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| DOI | 10.1016/j.tics.2023.12.001 |
| Political reinforcement learners | |
| 发表日期 | 2024 |
| ISSN | 1364-6613 |
| EISSN | 1879-307X |
| 起始页码 | 28 |
| 结束页码 | 3 |
| 卷号 | 28期号:3 |
| 英文摘要 | 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. |
| 语种 | 英语 |
| WOS研究方向 | Behavioral Sciences ; Neurosciences & Neurology ; Psychology |
| WOS类目 | Behavioral Sciences ; Neurosciences ; Psychology, Experimental |
| WOS记录号 | WOS:001215997200001 |
| 来源期刊 | TRENDS IN COGNITIVE SCIENCES
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/289826 |
| 作者单位 | Max Planck Society; Massachusetts Institute of Technology (MIT) |
| 推荐引用方式 GB/T 7714 | . Political reinforcement learners[J],2024,28(3). |
| APA | (2024).Political reinforcement learners.TRENDS IN COGNITIVE SCIENCES,28(3). |
| MLA | "Political reinforcement learners".TRENDS IN COGNITIVE SCIENCES 28.3(2024). |
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