相关实验视频
Updated: Sep 8, 2025

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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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在基于代理的模型中捕捉参数
Leon Assaad1, Rafael Fuchs2, Kirsty Phillips3
1Munich Center for Mathematical Philosophy, LMU, Geschwister-Scholl-Platz 1, 80539 Munich, Bavaria Germany.
概括
基于代理的模型 (ABM) 可以通过将参数区分为理由,语法和辩证法来建模复杂的参数. 使用贝叶斯网络的NormAN框架提供了一种比较这些模型和研究论证扩散的新方法.
科学领域:
- 计算社会科学
- 人工智能
- 认知科学
背景情况:
- 基于代理的模型 (ABM) 是用于研究复杂系统和新出现的特性.
- 对于在线社交媒体和科学话语来说, ABM 的信念和观点动态都很重要.
- 现有的ABM没有完全捕捉到丰富的论证场景.
研究的目的:
- 引入一个基于代理的论证模型的框架.
- 区分和分析论证的三个概念:理由,语法和辩证法.
- 提供一个组织方案来比较和选择参数模型.
主要方法:
- 区分论据作为命题内容 (理由),前提-结论关系 (语义) 和对话部署 (辩证法).
- 使用NormAN框架,在贝叶斯网络上构建ABM,作为参考模型.
- 分析模型中的复杂性连续性.
主要成果:
- 一个新的组织方案来比较和选择基于代理的论证模型.
- 阐明三种论证概念如何相互约束.
- 证明NormAN框架能够捕捉熟悉的论证方面以及对论证传播的辩证影响.
结论:
- 基于代理的模型可以有效地代表复杂的论证,包括辩证方面.
- 该NormAN框架提供了一个模拟参数交换的多功能方法.
- 进一步的研究可以探索辩证考量如何影响群体中的论点传播.
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