测度智力资本的知识分散指数
Knowledge Dispersion Index for Measuring Intellectual Capital
摘要：
在本文中，我们提出了一个新的指数来量化和衡量宏观和微观尺度上的信息流。我们讨论了这个指数的知识管理领域的影响，也作为智力资本，从而可以利用企业家。我们探索不同的功能和面向人类的指标，可用于在微观尺度上处理信息流。我们提出了一个约23个指标的表，如IT库存的变化和具有高级学位的员工的百分比，可以用于在微观尺度上完全量化知识分散为智力资本。在宏观尺度上，我们将经济分为工业和消费部门，每个部门的信息流决定了经济增长的速度以及经济在总需求下的整体表现。最后，我们提出了一个基于图论的知识扩散模型，并展示了如何在流程中的纠正变得不言自明。通过流量守恒和容量约束的原则，我们还推测这种流量可能会寻求一些平衡，并表现出自我校正代码。该模型允许我们考虑局部噪声形式的扰动，网络的演化，提供对来自较低节点的局部损坏的鲁棒性，并帮助确定网络超家族的潜在分类。
英文摘要：
In this paper we propose a novel index to quantify and measure the flow of information on macro and micro scales. We discuss the implications of this index for knowledge management fields and also as intellectual capital that can thus be utilized by entrepreneurs. We explore different function and human oriented metrics that can be used at micro-scales to process the flow of information. We present a table of about 23 metrics, such as change in IT inventory and percentage of employees with advanced degrees, that can be used at micro scales to wholly quantify knowledge dispersion as intellectual capital. At macro scales we split the economy in an industrial and consumer sector where the flow of information in each determines how fast an economy is going to grow and how overall an economy will perform given the aggregate demand. Lastly, we propose a model for knowledge dispersion based on graph theory and show how corrections in the flow become self-evident. Through the principals of flow conservation and capacity constrains we also speculate how this flow might seeks some equilibrium and exhibit self-correction codes. This proposed model allows us to account for perturbations in form of local noise, evolution of networks, provide robustness against local damage from lower nodes, and help determine the underlying classification into network super-families.
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