在部分模块化社区中信仰的传播
Robert L Goldstone1,2, Marina Dubova2, Rachith Aiyappa3
1Department of Psychological and Brain Sciences, Indiana University.
概括
了解在孤立的社交网络中传播的信念是关键. 模拟表明,平衡的探索和利用策略,以及一些随机性,增强了社区内的有用信念传播.
科学领域:
- 社交网络分析 社交网络分析
- 计算社会科学 计算社会科学
- 信息动力学 信息动力学
背景情况:
- 社交网络经常表现出信息隔离,有回声室和过泡阻碍跨社区信息流动.
- 了解信仰如何在不同社区内和不同社区之间传播和发展,对于应对社会挑战至关重要.
研究的目的:
- 通过使用网络分析工具,分析信仰在模块化社会社区的传播.
- 预测个人和团体参数对这些网络内的信念动态和分布的影响.
主要方法:
- 在模块化社区 (SBMC) 中信仰传播的发展框架.
- 使用随机区块模型来生成具有不同模块化的社交网络.
- 基于证据聚合 (总和/平均) 和参数化随机性,模拟信念变化.
主要成果:
- 增加集团外部连接并不总是提高集团业绩.
- 将随机性引入决策过程可以积极影响小组绩效.
- 总结证据,而不是平均值,可以带来更好的团队表现,以信念效用来衡量.
结论:
- 信条探索的中间层次对传播有用的信仰是有益的.
- 代表探索-开发连续性的对立力量的配对参数可以有利于信仰传播.
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