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：在突发事件中，高风险的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各种来源的信息。可用信息通常具有时空可变性、不确定性，有时甚至相互矛盾，导致决策可能产生偏差。目前，缺乏能够满足紧急情况特殊需求的信息处理与态势评估的系统性方法。为弥补这一空白，我们提出了一种名为ERIMap的基于贝叶斯网络的方法，该方法针对突发事件中复杂的信息环境进行定制。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，并对突发事件的关键变量进行推断，从而降低决策者的复杂性和认知负荷。ERIMap方法的输出是一个动态演化且具有空间分辨率的突发事件关键变量信念地图，每当有新观测数据可用时，该地图便会更新。该方法通过一个案例研究进行说明，该案例中，化工厂场地因事故导致气体泄漏，触发了一次应急响应。