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协同作用的小世界驱动着技术的复杂化
Hardik Rajpal1,2,3, Omar Guerrero3
1Centre for Complexity Sciences, Imperial College London, South Kensington, London SW7 2AZ, United Kingdom.
PNAS nexus
|April 17, 2025
概括
本研究引入了一种使用输入-输出数据分析经济生产的新方法. 它揭示了具有强大的核心网络和专业模块的复杂行业可以实现更高的经济复杂性和更低排放量的产出.
科学领域:
- 经济学 经济学 经济学
- 网络科学 网络科学
- 信息理论 信息理论
背景情况:
- 经济增长与技术进步有关,但在大型系统中,生产过程的相互作用仍然不清楚.
- 了解输入-输出关系对于分析整个行业和整个经济的生产技术至关重要.
研究的目的:
- 开发一种基于数据的方法,用输入-输出数据量化生产过程中的输入互补性.
- 将信息理论的协同信息与经济互补联系起来,而不是假设生产功能.
主要方法:
- 使用输入-输出数据来量化输入互补性.
- 应用了信息理论中的协同信息概念作为经济类比.
- 开发了一种数据驱动的方法来识别和量化输入-输入相互作用及其对输出的贡献.
主要成果:
- 揭示了输入-输入协同作用互动网络,是产业生产技术的特征.
- 发现复杂的行业表现出模块化小世界网络拓,以第三部门为核心.
- 观察到,成熟的核心网络和专业模块与更高的经济复杂性,产量和更低的排放有关.
结论:
- 拟议的方法提供了一个框架,以确定提高经济绩效的关键经济关系.
- 产业结构,以网络拓为特征,是经济复杂性和环境影响的重要决定因素.
- 协同效应信息为理解经济互补性和生产网络提供了一个新的视角.
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