从加权的信念网络中出现了简单和复杂的传染动态
Rachith Aiyappa1, Alessandro Flammini1, Yong-Yeol Ahn1
1Center for Complex Networks and Systems, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408, USA.
Science advances
|April 12, 2024
概括
社会传染,行为和信仰的传播,现在可以通过一个新的模型来解释. 这个模型显示了认知过程如何导致简单和复杂的传染动态,包括耐药性.
科学领域:
- 社会心理学 社会心理学
- 计算社会科学 计算社会科学
- 认知科学 认知科学
背景情况:
- 社会传染是个人和社会变革的根本驱动力.
- 现有的理论往往忽视了认知机制与传染动态的整合.
- 社会系统可以同时表现出各种传染动态,从简单到复杂.
研究的目的:
- 提出一种新的模型,将认知机制与社会传染理论相结合.
- 为了证明简单和复杂的传染动力学如何从认知相互作用中有机地出现.
- 解释抵抗的出现,复杂的传染的关键机制,从认知过程.
主要方法:
- 发展一种相互影响的信仰的计算模型.
- 模拟模型以观察新出现的传染动态.
- 分析模型能够复制简单和复杂的传染模式的能力.
主要成果:
- 提出的模型成功地产生了简单和复杂的传染动态.
- 模型中的认知机制自然会导致各种传染行为.
- 该模型阐明了复杂传染的标志性特性抵抗是如何从潜在的认知过程中产生的.
结论:
- 认知相互作用足以解释各种社会传染动态的出现.
- 该模型为理解社会传染提供了一个统一的框架,将认知和动态视角相结合.
- 这项工作为推动社会变革和信仰传播的机制提供了新的见解.
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