更高层次的新奇事物的动态
Gabriele Di Bona1,2,3,4, Alessandro Bellina3,4,5, Giordano De Marzo4,5,6,7
1School of Mathematical Sciences, Queen Mary University of London, London, UK.
Nature communications
|January 4, 2025
概括
这项研究引入了更高阶的新奇事物,定义为元素的首次共发生,以及更高阶的堆指数,以测量发现速度. 现实世界的数据显示,这些测量显示了标准方法无法捕捉到的勘探过程中的差异.
科学领域:
- 复杂性科学是一门复杂性科学.
- 网络科学 网络科学
- 信息理论是信息理论.
背景情况:
- 了解新奇和发现对于创新至关重要.
- 现有的方法仅通过第一次出现来定义新性.
- 新也可以从结合已知的元素中产生.
研究的目的:
- 定义和量化更高层次的新鲜事物.
- 为了引入更高阶的堆指数来发现速度.
- 模拟勘探过程及其不断发展的网络.
主要方法:
- 定义更高层次的新鲜事物 (同时发生).
- 介绍一个更高阶的Heaps指数.
- 分析现实世界的数据.
- 模拟探索作为一个网络上的随机步行.
主要成果:
- 标准堆的指数可以掩盖在更高阶的发现差异.
- 更高层次的测量显示出不同的勘探过程动态.
- 随机步行网络模型复制了经验上的高阶新奇性质.
结论:
- 高阶新及其指数提供了对发现的更细致的理解.
- 勘探过程和它们穿过的网络共同发展.
- 拟议的模型为科学和技术发现的机制提供了洞察力.
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