社会强化学习如何导致超稳定的两极分化和选民模式
Benedikt V Meylahn1, Janusz M Meylahn2
1Korteweg-de Vries Institue for Mathematics, University of Amsterdam, Amsterdam, The Netherlands.
PloS one
|December 17, 2024
概括
强化学习模型的意见动态,但观察到的两极化是变态稳定的,不是永久的. 仔细选择分析工具对于理解这些复杂的意见动态模型至关重要.
科学领域:
- 社会动态 社会动态
- 计算社会科学 计算社会科学
- 意见形成建模 意见形成建模
背景情况:
- 传统的论动态模型通常假设有促进两极分化的因素.
- 最近的研究表明,社会强化学习 (SRL) 可以解释持续的两极分化.
- 这部作品批判性地考察了SRL在意见动态中的作用.
研究的目的:
- 突出强化学习 (RL) 对于建模意见动态的有用性.
- 警告在基于RL的模型中不批判地应用分析工具.
- 调查SRL意见动态的长期行为和属性.
主要方法:
- 将SRL模型与既定选民模型联系起来.
- 分析模型的ergodicity和静止分布.
- 调查对代理学习过程修改的影响.
主要成果:
- 由SRL引起的极化是转移稳定的,而不是无限期持久的.
- 该模型与概率为1的共识趋同.
- 在学习规则的轻微变化将模型从非ergodic转移到ergodic.
结论:
- 强化学习是模拟意见两极化的一个强有力的工具.
- 分析工具的选择 (例如,静止分布,吸收时间) 关键取决于模型属性,如ergodicity.
- 模型属性对学习过程的细节很敏感,可能仅仅从模拟中就不清楚.
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