Daniel Kahneman is an Israeli-American psychologist whose research on human judgment and decision-making under uncertainty has had a profound impact on economics, public policy, medicine, and our understanding of the relationship between human cognition and Bayesian rationality. Working primarily with Amos Tversky, Kahneman documented systematic biases in how people assess probabilities and make decisions, showing that human judgment regularly departs from the normative standard provided by Bayes' theorem and expected utility theory. For this work, Kahneman received the Nobel Memorial Prize in Economic Sciences in 2002.
Life and Career
Born in Tel Aviv, British Mandate Palestine (now Israel). Grows up in France during World War II before returning to Palestine.
Earns his Ph.D. in psychology from UC Berkeley, studying visual perception and attention.
Begins landmark collaboration with Amos Tversky at the Hebrew University of Jerusalem, studying intuitive judgment under uncertainty.
Publishes "Judgment under Uncertainty: Heuristics and Biases" with Tversky in Science, documenting representativeness, availability, and anchoring heuristics.
Publishes "Prospect Theory: An Analysis of Decision under Risk" with Tversky in Econometrica, proposing a descriptive alternative to expected utility theory.
Receives the Nobel Memorial Prize in Economic Sciences for integrating psychological research on judgment under uncertainty into economic science.
Publishes Thinking, Fast and Slow, a bestselling synthesis of decades of research on dual-process theory, heuristics, and biases.
Dies at age 90, leaving an extraordinary intellectual legacy across psychology, economics, and decision science.
Heuristics and Biases
Kahneman and Tversky's research program began with a simple question: how do people actually assess probabilities, and how do their assessments compare to the normative standard of Bayes' theorem? Their answer was that people rely on a small number of mental shortcuts, heuristics, that are often useful but lead to systematic and predictable errors, biases, in specific situations.
The representativeness heuristic leads people to judge probabilities based on how well an observation resembles a category, ignoring base rates. The availability heuristic leads people to judge frequencies based on how easily examples come to mind, biasing estimates toward vivid or recent events. The anchoring heuristic leads people to make estimates by adjusting insufficiently from an initial value. Each of these departs from Bayesian updating in specific, documented ways.
One of Kahneman and Tversky's most striking findings is base rate neglect: when given diagnostic information about an individual, people tend to ignore the prior probability (base rate) of the relevant categories. In the classic cab problem, participants told that a witness identified a cab as Blue in a city where 85% of cabs are Green dramatically overestimate the probability that the cab was Blue, essentially ignoring the base rate. This is a direct violation of Bayes' theorem, which requires the prior probability to be weighted appropriately against the likelihood.
Prospect Theory
Kahneman and Tversky's prospect theory proposed a descriptive model of decision-making under risk that accounts for observed violations of expected utility theory. Its key features are reference dependence (outcomes are evaluated as gains or losses relative to a reference point), loss aversion (losses loom larger than equivalent gains), and probability weighting (people overweight small probabilities and underweight large ones). While prospect theory is primarily about decision-making rather than probabilistic inference, it complements the heuristics and biases research by showing that even when people have correct probability assessments, their choices may still deviate from the predictions of rational choice theory.
System 1 and System 2
In Thinking, Fast and Slow, Kahneman synthesized decades of research into the framework of two cognitive systems. System 1 is fast, automatic, and intuitive, relying on heuristics and pattern recognition. System 2 is slow, deliberate, and analytical, capable of following rules like Bayes' theorem. Many cognitive biases arise because System 1 generates quick intuitive judgments that System 2 fails to correct, either because it is not engaged or because it endorses the intuitive answer. Bayesian reasoning, which requires combining base rates with evidence in a precise mathematical way, is a quintessential System 2 task that System 1 performs poorly.
Relationship to Bayesian Statistics
Kahneman and Tversky's work is deeply relevant to Bayesian statistics in two ways. First, it provides the motivation for formal Bayesian methods: precisely because human intuition is unreliable for probabilistic reasoning, we need the discipline of Bayes' theorem to reason correctly under uncertainty. Second, it raises important questions about the communication and interpretation of Bayesian analyses, since the consumers of statistical results are themselves subject to the biases that Kahneman documented.
"We are prone to overestimate how much we understand about the world and to underestimate the role of chance in events." — Daniel Kahneman, Thinking, Fast and Slow
Legacy
Kahneman's influence extends across psychology, economics, medicine, law, and public policy. His documentation of how human judgment departs from Bayesian rationality has not undermined the Bayesian approach but rather strengthened the case for it: if people cannot intuitively perform Bayesian reasoning, then formal Bayesian methods are all the more necessary for making important decisions under uncertainty. His work stands as both a critique of unaided human judgment and an implicit endorsement of the Bayesian framework as the normative standard against which that judgment should be measured.