The purpose of this study was to propose an effective knowledge elicitation method and representation scheme that empowers humanitarian assistance/disaster relief (HA/DR) analysts and experts to create analytic models without the aid of data scientists and methodologists while addressing the issues of complexity, collaboration, and emerging technology across a diverse global network of HA/DR organizations. The study used an exploratory sequential mixed-methods research approach with two stages. In the first stage, literature was explored related to the issues while aligning them to analytic model perspectives to define the experimental modeling requirements. This stage concluded with the development of a simplified analytic modeling approach based on emerging activity-based intelligence (ABI) analytic methods. The second stage consisted of quantitative data collection and evaluation to test the ABI analytic model’s knowledge elicitation method and representation scheme. Using open-source data on the Syrian humanitarian crisis as the reference mission, ABI analytic models were proven capable in modeling HA/DR scenarios of physical systems, nonphysical systems, and thinking. As a data-agnostic approach to develop object and network knowledge, ABI aligns with the objectives of modeling within multiple HA/DR organizations. Using an analytic method as the basis for model creation allows for immediate adoption by analysts and removes the need for data scientists and methodologists in the elicitation phase. Applying this highly effective cross-domain ABI data fusion technique should also supplant the accuracy weaknesses created by traditional simplified analytic models.
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