with a lottery ticket that has a 50 percent chance of winning $20,000 might very rationally trade that ticket for $9,000. On average, the lottery ticket he holds is worth $10,000, but he might choose not to take the risk. Of course how little he is willing to accept for the ticket reflects just how risk averse he is. Someone who is very risk averse might settle for a sure gain of $1,000 in return for the ticket; someone who is tolerant of risk might refuse even $9,900. The important point is that there is nothing suboptimal about any of these decisions for Bernoulli’s poor man. These different decisions reflect different tolerances for risk that might affect the suitability of an individual for a specific job, but scientists (unlike mission planners) must be silent on notions of optimality with regard to risk tolerance. Of course it may be that individuals who are more risk averse are better suited for peacekeeping missions and individuals who are more risk tolerant are better suited for frontline combat missions, but different tolerances for risk, from the point of view of optimality theory, simply represent different approaches.

The assessment of risk by individuals, however, is an area where the tools of optimal decision making can be brought to bear. Consider a commander ordered specifically to “minimize vehicle losses.” Real-life situations seldom provide clear-cut alternatives for which risks and benefits can be estimated with a high degree of certainty. But suppose that a commander has a choice between two plans for committing 500 vehicles to an operation and that his staff has prepared the following best estimates of probable losses:

  • For plan 1, there is a 2 percent chance that 50 vehicles will be lost.

  • For plan 2, there is a 50 percent chance that 4 vehicles will be lost.

From a decision theoretic point of view, given the goal of minimizing vehicle losses, plan 1 is preferable. If 100 commanders face this decision, all choose plan 1, and the probability estimates are accurate, the losses would be half what they would have been if all had chosen plan 2.1 Despite the logic behind the probability estimates, behavioral research indicates unambiguously that most people would choose plan 2. For behavioral scientists, this preference reflects a standard feature of decision making, subjective probability distortion (Kahneman and Tversky, 1979). It is now abundantly clear that human decision makers see very small probabilities as much larger than they actually are (for an accessible review, see Plous, 1993). The financial industry is now beginning to take this feature of human behavior into account. Some financial firms now provide their decision makers with training and tools that help them overcome this widespread inefficiency in decision making.

Over the past decade, the neurophysiological roots of this kind of behavior have begun to be understood. It seems likely that this enhanced understanding of the mechanisms of decision making will shed new light on the sources of suboptimal decisions. We now know, for example, that structures in the basal ganglia and the prefrontal cortex (PFC) provide valuations for actions, and these valuations appear to be passed to the posterior parietal cortex, among other areas, for decision (see, for example, Glimcher et al., 2005). In fact, recent research has even begun to identify the neural algorithms that lead to some of these inconsistencies (Glimcher et al., 2007).

Loss Aversion in Decision Making

Another factor that leads to seriously suboptimal decision making is the asymmetry with which human decision makers typically weigh risky losses versus risky gains (see Ariely, 2008, and Plous, 1993). Consider a gambler who has $1,000 in his pocket and is offered a $100 bet that has a 1 in 20 chance of paying off $500. With the money in his pocket and the night’s action still to come, he reasonably refuses this highly improbable bet. Later on, having lost $10,000 on credit, he might well accept that same bet. Contemporary studies of decision making suggest that this reflects a change in his risk tolerance. As losses accumulate, decision makers typically become more and more risk tolerant, even to the point of becoming risk seekers. In the financial world, this behavior pattern has led to a number of catastrophic financial events and even to bank failures. Whether the pattern occurs in combat has not been studied formally, but the constancy of human nature and the annals of military history (e.g., Napoleon at Waterloo) suggest that it does occur, with ominous consequences.

Over the past year, the neural basis of this phenomenon has been identified. This finding has broad implications for understanding loss aversion as a source of suboptimal decision making. Before the neurological studies were conducted, the widespread conviction was that loss aversion was a discrete fear response that biased other decision-making mechanisms. The neurophysiological evidence weighs against this view, instead suggesting that a unitary valuation system unrelated to fear drives loss-averse behavior (Tom et al., 2007)

MAKING OPTIMAL USE OF INDIVIDUAL VARIABILITY

The preceding section discussed the evidence that nearly everyone, under some conditions, makes decisions that are less than optimal for achieving the organizational goals set for them. Irrespective of the type of suboptimal decision making, individuals differ in how they make decisions. For example, some individuals are more impulsive than others and some are more tolerant of risk. Such differences do not necessarily mean that risk-tolerant individuals are better

1

For plan 1, two commanders are likely to lose 100 (2 × 50) vehicles; for plan 2, fifty commanders are likely to lose 200 (50 × 4) vehicles.



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