The hot stove effect
People revisit the restaurants they like and avoid the restaurants with which they had a poor experience. This tendency to approach alternatives believed to be good is usually adaptive but can lead to a systematic bias. Errors of underestimation (an alternative is believed to be worse than it is) wi...
| Autores: | , |
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| Tipo de recurso: | capítulo de libro |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/59812 |
| Acceso en línea: | http://hdl.handle.net/10230/59812 http://dx.doi.org/10.1017/9781009002042.005 |
| Access Level: | acceso abierto |
| Palabra clave: | sampling negativity bias Hot Stove Effect learning Bayesian models rationality exploration |
| Sumario: | People revisit the restaurants they like and avoid the restaurants with which they had a poor experience. This tendency to approach alternatives believed to be good is usually adaptive but can lead to a systematic bias. Errors of underestimation (an alternative is believed to be worse than it is) will be less likely to be corrected than errors of overestimation (an alternative is believed to be better than it is). Denrell & March (2001) called this asymmetry in error correction the “Hot Stove Effect.” This chapter explains the basic logic behind the Hot Stove Effect and how this bias can explain a range of judgment biases. We review empirical studies that illustrate how risk aversion and mistrust can be explained by the Hot Stove Effect. We also explain why even a rational algorithm can be subject to the same bias. |
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