机器学习模式表明，多样化推动经济增长

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英文标题：
《Machine-learned patterns suggest that diversification drives economic
  development》
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作者：
Charles D. Brummitt, Andres Gomez-Lievano, Ricardo Hausmann, and
  Matthew H. Bonds
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最新提交年份：
2018
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英文摘要：
  We develop a machine-learning-based method, Principal Smooth-Dynamics Analysis (PriSDA), to identify patterns in economic development and to automate the development of new theory of economic dynamics. Traditionally, economic growth is modeled with a few aggregate quantities derived from simplified theoretical models. Here, PriSDA identifies important quantities. Applied to 55 years of data on countries\' exports, PriSDA finds that what most distinguishes countries\' export baskets is their diversity, with extra weight assigned to more sophisticated products. The weights are consistent with previous measures of product complexity in the literature. The second dimension of variation is a proficiency in machinery relative to agriculture. PriSDA then couples these quantities with per-capita income and infers the dynamics of the system over time. According to PriSDA, the pattern of economic development of countries is dominated by a tendency toward increased diversification. Moreover, economies appear to become richer after they diversify (i.e., diversity precedes growth). The model predicts that middle-income countries with diverse export baskets will grow the fastest in the coming decades, and that countries will converge onto intermediate levels of income and specialization. PriSDA is generalizable and may illuminate dynamics of elusive quantities such as diversity and complexity in other natural and social systems.
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中文摘要：
我们开发了一种基于机器学习的方法，即Principal Smooth Dynamics Analysis（Principal Smooth Dynamics Analysis，PriSDA），以识别经济发展中的模式，并自动开发新的经济动力学理论。传统上，经济增长是用从简化理论模型中得出的几个总量来建模的。在这里，普里斯达确定了重要的数量。通过对55年来各国出口数据的分析，普里斯达发现，各国出口篮子的最大区别在于其多样性，并将额外的权重分配给更复杂的产品。权重与文献中先前的产品复杂性度量一致。变异的第二个维度是相对于农业而言的机械熟练程度。PriSDA然后将这些数量与人均收入进行耦合，并推断出系统随时间的动态变化。普里斯达认为，各国的经济发展模式以日益多样化的趋势为主。此外，经济体在多样化之后似乎变得更加富有（即多样化先于增长）。该模型预测，具有不同出口篮子的中等收入国家将在未来几十年内增长最快，而这些国家的收入和专业化水平将趋于中等水平。普里斯达是可以概括的，它可能会揭示其他自然和社会系统中难以捉摸的数量的动态，例如多样性和复杂性。
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分类信息：

一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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关键词：经济增长 机器学习 多样化 Quantitative Contribution