Supporting Complex Decision Making Through Option Awareness

This article describes the theoretical underpinnings and preliminary experimental support for option awareness (OA): the perception and comprehension of the relative desirability of available options, as well the underlying factors and trade-offs that explain that desirability. The authors' research has produced a body of theory and experimental findings supporting the potential for OA to beneficially augment situation awareness (SA) and help decision makers identify the most robust options: those that are most likely to turn out well under the widest range of possible future conditions. OA incorporates perspectives from rationalistic and naturalistic models of decision making, as both are used concurrently in the types of complex high-technology work the authors have examined, including emergency management, infectious disease containment, and air traffic control. The authors have developed approaches to support OA through the use of exploratory modeling and visual analytics. These systems were tested over the course of four human-in-the-loop experiments. The results demonstrate the value of this approach to improve decision accuracy, confidence, and speed for decision makers facing scenarios at varying levels of difficulty. The methodology described here provides a framework to move forward with research on supporting OA in complex and uncertain scenarios in a variety of task domains.