This dissertation work describes a decision support system (DSS), two experiments, and the resulting insights regarding the role and influence of an uncertainty visualization in assisting decision makers to gauge more realistically whether they have enough information to make decisions. The primary goal of this work is to improve the scientific understanding of decision making in situations where the best design options are ambiguous. Decision makers are often faced with the task of identifying the "best" option from a set, where "best" is defined by multiple criteria came out by the decision maker. However, uncertainty and lack of information can make the task hard.
Uncertainty is inherent in all real work contexts; it creates ambiguities that make decision making difficult. To help decision makers recognize and manage ambiguity the author developed and evaluated a domain-independent decision support tool (DSS), the Uncertainty DSS. It is designed to help decision makers recognize situations in which uncertainty creates ambiguity in their choices, and to identify information which can help reduce that ambiguity. It does so by providing a simple graphical representation of the relative value ranges of multiple options. The aim is to help decision makers visually recognize when options overlap in their possible values, in which case it is difficult to identify the "best" option with information available. In order to evaluate the impact of the Uncertainty DSS, the author created a pared-down version, the Certainty DSS, which provides no uncertainty visualizations. Two experiments were designed and conducted with engineering designers in the context of both individual and team decision making. Participants carried out real decision making task in the context of real design projects in which they had been engaged for at least a month. The results of the experiment with individual decision makers showed that without the visualizations, engineering designers did not distinguish between ambiguous and unambiguous sets of options despite being aware in a general sense that there was much uncertainty in their design alternatives. However, with Uncertainty DSS, participants exhibited a significantly improved ability to recognize ambiguous decision situations, and expressed appropriately reduced confidence in ambiguous situations. Additionally, Uncertainty DSS increased the likelihood that participants would form plans to seek clarifying information on critical, uncertain parameters. The results of the experiments with team decision makers were similar to the individual decision makers. Moreover, teams using the Uncertainty DSS communicated more within the team and developed better shared situation awareness. These results suggest that a relatively simple visualization of uncertainty can benefit both individual decision makers, and teams of decision makers by assisting them in: 1) identifying when they do not have enough information to make an unambiguous choice, 2) identifying what additional information might reduce uncertainty and 3) providing a common structure is which teams can discuss uncertainty and its impact on the design decisions.
While there is still room for improvement to fine tune the DSS measurements, and training given to designers to enable them to think about how uncertainty and information (a lack of it) impact design decisions. However, this work represents a first step demonstrating that uncertainty visualization can change the way in which designers think about uncertainty--New guidance for tools for decision makers.
University of Minnesota Ph.D. dissertation. September 2012. Major: Industrial and Systems Engineering. Advisor: Caroline Hayes. 1 computer file (PDF0; x, 164 pages, appendices A-C.
Visualizing uncertainty information in Engineering design processes to assist individual and team decision making.
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