Supporting Complex Decision Making Through Option Awareness
Abstract
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.
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Pfaff et al. showed that using visual analytics to let decision makers explore options increases their option awareness, leading to more accurate and faster decisions.
“Pfaff et al. [31] show that using visual analytics to allow decision makers to explore options increases their option awareness, resulting in more accurate and faster decisions.”
From Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI (2023) · cites this paper as [31]
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- Authors
- Pfaff, M. S., Klein, G. L., Drury, J. L., Moon, S. P., Liu, Y., & Entezari, S. O.
- Affiliations
- Indiana University (US)
- Mitre (United States) (US)
- Corresponding author
- Pfaff, Mark S.
- Venue
- Journal of Cognitive Engineering and Decision Making
- Publisher
- SAGE Publications
- Published
- 2012-09-10
- Volume
- 7
- Issue
- 2
- Pages
- 155-178
- ISSN
- 1555-3434 (ISSN-L)
- OpenAlex
- W2087087443
- Cited by
- 62
- References
- 61
- Citations by year
- 2025: 4 · 2024: 4 · 2023: 4 · 2022: 2 · 2021: 2 · 2020: 4 · 2019: 17 · 2018: 2 · 2017: 2 · 2016: 3 · 2015: 10 · 2014: 4 · 2013: 4
- FWCI
- 8.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Cognitive Engineering and Decision Making (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Keywords
- complex decision-making, situational awareness, decision support, exploratory modeling, visual analytics, naturalistic decision making, rational decision-making, trade-offs, decision accuracy, decision confidence, emergency management, air traffic control, uncertainty
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