To effectively coordinate the response to a flood disaster, decision-makers have to prioritise areas that are in most urgent need of assistance. This prioritisation often has to be carried out under time pressure and on the basis of incomplete information, creating a high cognitive load for decision-makers. Methods that integrate Bayesian networks into GIS to draw spatial inference can inform this prioritisation process. However, existing approaches are not equipped to address the time pressure and unclear information-scape that is typical for a flood disaster. In this work, we present a novel spatial inference method for area prioritisation that is designed to address these time and information constraints. The core of this method is a GIS-informed Bayesian network, integrated into an expected loss framework, that can be set up during the preparation phase. The method can then quickly provide area prioritisation recommendations for disaster relief, which has the potential to support decisions-makers during the response phase. In this way, our method provides a means of shifting some of the most time-consuming aspects of the decision-making process from the time-critical disaster response phase to the less critical preparation phase. To illustrate how our method can support rapid and transparent area prioritisation, we present a case study of an extreme flood scenario in Cologne, Germany.

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.

Humanitarian crises, such as the 2014 West Africa Ebola epidemic, challenge information management and thereby threaten the digital resilience of the responding organizations. Crisis information management (CIM) is characterised by the urgency to respond despite the uncertainty of the situation. Coupled with high stakes, limited resources and a high cognitive load, crises are prone to induce biases in the data and the cognitive processes of analysts and decision-makers. When biases remain undetected and untreated in CIM, they may lead to decisions based on biased information, increasing the risk of an inefficient response. Literature suggests that crisis response needs to address the initial uncertainty and possible biases by adapting to new and better information as it becomes available. However, we know little about whether adaptive approaches mitigate the interplay of data and cognitive biases. We investigated this question in an exploratory, three-stage experiment on epidemic response. Our participants were experienced practitioners in the fields of crisis decision-making and information analysis. We found that analysts fail to successfully debias data, even when biases are detected, and that this failure can be attributed to undervaluing debiasing efforts in favor of rapid results. This failure leads to the development of biased information products that are conveyed to decision-makers, who consequently make decisions based on biased information. Confirmation bias reinforces the reliance on conclusions reached with biased data, leading to a vicious cycle, in which biased assumptions remain uncorrected. We suggest mindful debiasing as a possible counter-strategy against these bias effects in CIM.