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Procesos y sesgos de aprendizaje por refuerzo humano: caracterización computacional y posibles aplicaciones a
1Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale, Paris, France.
Mind & society
|December 22, 2025
Resumen
Este estudio introduce un marco para comprender los sesgos computacionales en el aprendizaje por refuerzo (AR). Distingue entre sesgos práxicos y epistémicos, ofreciendo información para políticas públicas conductuales.
Área de la Ciencia:
- Ciencia Cognitiva
- Inteligencia Artificial
- Economía Conductual
Sus antecedentes:
- El aprendizaje por refuerzo (AR) modela el aprendizaje del agente a través de recompensas y castigos.
- El AR humano a menudo se desvía de los estándares normativos, mostrando sesgos sistemáticos.
- La investigación existente destaca los sesgos computacionales en el AR.
Objetivo del estudio:
- Proponer un marco conceptual y una taxonomía para evaluar los sesgos computacionales en el AR.
- Distinguir entre sesgos práxicos (errores de selección de acciones) y sesgos epistémicos (errores de representación).
- Caracterizar y discutir la valoración relativa y la actualización sesgada como sesgos epistémicos clave.
Principales métodos:
- Desarrollo de un marco conceptual.
- Creación de una taxonomía para sesgos computacionales.
- Revisión y síntesis de la literatura existente.
Principales resultados:
- Introducción de las categorías de sesgo práxico y epistémico.
- Identificación de la valoración relativa y la actualización sesgada como sesgos epistémicos primarios.
- Discusión de las firmas conductuales y los roles adaptativos de estos sesgos.
Conclusiones:
- Los hallazgos informan los dominios teóricos y aplicados del AR.
- Las intervenciones basadas en el refuerzo se utilizan menos en las políticas públicas en comparación con los impulsos.
- Aboga por la integración del AR en el diseño de políticas públicas conductuales, abordando conceptos erróneos históricos.
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