气候变化领域集成服务门户(CCPortal): Political reinforcement learners

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DOI	10.1016/j.tics.2023.12.001
	Political reinforcement learners
	Schulz, Lion; Bhui, Rahul
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
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/289827
作者单位	Max Planck Society; Massachusetts Institute of Technology (MIT)
推荐引用方式 GB/T 7714	Schulz, Lion,Bhui, Rahul. Political reinforcement learners[J],2024,28(3).
APA	Schulz, Lion,&Bhui, Rahul.(2024).Political reinforcement learners.TRENDS IN COGNITIVE SCIENCES,28(3).
MLA	Schulz, Lion,et al."Political reinforcement learners".TRENDS IN COGNITIVE SCIENCES 28.3(2024).