Political reinforcement learners.

Schulz, Lion; Bhui, Rahul

Schulz, Lion; Bhui, Rahul.

Afiliación

Schulz L; Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max-Planck-Ring 8-14, 72076 Tübingen, Germany. Electronic address: lion.schulz@tue.mpg.de.
Bhui R; Sloan School of Management and Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA.

Trends Cogn Sci ; 28(3): 210-222, 2024 03.

Article en En | MEDLINE | ID: mdl-38195364

ABSTRACT

ABSTRACT

Politics can seem home to the most calculating and yet least rational elements of humanity. How might we systematically characterize this spectrum of political cognition? Here, we propose reinforcement learning (RL) as a unified framework to dissect the political mind. RL describes how agents algorithmically navigate complex and uncertain domains like politics. Through this computational lens, we outline three routes to political differences, stemming from variability in agents' conceptions of a problem, the cognitive operations applied to solve the problem, or the backdrop of information available from the environment. A computational vantage on maladies of the political mind offers enhanced precision in assessing their causes, consequences, and cures.

Asunto(s)

Aprendizaje; Refuerzo en Psicología; Humanos; Cognición; Política

Palabras clave

computational models; political psychology; reinforcement learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Refuerzo en Psicología / Aprendizaje Límite: Humans Idioma: En Revista: Trends Cogn Sci Asunto de la revista: PSICOLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido

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