On the Interplay of Data and Cognitive Bias in Crisis Information Management

David Paulus (), Ramian Fathi (), Frank Fiedrich (), Bartel Van Walle () and Tina Comes ()
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David Paulus: Delft University of Technology
Ramian Fathi: University of Wuppertal
Frank Fiedrich: University of Wuppertal
Bartel Van Walle: United Nations University
Tina Comes: Delft University of Technology

Information Systems Frontiers, 2024, vol. 26, issue 2, No 3, 415 pages

Abstract: Abstract 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.

Keywords: Data bias; Cognitive bias; Crisis information management; Digital resilience; Mindfulness; Epidemics (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10796-022-10241-0

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