HyWorM: An Experiment in Dynamic Improvement of Analytic Processes

Trewhitt, E.1, Whitaker, E.1, Veinott, E.2, Thomas, R.3, Riley, M.1, McDermott, A.4, Eusebi, L.4, Dougherty, M.5, Illingworth, D.5, Guarino, S.4

International Conference on Human-Computer Interaction (July 2021)

HyWorM is an approach and implementation for guiding analytic sensemaking processes using the HyGene model of human hypothesis generation. It is an evolution of the RAMPAGE Workflow Monitor (WorM) that monitors and guides analysts in the production of counterfactual forecasts, dynamically adapting work prompts and the revelation of new evidence to broaden and narrow analyst attention, then controlling the schedule of specific forecast problems. WorM also monitors and controls the timing of workflow steps to ensure that attention is distributed effectively across counterfactual problems and other analysis tasks. The inclusion of HyGene theory in WorM to yield the HyWorM process shows potential to broaden analysts’ attention to a variety of evidence by using results from the HyGene simulation. Based on previous studies with HyGene, we hypothesize that this will improve the quality of counterfactual forecasts.

1 Georgia Tech Research Institute
2 Michigan Technological University
3 Georgia Institute of Technology
4 Charles River Analytics
5 University of Maryland

To learn more, contact Ashley McDermott.

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