反一致性的去极化力量

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Arkadiusz Lipiecki, Katarzyna Sznajd-Weron
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引用次数: 0

摘要

政治两极分化阻碍了从公共卫生到环境政策等多个领域的集体决策。因此,去极化策略是至关重要的,并得到越来越多的研究。反从众,通过反对他人的意见来回应社会影响,与两极分化的加剧有关,而它作为一种去两极分化力量的潜在作用在很大程度上被忽视了。尽管心理学家指出了不同形式的反从众,但大多数计算模型只关注仇外心理,即对局外人的偏见,这会使观点变得激进。我们的工作通过考虑另一种类型的反从众——主张独特性来解决这一差距。我们提出了一个反直觉的假设,即通过反对影响群体来增加分歧可以减少基于问题的两极分化。在一系列计算模型中,我们表明,基于促进独特性的去极化干预可能比传统干预(如减少群体内偏袒或增强宽容)更有效。我们通过最近关于战略反从众的心理实验来讨论我们发现的相关性,这些实验证明了在现实世界环境中应用所提出的去极化干预的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Depolarizing power of anticonformity
Political polarization hinders collective decision-making across multiple domains, from public health to environmental policy. Therefore, depolarization strategies are crucial and have been increasingly studied. Anticonformity, responding to social influence by opposing the opinions of others, has been associated with increased polarization, while its potential role as a depolarizing force has been largely overlooked. Although psychologists point to different forms of anticonformity, most computational models focus solely on xenophobia, prejudice against outsiders, which radicalizes opinions. Our work addresses this gap by considering another type of anticonformity – asserting uniqueness. We propose the counterintuitive hypothesis that increasing the disagreement by anticonforming to the influence group can reduce issue-based polarization. Within a family of computational models, we show that a depolarizing intervention based on promoting uniqueness may be more effective than traditional interventions, such as decreasing in-group favoritism or enhancing tolerance. We discuss the relevance of our findings through the lens of recent psychological experiments on strategic anticonformity, which demonstrate the potential of applying the proposed depolarizing intervention in real-world settings.
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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
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