Creating Effective Decision Aids for Complex Tasks
Caroline Clarke Hayes and Farnaz Akhavi
Journal of Usability Studies, Volume 3, Issue 4, August 2008, pp. 152-172
Article Contents
Summary of observations
- Designers had more than one approach to decision making: they used both rapid elimination and considered comparison for more difficult deliberations. These represent two ends of a spectrum of rapid to deliberate focus. Decision aids may not be appropriate and may be viewed as burdensome in rapid elimination tasks, although they provide value in tasks requiring considered comparison. This observation supports the inference drawn from the laboratory studies that decision aids are not necessarily appropriate for all decisions.
- The tasks of information seeking, comparison of alternatives, and down selection are tightly intertwined. This may suggest that to support designers' actual work practices, a decision aid may need to support all of these tasks seamlessly. Neither of the mathematical models used in this study, nor the decision aids incorporating them, supported information seeking. This may limit the utility and impact of the decision aids.
- The process of arriving at a decision is a flexible exploration process. The process of preparing for a decision is really one of exploration to develop a deeper understanding of the design goals and the alternatives and the unexplored possibilities. As Ullman et al. (1988) also observed designers exhibit great flexibility in this exploration process; they continually jump between adding or refining alternatives, gathering additional information, making comparisons, adding new criteria, adjusting estimates, etc. Ideally, a decision aid should support this flexible exploration process. Most mathematical decision methods assume that this exploration has already been done, and that design goals, relevant criteria, and alternatives have been specified and are now fixed. Such assumptions are likely too rigid for the ill-defined nature of complex design tasks, particularly conceptual design.
- Precise information or statistical distributions describing likely design performance are often not available in practice. Furthermore, there may be high costs associated with information seeking. Thus, the assumption made by many mathematical models that statistical distributions estimating design performance can be obtained may not be reasonable. Decision aids must not assume it is.
- Designers can rapidly apply much knowledge and experience in their heads. However, articulating this information and entering it into a tool may be perceived to be a burden. Additionally, the designers are very astute about which information they need most. They do not typically explore all criteria for all alternatives. They spend more time on criterion that will distinguish top options, skipping many others. This is a time-saving strategy that most mathematical methods do not support.