HyWorM: An Experiment in Dynamic Improvement of Analytic Processes
Department of Cognitive and Learning Sciences
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.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
HyWorM: An Experiment in Dynamic Improvement of Analytic Processes.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
12792 LNCS, 311-320.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15292