Base rate neglect is when humans ignore background frequencies when making probability inferences, leading to biases. It is part of heuristics and biases and descriptive decision theory. Bayes’ rule can help integrate base rates for accurate probabilities. This occurs in everyday life with implications for society.
Background rate neglect is a term used in cognitive psychology and decision sciences to explain how human reasoners, when making inferences about probability, often tend to ignore background frequencies. For example, if the probability that a given woman has breast cancer is known to be 1/10,000, but a test of 10,000 women gives 100 positive results, reasoners will tend to overestimate the probability that any of the women who test positive actually have cancer. , rather than considering the possibility of false positives.
Base rate neglect analysis is relatively recent within psychology, often considered a part of the field of heuristics and biases. Rather than assuming that humans are always rational thinkers, psychologists in this field explore the ways in which human judgments systematically deviate from the axioms of probability theory. These biases occur because humans are often forced to make quick judgments based on little information, and because the most adaptable or quickest judgments are not always the most correct. It appears that our species was not created by evolution to consistently produce mathematically accurate inferences based on a set of observed data.
The phenomenon of base rate neglect is also considered a part of descriptive decision theory, which studies how humans actually reason, in contrast to normative decision theory, which studies the best possible procedures for making a given decision. It has been found that human reasoners often ignore the base rate even when the information is readily available. This has yielded important results for the social sciences and economics, among other areas.
Base rate neglect is often mentioned in conjunction with Bayes’ rule, a decision-making procedure that follows rapidly from the axioms of probability theory. This rule demonstrates how to properly integrate base rates into new observations to provide consistently and accurately updated probabilities. Therefore, the deviation from the base rates is also referred to as the deviation from the Bayes rule.
Another example of base rate dropout in an experimental setting would be presenting a group of test subjects with a list of ten students and descriptions of their habits and personalities. The presentation is followed by a question about what grade point average a student is likely to have. This information is presented alongside baseline rate information on students’ academic achievement, which is supposed to guide test subjects in their guesses, but regularly does not. Given ten poor student descriptions, test subjects will assign GPA estimates that are substantially out of line with baseline rates. Given ten positive descriptions, the bias occurs in the opposite direction. Presumably these biased estimates of probabilities occur every day in billions of human minds, with substantial implications for the way our society operates.
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