1. Political reinforcement learners.
- Author
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Schulz, Lion and Bhui, Rahul
- Subjects
- *
MACHINE learning , *REINFORCEMENT learning , *INTERGROUP relations , *POLITICAL psychology , *PROBLEM solving - Abstract
Fault lines in our cognition are frequently tied to major societal issues, from belief in misinformation to intergroup conflict. However, we lack a unified formal framework that lets us understand the political animal's varied beliefs and behaviors. Political cognition can be succinctly characterized using the computational framework of reinforcement learning (RL), which describes the algorithms we use to acquire, process, and act upon information. Political differences may arise from divergent conceptions of the world, diverse and imperfect algorithms for learning and decision-making, or experience that leads agents astray. RL offers a unifying computational language to investigate the roots and remedies of political dysfunction. 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. 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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