected and processed rapidly. The information available tends to be temporally and spatially variable, uncertain, and sometimes conflicting, leading to potential biases in decisions. Currently, there is a lack of systematic approaches for information processing and situation assessment which meet the particular demands of emergency situations. To address this gap, we present a Bayesian network-based method called ERIMap that is tailored to the complex information-scape during emergencies. The method enables the systematic and rapid processing of heterogeneous and potentially uncertain observations and draws inferences about key variables of an emergency. It thereby reduces complexity and cognitive load for decision makers. The output of the ERIMap method is a dynamically evolving and spatially resolved map of beliefs about key variables of an emergency that is updated each time a new observation becomes available. The method is illustrated in a case study in which an emergency response is triggered by an accident causing a gas leakage on a chemical plant site.

翻译：在紧急情况下，高风险决策往往需要在时间压力和紧张状态下做出。为支持此类决策，需要快速收集和处理来自不同来源的信息。可用信息往往具有时空变异性、不确定性，有时甚至相互矛盾，可能导致决策偏差。目前，缺乏能够满足紧急情况特殊需求的信息处理和态势评估系统方法。为填补这一空白，我们提出一种基于贝叶斯网络的方法ERIMap，该方法专为紧急情况下的复杂信息环境而设计。该方法能够系统且快速地处理异构且可能不确定的观测数据，并对紧急事件的关键变量进行推理推断，从而降低决策者的认知负荷与问题复杂度。ERIMap方法的输出是一幅动态演化且具有空间分辨率的信念地图，该地图呈现对紧急事件关键变量的概率评估，并在每次获得新观测时实时更新。本文通过一个案例研究阐释该方法的应用场景：某化工厂区因事故导致气体泄漏而触发应急响应。