Working Paper: NBER ID: w31631
Authors: Pedro Bordalo; John J. Conlon; Nicola Gennaioli; Spencer Yongwook Kwon; Andrei Shleifer
Abstract: We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis by attending “bottom up” to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used, others are neglected. The model unifies biases in judgments about i.i.d. draws, such as the Gambler’s Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions about how changes in the salience of specific features should jointly shape the prevalence of these biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Bottom-up attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of the Bayes rule.
Keywords: cognitive biases; statistical reasoning; attention; decision-making
JEL Codes: D01; D91; G41
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| salience (D91) | judgments (K41) |
| individual flip (D19) | overestimation of balanced sequences (C51) |
| changes in salience (D91) | instability in judgments (D91) |
| salience (D91) | biases (D91) |
| salience (D91) | multimodal distributions of answers (C46) |