A Study on Five Cognitive Biases
Traditionally, the studies examining heuristics and biases in decision-making have used experimental designs to demonstrate violations of rationality. The objective of this study was to perform the replication of five classic cognitive biases with an alternative form of measurement. The problems and...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/230602 |
| Acceso en línea: | http://hdl.handle.net/11336/230602 |
| Access Level: | acceso abierto |
| Palabra clave: | COGNITIVE BIASES HEURISTICS THINKING BIASES https://purl.org/becyt/ford/5.1 https://purl.org/becyt/ford/5 |
| Sumario: | Traditionally, the studies examining heuristics and biases in decision-making have used experimental designs to demonstrate violations of rationality. The objective of this study was to perform the replication of five classic cognitive biases with an alternative form of measurement. The problems and the response scales were adapted from the experimental tasks performed by Stanovich and West (2008) to measure five cognitive biases: base-rate, conjunction, framing, anchoring, and outcome. It is a quantitative study with a cross-sectional experimental design. The set of problems was applied to a sample of 440 participants, 72% of women (M age = 21.3, SD = 4.05). The comparison of the average scores of each pair of problems yielded a response form compatible with the predictions inferred from the theory of cognitive biases. In addition, it was possible to replicate the results of the experimental procedures on which this study was based. Future research should aim to determine personal and situational variables associated with different thinking biases and to develop interventions for eliminating these biases, thus optimizing performance in areas where the cost of errors may be too high. |
|---|