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In contrast suppose you are late for a job interview across town. You can speed, with a high chance of getting to the appointment on time, but also incurring the risk of getting caught by the police, fined, and be very late for the appointment. Alternatively you can choose to drive the speed limit, and certainly be slightly late. Here the choice is between two negatives, a risky one and a sure thing. You are “caught between a rock and a hard place”, and under such circumstances people tend to be risk-seeking. [413, 410]. The second of these contexts, the negative frame of choice, is often characteristic of real life decisions. For example, in addition to the speeding choice above, consider a company with major safety violations in its plant. Management can choose to invest heavy funding into addressing them through new equipment, hiring safety consultants, and pulling workers off the line for safety training, thus incurring the sure loss of time and money. Alternatively they can chose to take the risk that there will be neither a serious injury nor a surprise inspection from federal safety inspectors. All too-often, the framing bias will lead to an inclination toward the second option, at the expense of worker safety. A direct expression of this form of the framing bias is known as the sunk cost bias [414, 415]. This bias affects individual investors who hesitate to sell losing stocks (a certain loss), but tend to sell winning stocks to lock in a gain. Likewise, when you have invested a lot of money in a project that has “gone sour”, there is a tendency to keep it in the hopes that it will turn around. Similarly, managers and engineers tend to avoid admitting a certain cost when replacing obsolete equipment. The sunk cost bias describes the tendency to choose the risky loss over the sure one, even when the rational, expected value choice should be to abandon the project. Because people tend to incur greater risk in situations involving losses, decisions should be framed in terms of gains to counteract this tendency. Y Sunk cost bias makes it difficult for you to make money in the stock market. 7. Default heuristic. Faced with uncertainty regarding what choice to make people often adopt the default alternative [416]. Most countries use their drivers’ licenses to allow people to specify whether to donate their organs or not in the event of a fatal crash. Countries differ according to whether people need to opt in and decide to donate, or opt out and decide not to donate. Over 70% people follow the default and let the designers of the form decide for them. A similarly large effect is seen for people choosing to enroll in a retirement savings plan or having to opt out. Defaulting people into a retirement plan increased participation from about 50% to about 90% [417, 418].

7.4.2 Benets of Heuristics and the Cost of Biases The long list of decision-making biases and heuristics above may suggest that people are not very effective decision makers in everyday situations, and might suggest that human contributions to decision making are a problem that should be fixed. However, this perspective neglect the fact that most people do make good decisions most of the time, and have the flexibility to deal with situations that can’t be reduced to an equation. The list of biases accounts for the infrequent circumstances, like the decision makers in the Three Mile Island nuclear plant, when decisions produce bad outcomes. One reason that most decisions are good, is that heuristics are accurate most of the time. A second reason is that people have a profile of resources: information-processing capabilities, experiences, and decision aids (e.g., a decision matrix) that they can adapt to the situations they face. Experts are proficient in adjusting their decision strategies. To the extent that people have sufficient resources and can adapt to them, they make good decisions. When people are not able to adapt, such as where people have little experience with the situations, poor decisions can result [357]. The focus can be either on the general high quality of most decisions, or on the errors due to biases associated with heuristics. Both of these approaches are equally valid, but focusing on the errors supports the search for human factors solutions to eliminate, or at least mitigate those biases that do show. It is to this that we now turn. 7.4.3 Principles for Improving Decision Making Decision making is often an iterative cycle in which decision makers are often adaptive, adjusting their response according to their experience, the task situation, cognitive ability, and the available decision-making aids. It is important to understand this adaptive decision process because system design, training, and decision aids need to support it. Attempts to improve decision making without understanding this process tend to fail. In this section, we briefly discuss some possibilities for improving human decision making: task redesign, including choice architecture and procedures; training; displays; and automated decision support systems. Task redesign. We often jump to the conclusion that poor performance in decision making means we must do something “to the person” to make him or her a better decision maker. However, sometimes a change in the system can support better decision making, eliminating the need for the person to change. As described in Chapter 1, decision making may be improved by task design. Changing the system should be considered before changing the person through training or even providing a computer-based decision aid. For example, consider the situation in which the removal of a few control rods led to a runaway nuclear reaction, which resulted in 3 deaths and 23 cases of exposure to high levels of radioactivity. Learning from this experience, reactor designers now create reactors that remain stable even when several control rods are removed [227]. Creating systems with greater stability leaves a greater margin for error in decisions and can also make it easier to develop accurate mental models. Choice architecture. The structure of the interaction influences choice in much the same way architecture of a building influences the movement of people through buildings [18]. Choice architects influence decisions by recognizing the natural cognitive tendencies we have discussed and presenting people with information and options that will take advantage of these tendencies to generate good decisions. The following principles show how choice architecture can nudge people towards decisions [419]. 1. Limit the number of options. Because too many options place a high burden on the decision maker, the number of options should be limited to the fewest number that will encourage exploration of options. Although the appropriate number depends on the specific elements of the decision maker and situation, four to five options where none is better on all dimensions. Fewer options should be offered if decision makers are less capable, such as older people, those in a time pressured situation, or less numerate decision makers faced with numerical options [420, 419]. 2. Select useful defaults. The effect of defaults on organ donation rates demonstrates the power of defaults: People often choose default options. Options for designing defaults include random, uniform choice for all users, forced choice, persistent default where the system remembers previous settings, and predictive default where the system picks based on user characteristics. If there is no time pressure and the choice is important then active choice should be used. If there is an obvious benefit to a particular choice then a uniform default for all users should be used, such when organizations select double-sided printing as the default [421]. As laptops, tablet and desktop computers, as well as phones, TVs and cars become more integrated predictive defaults become more feasible and valuable.

3. Make choices concrete. People focus on concrete immediate outcomes and tend to be overly optimistic about future regarding available time and money. To counteract people’s tendency to neglect the abstract future situation a limited window on opportunity can focus their attention like: “offer ends midnight tonight.” Another approach is to translate the abstract future value choices into immediate, salient consequence. For example, show people their future self so they can invest for that future self [422]. People who saw realistic computer renderings of older version of themselves invested more.

4. Create linear, comparable relationships. People tend to struggle to consider complex transformations and non-linear relationships. Transforming variables to their concrete linear equivalent promotes better decisions. For example, describing interest rates in terms of the number of payments to eliminate debt in three years is more effective than expecting people to calculate the non-linear, compounding effect of interest. Likewise, presenting fuel economy data in terms of gallons per 100 miles rather than miles per gallon, eliminates the mental transformation that is needed to compare vehicles [423]. The units presented should be those directly relevant to the decision.

5. Sequence and partition choices. The sequence and grouping of choices includes decisions: People are more likely to choose defaults if they first have to select an option from many choices. Creating categories influences distribution of selections: People are biased towards an even distribution across categories. This general tendency guides the selection of investment options in retirement funds, and so options should be grouped to avoid biasing investors towards creating a risky portfolio simply because there are more categories of risky funds. When presenting food choices aggregating unhealthy options into one category and disaggregating healthy options will lead people to pick more of the healthy options. Proceduralization. Procedures and checklists can make decisions more consistent and accurate [424]. This may include for example prescriptions of following the decision decomposition steps of multiattribute utility theory. Such a technique has been employed successfully in certain real world decisions which are easily decomposable into attributes and values, such as selecting the location of the Mexico City airport [425], or coordinating environmental and energy policy [426]. The formal representation of fault tree and failure modes analysis [53], is a procedure that can assist the decision-maker in diagnosing the possibility of different kinds of system failures. A study of auditors has recommended a procedure by which evidence, accumulated by a junior auditor, is compiled and presented to a senior auditor who makes decisions, in such a way as to avoid the sequential biases often encountered in processing information [427]. One widely used procedural approach has been designed to support the traditional “decision-analysis” cognitive process of weighing alternative actions. This method is popular with engineers and business managers and uses a decision table or decision matrix. It supports the normative multiattribute utility theory described at the start of this chapter and in Chapter 2. Decision tables are used to list the possible outcomes, probabilities, and values of the action alternatives. The decision maker enters estimated probabilities and values into the table. Computers are programmed to calculate and display the utilities for each possible choice. A decision table is helpful because it reduces the working-memory load. By deflecting this load to a computer, it encourages people to consider the decision space more broadly. More generally, tools for multiattribute utility theory succeed not by making the decision for people, but by helping people think through the decision [428]. Decision trees are useful for guiding decisions that involve evaluating information in a sequence to make a decision. With this method, a branching point is used to represent the decision alternatives; this is followed by branching points for possible consequences and their associated probabilities. This sequence is repeated as far as necessary to make the decision, so the user can see the overall probability for each entire action-consequence sequence. An important challenge in implementing this technique is user acceptance [429]. The step-based approach is not how people typically make decisions, and so it can seem foreign. However, for those tasks where choices involve high risk and widely varying probabilities, such as cancer diagnosis, it can be worth training people to be more comfortable with this type of aid. Figure 7.8 shows a fast and frugal decision tree [430], which provides a fast and understandable way to guide decisions. The defining feature of these trees is that each branch leads to a decision. In the figure, two physiological indicators of a possible cancer diagnosis are considered: “cellsize” and “cellshape.” The first node of the tree indicates that a person likely is healthy if “cellsize” is less than or equal to 2. If “cellsize” is greater than 2, then “cellshape” is considered, and the second node indicates that if “cellshape” is greater than or equal to 2, that a person likely has cancer.

Figure 7.8 Fast and frugal decision tree for cancer diagnosis. The tree is fast and frugal because it provides a diagnosis after considering each indicator (e.g., cellsize). (Based on the Wisconsin Breast Cancer Database and created with R package FFTrees [431].)

Fast and frugal decision trees have helped caregivers direct people suffering from chest pain and other symptoms of a heart attack towards the appropriate care (i.e., coronary care unit or a regular bed). The tree reduced the number of people not suffering from a heart attack from mistakenly sent to the coronary care unit. Likewise, it reduced the number of heart attack victims wrongly turned away from the coronary care unit [378]. Decision trees are also easy to use and more understandable than a logistic regression model that performed similarly well from a statistical perspective.

Training decision making. Pure practice does not necessarily lead to better decision making, because of the poor feedback offered in many naturalistic decision environments, such as health care, or legal decisions, where the feedback is often delayed, missing, or incorrect. However in a relatively predictable environment, with reliable cues, extensive practice and expertise can lead to relatively rapid and accurate diagnosis, in the process described earlier as recognition primed decision making [374]. The conditions and job environments that possess such cues are well articulated by Kahneman [351], as well as those which are not. And which the findings seem to indicate that expertise does not produce better decision making.

Training has a mixed record of success in improving decision making. For example, training decision makers on how to use some of the procedures and tools, such as decision trees discussed in the previous section, can improve decisions. Training and instructions to remove or reduce many of the biases discussed above, a technique known as debiasing [432], has mixed results. Instructions or exhortations to avoid biases are ineffective [361]. Similarly, simply teaching people about biases (e.g., reading this chapter) is only moderately effective. Education may produce inert knowledge which can be understood, but not transferred to practice. Instead, effective techniques focus not only on instructing the nature of a particular bias in a particular context [346], but also providing feedback to show how better outcomes are produced when the trained strategy is followed, and worse outcomes when it is not. The following are some specific examples of success. Hunt and Rouse [433] trained operators to extract diagnostic information from the absence of cues. Some success in reducing the confirmation bias has also been observed by the training strategy of “consider the opposite” [434], such as forcing forecasters to entertain reasons why their forecasts might not be correct reduced their biases toward overconfidence in the accuracy of their forecasts [435]. Also successful is a training aid that provides more comprehensive and immediate feedback, so that operators are forced to attend to the degree of success or failure of their rules. We noted that the feedback given to weather forecasters is successful in reducing the tendency for overconfidence in forecasting [436]). Similarly people think of events in terms of probability rather than frequency because probabilities account for events that did not occur (negative evidence) as well as those that did [354].

Perhaps the most effective training approach for decision making is termed the pre-mortem. Rather than examining the reasons why a decision was poor after the damage has been done—postmortem analysis—the pre-mortem analysis encourages decision makers to consider everything that might go wrong before if the candidate decision was made [437]. Displays. There is good evidence that displays can influence the front end of decision processes (cue integration and diagnosis), by guiding selective attention. Items at the start and end of a menu receive more attention and are ordered more frequently [438]. Pictorial representations of risk data led people to decisions that reflects a more calibrated sense of risk than did numerical or verbal statements [439]. Similarly, loan application information structured as a list was perceived as more demanding than information structured as a matrix. This perceived demand influenced judgments, suggesting that people minimized the amount of attentional effort required for information integration [440]. Cook and Smallman [441] found that an integrated graphical display of intelligence cues shown to professional intelligence analysis reduced the confirmation bias, relative to a text-based presentation which implicitly suggested a sequential ordering and so invited sequential biases.