What is decision under uncertainty?

Daniel Kahneman, answered from the texts and cited to the page.

Daniel Kahneman answers

Decision under uncertainty is the problem of choosing when you do not know what will happen — and the central question is whether people's choices follow any systematic logic, rational or otherwise. The way Amos and I approached it was deliberately simple. We studied how people respond to elementary gambles: would you accept a bet on a coin toss where you win $130 if it comes up heads and lose $100 if it comes up tails?¹

That is not a complex financial instrument; it is a stripped-down case designed to reveal the underlying machinery of preference. The simplicity was the point. If you can show that people's intuitive preferences violate the rules of rational choice even in cases this clean, the finding is hard to explain away. What we found, examining our own choices as much as anyone else's, was that the violations were systematic.¹

They were not random noise. They had structure — which meant they could be theorized. That is what prospect theory was built to do: describe the preferences people actually have, not the preferences a rational agent is supposed to have.² A large part of what drives the errors in judgment that feed into decisions is the use of heuristics — mental shortcuts that substitute an easier question for the harder one being asked.

Representativeness is the clearest case: when people are asked how probable it is that some individual belongs to a particular category, they answer instead how similar that individual seems to the stereotype of that category.³ Base rates — the actual frequencies in the population — are largely ignored, because they have no effect on similarity even though they should have a major effect on probability.⁴

In one experiment where subjects were told that a group of 100 professionals consisted of either 70 engineers and 30 lawyers, or 30 engineers and 70 lawyers, the probability judgments barely shifted between conditions — a sharp violation of Bayes' rule, since the correct odds ratio between the two conditions should be roughly 5.44 for any given description.⁵

So decision under uncertainty is not simply a matter of calculating expected values. It is a problem that passes through a mind equipped with heuristics that sometimes serve well and quite often do not.⁶

Sources

1. Would you accept a bet on the toss of a coin where you win $130 if the coin shows heads and lose $100 if it shows tails? These elementary choices had long been used to examine broad questions about decision making... we observed systematic biases in our own decisions, intuitive preferences that consistently violated the rules of rational choice.

   Thinking, Fast and Slow

2. Five years after the Science article, we published 'Prospect Theory: An Analysis of Decision Under Risk,' a theory of choice that is by some counts more influential than our work on judgment, and is one of the foundations of behavioral economics.

   Thinking, Fast and Slow

3. In the representativeness heuristic, the probability that Steve is a librarian, for example, is assessed by the degree to which he is representative of, or similar to, the stereotype of a librarian. Indeed, research with problems of this type has shown that people order the occupations by probability and by similarity in exactly the same way.

   Judgment under Uncertainty, Heuristics and Biases, p. 2

4. One of the factors that have no effect on representativeness but should have a major effect on probability is the prior probability, or base-rate frequency, of the outcomes... Considerations of base-rate frequency, however, do not affect the similarity of Steve to the stereotypes of librarians and farmers.

   Judgment under Uncertainty, Heuristics and Biases, p. 2

5. In a sharp violation of Bayes' rule, the subjects in the two conditions produced essen—" [the ratio of odds should be (.7/.3)², or 5.44, for each description]

   Judgment under Uncertainty, Heuristics and Biases, p. 2

6. people replace the laws of chance by heuristics, which sometimes yield reasonable estimates and quite often do not.

   Judgment under Uncertainty, the Kahneman and Tversky Chapters, p. 30