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.

中文翻译：

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政治强化学习者


政治似乎是人类最精于算计却又最不理性的元素的家园。我们如何系统地描述这一政治认知谱系的特征？在这里，我们提出强化学习（RL）作为剖析政治思维的统一框架。强化学习描述了智能体如何通过算法导航政治等复杂且不确定的领域。通过这个计算镜头，我们概述了导致政治差异的三种途径，这些差异源于主体对问题的概念、用于解决问题的认知操作或环境中可用信息的背景的变化。对政治思维弊病的计算优势提高了评估其原因、后果和治疗方法的准确性。