Associative Learning and Representativeness
59 Pages Posted: 12 Jun 2020 Last revised: 24 Jun 2020
Date Written: June 23, 2020
The representativeness heuristic constitutes constitutes a striking departure from Bayesian inference. 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 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 consequences for the real economy. However, the cognitive foundations of representativeness heuristic (RH) remain unknown. Here, we show that the RH emerges from a model of associative memory, leading to a cognitive foundation for the RH, and a means of integrating the RH into economic models involving decision-making under uncertainty.
Keywords: Context, Memory, Diagnostic expectations
JEL Classification: E03, G02
Suggested Citation: Suggested Citation