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Decision-Making
Published in Nancy J. Stone, Chaparro Alex, Joseph R. Keebler, Barbara S. Chaparro, Daniel S. McConnell, Introduction to Human Factors, 2017
Nancy J. Stone, Chaparro Alex, Joseph R. Keebler, Barbara S. Chaparro, Daniel S. McConnell
One way to improve human decision-making is to provide support in the form of decision aids such as simple protocols or computer-generated lists of options. Decision aids are tools that help the human decision-maker deliberate upon two or more options (Bekker et al., 2003). Protocols may consist of an enumerated list of options to consider when purchasing a car (e.g., price, gas mileage, and financing), or a decision tree, as shown in Figure 7.2, which provides a guide to selecting an appropriate statistical test for an experiment. Alternatively, computer-based decision aids may implement sophisticated algorithms to generate a list of options based on inputs that are automatically sampled by the computer or based on user responses to computer queries. Decision aids are widely used in the medical profession. One database (http://decisionaid.ohri.ca) alone lists over 400 medical decision aids, including everything from aids for selecting among treatments for breast cancer, choosing whether to have cataract surgery, and deciding whether to have hair loss treated medically.
Diagnostic techniques and tools
Published in James Douglas, Bill Ransom, Understanding Building Failures, 2013
Information and decision aids comprise diagnostic charts, matrices, tables and computer-based expert systems (CIB 1993). An example of diagnostic information (for collection) – i.e. a Schedule of defects – is shown in Appendix B. The following decision aids (for diagnosis) are contained in Appendix C:General dampness assessment checklist.Fabric dampness data checklist.Moisture content of masonry samples checklist.Condensation assessment checklist.Fungal and insect attack checklist.Crack damage checklist.Appendix D shows an example of a sample diagnostic report. It follows the diagnostics methodology outlined in Chapter 2.
Human–Computer Interaction
Published in Julie A. Jacko, The Human–Computer Interaction Handbook, 2012
François Sainfort, Julie A. Jacko, Molly A. McClellan, Paula J. Edwards
Patient DSS are one of several types of decision aids used to help patients participate in health-related decisions. Decision aids are interventions provided to assist individuals as they make a deliberative choice between two or more alternatives (Bekker, Hewison, and Thornton 2003). Patient DSS supports a patient in one or more stages of making a health-related decision. Much of the past and current patient DSS research and development efforts have targeted patients with life-threatening or chronic diseases. Most have focused on supporting decisions regarding treatment options (i.e. medical or surgical therapies), although a few have examined early detection and other issues (O’Connor 1999). Patient DSS that support patients faced with treatment decisions provide one or more of the following functions that facilitate patient participation in decision making (O’Connor 1999; Scott and Lenert 1998): Educate the patient. Provide the patient with information about the treatment alternatives and outcomes, especially highlighting risks and benefits associated with each treatment alternative.Tailor information. Tailor information content and/or presentation based on patient characteristics such as their health and demographics factors.Assess preferences. Use preference-elicitation methods to assess the patient’s values/preferences for the possible intervention outcomes.Optimize decision. Optimize the decision outcome based on context, heuristics, probabilities of out comes, and algorithms.
Decision making under stress: the role of information overload, time pressure, complexity, and uncertainty
Published in Journal of Decision Systems, 2020
Gloria Phillips-Wren, Monica Adya
The use of technology to aid decision making in these types of managerial situations has typically focused on just two stressors: time pressure and information overload (Aminilari & Pakath, 2005; Marsden et al., 2006; Maule et al., 2000; Smith et al., 1997; Smith & Hayne, 1997). Research has shown that decision aids can mitigate negative effects of stress under these conditions and improve decision quality. In this research, we develop a model for decision aiding under stress that identifies specific stressors in managerial situations and addresses ways that DSS can support DMUS. To do so, we assimilate key findings from the literature broadly in psychology, organisational behaviour, information systems, and management to propose a model for DMUS that can guide the development and use of decision aids such as DSS. We separate Decision Stressors from Job Stressors and relate them to decision quality. We propose that individual differences and decision aids can moderate the decision maker’s psychological perception of stress and, thereby, improve decision quality. In this article, we focus specifically on Decision Stressors only. Our primary research question is: What task factors generate stress during decision making? A general question is: How does stress affect decision quality?
Cognitive bias, decision styles, and risk attitudes in decision making and DSS
Published in Journal of Decision Systems, 2019
Gloria Phillips-Wren, Daniel J. Power, Manuel Mora
Analytics and decision aids such as decision support systems (DSS) are intended to improve the quality of decisions by, for example, using communications technologies, acquiring and processing data, assisting in analyzing data and documents, using quantitative models to identify and solve problems, completing decision process tasks, and guiding decision making. Traditionally, technologically-oriented decision tools have supported only part of an organizational or individual decision process due to the complexity and uncertainty inherent in semi-structured and unstructured decision tasks. The user is expected to interact with the system in some way to provide input or data, make choices about processing, interpret results, or come to a decision. In short, the system should help the decision maker think rationally. In general, these aids are underpinned by statistical, mathematical, and computer science research on problem identification and solution along with studies of human – computer interaction (for a brief history of DSS, see Shim et al., 2002). Advances in fields such as artificial intelligence, data acquisition and storage, cloud computing, virtualization, and network speed has led to the more recent fields of business intelligence, analytics, and big data.