Title: Understanding and Supporting Decision Making in Denied and Degraded Environments
Date: Monday, June 19, 2023
Time: 2:30PM EST
Location: (Virtual) Microsoft Teams Meeting
William Sealy
Robotics Ph.D. Candidate
School of Aerospace Engineering
Georgia Institute of Technology
Committee:
Dr. Karen Feigh (Advisor) – School of Aerospace Engineering, Georgia Institute of Technology
Dr. Sonia Chernova, School of Interactive Computing, Georgia Institute of Technology
Dr. Matthew Gombolay, School of Interactive Computing, Georgia Institute of Technology
Dr. Dustin Arendt, Visual Analytics, Pacific Northwest National Lab
Dr. Corey Fallon, Analytical Insights, Pacific Northwest National Lab
Abstract:
Decision making is not guaranteed to occur in well-structured environments with perfect information. Tasks in the research most often focus on decisions made with complete information in an unlimited time-frame, and in cases where information is missing or uncertain, the current research stops short of addressing the effect of the distribution of the missing information in the environment. This dissertation seeks specifically to understand how these distributions of information affect decision makers under time pressure, and how best to support decision making in imperfect environments across a range of decision strategies. The contributions of the work are three fold. First, results showed that three studied factors of information distributions (namely Total Information, Complete Attribute Pairs, and Information Imbalance) were significant predictors of decision accuracy in six separate human subject studies featuring varying information complexity and decision strategy biases. Second, this dissertation has highlighted key differences in expert and novice behavior through the lens of information estimation and predecisional information search which further explained individual differences in performance under uncertainty and provided novel design considerations for decision support systems (DSS) in these environments. Finally, the application of both information modification and option prediction DSS showed significant increases in accuracy and reduction in response times across performance groups in both heuristic and analytically-biased environments.