技术创新的混合加拉姆巴斯模型
Giulia Rotundo1, Roy Cerqueti2,3, Gurjeet Dhesi4,5
1Department of Statistical Sciences, Sapienza University of Rome, p.le A. Moro 5, 00185 Rome, Italy.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
这项研究介绍了混合意见动态模型,将Galam和Bass模型结合在复杂的网络上. 结果显示,意见轨迹有更高的偏好依恋度,这表明意见形成更加可预测.
科学领域:
- 复杂的系统
- 社会网络分析
- 意见的变化
背景情况:
- 现有的模式往往忽略了网络结构在意见形成.
- 像巴斯这样的技术采用模式并没有完全捕捉到论的动态.
- 巴巴西-阿尔伯特网络为复杂的交互提供了一个现实的结构.
研究的目的:
- 在复杂的网络中开发一种混合模型,将意见动态 (Galam) 和技术采用 (Bass) 整合起来.
- 纳入不可逆转的意见转变和有魅力的竞争对手的影响.
- 使用科尔莫戈罗夫-西奈量化系统的不可预测性.
主要方法:
- 在巴拉巴西-阿尔伯特网络上混合的Galam-Bass模型.
- 模拟意见形成与不可逆转的过渡和竞争对手的影响.
- 在信息丢失分析中应用科尔莫戈罗夫-西奈.
主要成果:
- 该模型成功地模拟了结合网络结构的意见形成.
- 无法逆转的转变和竞争对手的影响改变了意见的动态.
- 更多的偏好导致了更多的意见轨迹.
结论:
- 混合模型为研究复杂网络的意见动态提供了一个新的框架.
- 网络结构对意见形成和可预测性有重大影响.
- 科尔莫戈罗夫-西奈有效量化了意见动态中的信息损失.
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