• Contents
• Rational Choice Theory
• Cognitive Bias Research Using Atypical Examples to Prove Systemic Bias
• Decision-Making Under Uncertainty
• Natural Decision Making
• Conclusion

Contents

For one, whereas the bulk of decision research studies how undergraduates solve toy problems they had never encountered before, Klein studies how professionals solve real-world problems with which they have experience.

Second, these experts make high-stakes decisions under uncertainty, that is, in ill-defined situations that may change in dramatic and unforeseeable ways. Klein’s phrase for these is natural decision making. Contrast this “natural” world with the stock-in-trade of decision research, lotteries and gambles, where everything is certain, including the probabilities, and nothing unexpected can ever happen.

Third, to make good decisions under uncertainty, experts rely on a repertoire of abilities, including intuition, mental simulation, aspiration levels, and storytelling.

Rational Choice Theory

In the words of Savage (1954), known as the father of modern Bayesian decision theory, the theory assumes a small world where the exhaustive and mutually exclusive set of all future states of the world and their consequences are known. A lottery is one example, the game of roulette another. In a small world, rational choice amounts to estimating for each possible action the utility and subjective probability of all its consequences, multiplying these, and summing up the products. The resulting value is the subjective expected utility of an action. Finally, the action with the highest subjective expected utility is deemed the rational choice. There are many versions of this basic theory, but that is not the issue here. The important point is that rational choice theory has a restricted domain. Savage emphasized that its usefulness is limited to small worlds and that it would be “ridiculous” to apply it other problems such as chess (because of intractability, that is, the optimal sequence of moves cannot be computed) or planning a picnic (because of uncertainty, that is, one cannot know ahead all consequences that might happen) (Savage, 1954, p. 16).

...The problem here is not rational choice theory per se but rather the widespread yet mistaken belief that the theory can provide a universal norm for all rational behavior. Klein’s second and related target of critique is the heuristics-and-biases program. According to him, “heuristics and biases do not occur in experienced decision makers working in natural settings” (2018, p. 274). Although this program agrees with Klein that people’s behavior systematically deviates from rational choice theory, it differs with him by nevertheless upholding the theory as a universal norm: If people’s choices differ from rational choice theory, the heuristics-and-biases program attributes this deviation to flaws in the human mind rather than in the theory.

Cognitive Bias Research Using Atypical Examples to Prove Systemic Bias

Klein (2018, pp. 274–275) argues that testing participants on selected, uncharacteristic cases in order to demonstrate that they have systematic biases amounts to nothing less than a confirmation bias in research.

By using selected, atypical test items, one can always make people appear systematically biased.

quite a few other socalled cognitive illusions have been shown to be due to a “bias bias” in researchers’ thinking, namely the tendency to spot biases even when there are none (Gigerenzer, 2018).

Decision-Making Under Uncertainty

Klein defines uncertainty by a list of features, such as high-stakes decisions with time pressure where information is unreliable, ambiguous, or missing, and where goals may be ill-defined (2018, p. 280): - High-stakes decisions are those in which lives are in danger. - Ill-defined goals are those where the problem that needs to be solved may be initially unclear. For instance, fireground commanders need to find out whether the goal is to extinguish the fire, or whether the fire is so strong that the goal is to prevent it from spreading further. In the psychological literature, by contrast, the term uncertainty is often misleadingly used for risk (where you can predict the likelihood of a future outcome), wrongly suggesting that all problems can be solved by logic and probability theory. Once again, statisticians and economists have long since reminded us to take the distinction between situations of risk (such as small worlds) and uncertainty seriously (Knight, 1921). Klein indeed takes uncertainty seriously. He also recognizes that decision making under uncertainty requires other tools than decision making under risk.

Natural Decision Making

In Klein’s view, experts work not merely with one strategy such as utility maximization or Bayes’ rule but with a repertoire of abilities. These include expert intuition, mental simulation, metaphor, and storytelling.

Undoubtedly the most striking observation—from the point of view of anyone trained in rational choice theory—is that experts rarely make choices.

Anyone who has taken a course in decision research, however, is likely to have been told that experienced people carefully compare options, while novices jump on the first one that comes to mind. According to Klein, it’s the other way around. He reports that the experts he studies, such as firefighters and emergency room physicians, rarely compare options (2018, p. 24). Rather, based on their experience, a single option comes to mind. An expert may follow it immediately or mentally simulate the option, imagining it being carried out. If this simulation does not lead to the desired goal, then the same process is repeated with the second option that comes to mind, and so forth. Several options may be considered but are not compared; that is, options are evaluated one-by-one until one is found to be good enough. And if the situation changes because, for example, the fire has spread, this process is started again.

Conclusion

We are primarily rational, not irrational.