arXivDaily arXiv每日学术速递 周一至周五更新
arXiv physics/0406091physics.soc-phcond-mat.stat-mechphysics.gen-ph
收藏

社交网络中的创新流:生产率分布

Innovation flow through social networks: Productivity distribution

T. Di Matteo, T. Aste, M. Gallegati

摘要:

对几个国家和几年的非金融企业生产率的详细实证分析表明,生产率遵循幂律尾部的非高斯分布。我们表明,这些实证研究结果可以解释为在社会网络中的交换机制,企业提高生产力的直接创新或/和模仿其他公司的技术和组织解决方案的后果。网络连接的类型决定了信息传播的速度和效率,以及创新渗透或行为被模仿的速度。从一个复杂网络中的创新流模型中,我们得到了生产率水平的期望值与企业之间的联系网络的连通性成正比。与经验分布的比较表明,这样的网络必须是一个无标度类型的幂律度分布在大的连接范围。

英文摘要:

A detailed empirical analysis of the productivity of non financial firms across several countries and years shows that productivity follows a non-Gaussian distribution with power law tails. We demonstrate that these empirical findings can be interpreted as consequence of a mechanism of exchanges in a social network where firms improve their productivity by direct innovation or/and by imitation of other firm's technological and organizational solutions. The type of network-connectivity determines how fast and how efficiently information can diffuse and how quickly innovation will permeate or behaviors will be imitated. From a model for innovation flow through a complex network we obtain that the expectation values of the productivity level are proportional to the connectivity of the network of links between firms. The comparison with the empirical distributions reveals that such a network must be of a scale-free type with a power-law degree distribution in the large connectivity range.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑