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In understanding decision making over the last 50 years, there have been a variety of approaches to analyzing the skill or proficiency in reasoning that develops as the decision maker gains expertise. These are shown in Figure 7.2. To some degree, all of these approaches are related, but represent facets of decision making and macrocognition. These approaches provide a framework for many of the sections to follow. In the first row of Figure 7.2, Rasmussen [347] has proposed a three-level categorization of behavior. These levels evolve as the person develops progressively more skill or as the problems become progressively less complex. The progression from knowledgebased to rule-based to the more automatic skill-based behavior parallels the development of automaticity described in the previous chapter. Closely paralleling this, in the second row, is the distinction between careful analytic processing (describing all the options and factors that should enter into a choice), and the more “gut level” intuitive processing, often less accessible to conscious awareness [348]. Here, as with Rasmussen’s levels of behavior, more intuitive decisions are more likely to emerge with greater skill and simpler problems.

The third row shows different cognitive systems that underly how people make decisions [349, 350, 351]. System 2, like analytical judgments and knowledge-based reasoning, is considered to serve a deliberative function that involves resource-intensive effortful processes. In contrast, System 1 like intuitive judgments and the skill-based reasoning, engages relatively automatic “gut-feel” snap judgments. System 1 is guided by what is easy, effort-free and feels good or bad; that is, the emotional component of decision making. In partial contrast with skill-based behavior and intuitive judgments however, engaging System 1 does not necessarily represent greater expertise than engaging System 2. Instead, the two systems operate in parallel in any given decision, with System 1 offering a snap decisions of what to do, but then System 2, if time and cognitive resources or effort are available, overseeing and checking the result of System 1 to assure its correctness. System 1 also aids System 2 by focusing attention and filtering options—without it we would struggle to make a decision [352]. In the fourth row, we show two different “schools” of decision research that will be the focus of much of our discussion below. The “heuristics and biases” approach, developed by Kahneman and Tversky [353, 354] has focused on the kinds of decision shortcuts made because of the limits of reasoning, and hence the kinds of biases that often lead to decision errors. These biases identify “what’s wrong” with decision making and what requires human factors interventions. In contrast, the naturalistic decision making school, proposed by Klein [355, 356] examines decision making of the expert, many of whose choices share features of skill-based behavior, intuitive decision making that are strongly influenced by System 1. That is, such decisions are often quick, relatively effortfree, and typically correct. While these two approaches are often set in contrast, it is certainly plausible to see both as being correct,but applicable in different circumstances, and hence more complementary than competitive [357]. Heuristics and intuitive decision making work well for experienced people in familiar circumstances, but biases undermine performance of novices or experts in unfamiliar circumstances. In the final row, we describe a characteristic of metacognition that appears, generally to emerge with greater skill. That is, it becomes increasingly adaptive, with the human better able to select the appropriate tools, styles, types, and systems, given the circumstances. That is, with expertise, people develop a larger cognitive toolkit, as does the wisdom regarding which tools to apply when. The first row of Figure 7.2, shows skill-, rule-, and knowledgebased (SRK) behavior depends on people’s expertise and the situation [358, 347, 359]. High levels of experience with analog representations promote relatively effortless skill-based behavior (e.g., riding a bicycle), whereas little experience with numeric and textual information will lead to knowledge-based behavior (e.g., selecting an apartment using a spreadsheet). In between, like the decision to bring a raincoat on a bike ride, follows rule-based behavior: “if the forecast chance of rain is greater than 30%, then bring it.” These SRK distinctions also describe types of human errors [360], which we discuss in Chapter 16). These distinctions are particularly important because we can improve decision making and reduce errors by supporting skill-, rule-, and knowledge-based behavior. Figure 7.3 shows the SRK process for responding to sensory input that enters at the lower left. This input can be interpreted at one of three levels, depending on the operator’s degree of experience with the particular situation and how information is represented [358, 348]. The right side shows an example of sensory input: a meter that an operator has to monitor. The figure shows that the same meter is interpreted differently depending on the level of behavior engaged: as a signal for skill-based behavior, as a sign for rule-based behavior, and as a symbol for knowledge-based behavior. Signals and skill-based behavior. People who are extremely experienced with a task tend to process the input at the skill-based level, reacting to the perceptual elements at an automatic, subconscious level. They do not have to interpret and integrate the cues or think of possible actions, but only respond to cues as signals that guide responses. Because the behavior is automatic, the demand on attentional resources described in Chapter 6 is minimal. For example, an operator might turn a valve in a continuous manner to counteract changes in flow shown on a meter (see bottom left of Figure 7.3). Y Designs that enable skillbased behavior are “intuitive”. Signs and rule-based behavior. When people are familiar with the task but do not have extensive experience, they process input and perform at the rule-based level. The input is recognized in relation to typical system states, termed signs, which trigger rules for accumulated knowledge. This accumulated knowledge can be inthe person’s head or written down in formal procedures. Following a recipe to bake bread is an example of rule-based behavior. The rules are “if-then” associations between cue sets and the appropriate actions. For example, Figure 7.3 shows how the operator might interpret the meter reading as a sign. Given that the procedure is to reduce the flow if the meter is above a set point, the operator then reduces the flow. Symbols and knowledge-based behavior. When the situation is new, people do not have any rules stored from previous experience to call upon, and do not have a written procedure to follow. They have to operate at the knowledge-based level, which is essentially analytical processing using conceptual information. After the person assigns meaning to the cues and integrates them to identify what is happening, he or she processes the cues as symbols that relate to the goals and decides on an action plan. Figure 7.3 shows how the operator might reason about the low meter reading and think about what might be the reason for the low flow, such as a leak. It is important to note that the same sensory input, the meter in Figure 7.3, for example, can be interpreted as a signal, sign, or symbol. The relative role of skill-, rule-, and knowledge-based behavior depends on characteristics of the person, the technology, and the situation [354, 361]. Characteristics of the person include experience and training. As we will see people can be trained to perform better in all elements of macrocognition; however, as with most human factors interventions, changing the task and tools is more effective.In the following sections, we first discuss the cognitive processes in decision making: how it too can be described by stages, the normative approach to decision making (how it “should” be done to produce the best outcomes), and the reasons why people often do not follow the normative decision making processes. Two important departures from normative decision making, receive detailed treatment: naturalistic decision making and heuristics and biases. Because decision errors produced by the heuristics and biases can be considered to represent human factors challenges, we complete our treatment of decision making by describing several human factors solutions to mitigate decision errors. Finally, our chapter concludes by describing four “close cousins” of decision making within the family of macrocognitive processes: situation awareness, troubleshooting, planning and metacognition.