Decision Making and Intuition
As already discussed, the rational decision-making process includes a sequence of five steps. We also noted, however, that researchers have highlighted the potential influence of bounded rationality on this process. More recent research has suggested that individuals may also rely on additional processes when making decisions. In fact, Stanovich and West suggest that individuals use two different processes when making decisions.28 According to their framework, the rational decision-making process discussed in the previous section is known as “System 2.”
Complementing this formal system of decision making, Stanovich and West suggest that individuals also rely on a less formal process based on intuition to make decisions; they refer to this process as “System 1.” Consistent with their framework, System 2 is a process described as being slow, comprehensive, and deliberate, whereas System 1 is described as being fast, automatic, and intuitive. Intuition, in fact, refers to an individual’s inborn ability to synthesize information quickly and effectively.29 Taken together, some researchers suggest that individuals employ the more sophisticated System 2 process to monitor or override the more automatic System 1 process. Often, however, System 2 does not monitor System 1 effectively; in such cases, intuition drives decision making.
Decision-Making Heuristics and Biases
Daniel Kahneman and Amos Tversky were awarded the Nobel Prize for further examining the role of intuition in decision making. In particular, their ground-breaking research examined how individuals use heuristics, or simple rules of thumb, to make decisions. In addition, Kahneman and Tversky examined how these heuristics introduce bias in decision-making processes. Bias refers to departures from rational theory that produce suboptimal decisions. In other words, when managers rely on rules of thumb when making decisions, their decisions are often flawed. Kahneman and Tversky’s work spurred a great deal of interest in the discovery and examination of a number of decision-making biases. Researchers have discovered many other decision-making biases; Table 6.2 summarizes some of the more prominent biases examined by decision-making researchers.
Table 6.2 Common Decision-Making Biases30
| Name of Bias | Brief Description |
|---|---|
| Bandwagon Effect | The tendency to believe certain outcomes will occur (i.e., the stock market will increase) because others believe the same thing |
| Confirmation Bias | The tendency to search for information that supports one’s preconceived beliefs and to ignore information that contradicts those beliefs |
| Loss Aversion | Characteristic of individuals who tend to more strongly prefer avoiding losses rather than acquiring gains |
| Overconfidence | When assessing our ability to predict future events, the tendency to believe that our forecasts are better than they truly are |
| Unrealistic Optimism | Individuals’ tendency to believe that they are less susceptible to risky events (i.e., earthquakes, disease transmission, etc.) than others |
Decision-Making Conditions: Risk and Uncertainty
In most instances, it is impossible for decision makers to know exactly what the future consequences of an implemented alternative will be. The word future is the key in discussing decision-making conditions. Because organizations and their environments are constantly changing, future consequences of implemented decisions are not perfectly predictable. In general, the two different conditions under which decisions are made are risk and uncertainty. Although many managers use them interchangeably, these two terms are in fact different.
Frank Knight distinguished between risk and uncertainty almost a century ago.31 According to his framework, risk refers to situations in which statistical probabilities can be attributed to alternative potential outcomes. For example, the probabilities associated with the potential outcomes of roulette are known to individuals in advance. In contrast, uncertainty refers to situations where the probability that a particular outcome will occur is not known in advance. A manager, for instance, may be unable to articulate the probability that building a new manufacturing facility will increase a firm’s sales in five years.32
Despite this distinction between risk and uncertainty, it is important to note that objective standards are not always available when examining a situation with alternative potential outcomes. Specifically, two managers may attribute differing levels of uncertainty or risk to the same or similar decisions. For example, suppose that the managers of two competing firms—Alpha Inc. and Beta Inc.—are each considering opening new manufacturing facilities in China but are unsure whether the new plants will improve the firms’ profitability. Suppose, however, that the manager of Alpha Inc. has previously opened 12 new facilities in China, but the manager of Beta Inc. has no such experience. As such, the manager of Alpha Inc. has more information about opening these plants and might be able to better estimate the risk probabilities associated with profitability versus failure as compared to the manager of Beta Inc. In fact, the manager of Beta Inc. might not be able to estimate any risk probabilities but instead must view this plant with complete uncertainty.
Now that we have distinguished between risk and uncertainty, the question remains: Why do we need to distinguish between these two terms? Research suggests that individuals dislike uncertainty even more than they dislike risk.33 Vague or unknown probabilities of success are more likely to discourage managers from undertaking actions than is risk. This negative influence of uncertainty has implications for all sorts of decisions such as hiring new employees, introducing new products, or acquiring other firms.
MyManagementLab : Watch It, Decision Making at Southwest Airlines
If your instructor has assigned this activity, go to mymanagementlab.com to watch a video case about Southwest Airlines and answer the questions.