James Paron, Michael Kahana, Jessica Wachter, Associative Learning and Representativeness.
Abstract: The representativeness heuristic constitutes a striking departure from Bayesian
updating. According to a strong form of the heuristic, agents reverse
a conditioning argument: for example inferring that a patient is more
likely than not to have a rare disease, conditional on a
positive test result. The correct inference is that a positive test
result is more likely than not, conditional on
disease. Recent research implicates representativeness
in a wide range of financial market anomalies, with potential consequences for
the real economy. However,
the cognitive foundations of the representativeness heuristic (RH) remain
unknown. Here, we show that the RH emerges from a theory of
associative memory and recognition, leading to a cognitive foundation for the RH, and
a means of integrating the RH into economic models involving
decision-making under uncertainty.
