12,270 research outputs found
A comparative analysis of the predictive abilities of economic complexity metrics using international trade network
The complex networks approach has proven to be an effective tool to understand and predict the evolution of a wide range of complex systems. In this work, we consider the network representing the exchange of goods between countries: the international trade network. According to the type of goods they export, the complex networks approach allows inferring which countries will have a bigger growth compared to others. The aim of this work is to study three different methods characterizing the complex networks and study their behaviour on two main topics. Can the method predict the economic evolution of a country? What happens to those methods when we merge the economies
PopRank: Ranking pages' impact and users' engagement on Facebook
Users online tend to acquire information adhering to their system of beliefs
and to ignore dissenting information. Such dynamics might affect page
popularity. In this paper we introduce an algorithm, that we call PopRank, to
assess both the Impact of Facebook pages as well as users' Engagement on the
basis of their mutual interactions. The ideas behind the PopRank are that i)
high impact pages attract many users with a low engagement, which means that
they receive comments from users that rarely comment, and ii) high engagement
users interact with high impact pages, that is they mostly comment pages with a
high popularity. The resulting ranking of pages can predict the number of
comments a page will receive and the number of its posts. Pages impact turns
out to be slightly dependent on pages' informative content (e.g., science vs
conspiracy) but independent of users' polarization.Comment: 10 pages, 5 figure
Reprint of The new paradigm of economic complexity
Economic complexity offers a potentially powerful paradigm to understand key societal issues and challenges of our time. The underlying idea is that growth, development, technological change, income inequality, spatial disparities, and resilience are the visible outcomes of hidden systemic interactions. The study of economic complexity seeks to understand the structure of these interactions and how they shape various socioeconomic processes. This emerging field relies heavily on big data and machine learning techniques. This brief introduction to economic complexity has three aims. The first is to summarize key theoretical foundations and principles of economic complexity. The second is to briefly review the tools and metrics developed in the economic complexity literature that exploit information encoded in the structure of the economy to find new empirical patterns. The final aim is to highlight the insights from economic complexity to improve prediction and political decision-making. Institutions including the World Bank, the European Commission, the World Economic Forum, the OECD, and a range of national and regional organizations have begun to embrace the principles of economic complexity and its analytical framework. We discuss policy implications of this field, in particular the usefulness of building recommendation systems for major public investment decisions in a complex world
The new paradigm of economic complexity
Economic complexity offers a potentially powerful paradigm to understand key societal issues and challenges of our time. The underlying idea is that growth, development, technological change, income inequality, spatial disparities, and resilience are the visible outcomes of hidden systemic interactions. The study of economic complexity seeks to understand the structure of these interactions and how they shape various socioeconomic processes. This emerging field relies heavily on big data and machine learning techniques. This brief introduction to economic complexity has three aims. The first is to summarize key theoretical foundations and principles of economic complexity. The second is to briefly review the tools and metrics developed in the economic complexity literature that exploit information encoded in the structure of the economy to find new empirical patterns. The final aim is to highlight the insights from economic complexity to improve prediction and political decision-making. Institutions including the World Bank, the European Commission, the World Economic Forum, the OECD, and a range of national and regional organizations have begun to embrace the principles of economic complexity and its analytical framework. We discuss policy implications of this field, in particular the usefulness of building recommendation systems for major public investment decisions in a complex world.publishedVersio
Human Computation and Convergence
Humans are the most effective integrators and producers of information,
directly and through the use of information-processing inventions. As these
inventions become increasingly sophisticated, the substantive role of humans in
processing information will tend toward capabilities that derive from our most
complex cognitive processes, e.g., abstraction, creativity, and applied world
knowledge. Through the advancement of human computation - methods that leverage
the respective strengths of humans and machines in distributed
information-processing systems - formerly discrete processes will combine
synergistically into increasingly integrated and complex information processing
systems. These new, collective systems will exhibit an unprecedented degree of
predictive accuracy in modeling physical and techno-social processes, and may
ultimately coalesce into a single unified predictive organism, with the
capacity to address societies most wicked problems and achieve planetary
homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added
references to page 1 and 3, and corrected typ
Productive Ecosystems and the arrow of development
Economic growth is associated with the diversification of economic activities, which can be observed via the evolution of product export baskets. Exporting a new product is dependent on having, and acquiring, a specific set of capabilities, making the diversification process path-dependent. Taking an agnostic view on the identity of the capabilities, here we derive a probabilistic model for the directed dynamical process of capability accumulation and product diversification of countries. Using international trade data, we identify the set of pre-existing products, the product Ecosystem, that enables a product to be exported competitively. We construct a directed network of products, the Eco Space, where the edge weight corresponds to capability overlap. We uncover a modular structure, and show that low- and middle-income countries move from product communities dominated by small Ecosystem products to advanced (large Ecosystem) product clusters over time. Finally, we show that our network model is predictive of product appearances
Complexity of products: the effect of data regularisation
Among several developments, the field of Economic Complexity (EC) has notably
seen the introduction of two new techniques. One is the Bootstrapped Selective
Predictability Scheme (SPSb), which can provide quantitative forecasts of the
Gross Domestic Product of countries. The other, Hidden Markov Model (HMM)
regularisation, denoises the datasets typically employed in the literature. We
contribute to EC along three different directions. First, we prove the
convergence of the SPSb algorithm to a well-known statistical learning
technique known as Nadaraya-Watson Kernel regression. The latter has
significantly lower time complexity, produces deterministic results, and it is
interchangeable with SPSb for the purpose of making predictions. Second, we
study the effects of HMM regularization on the Product Complexity and logPRODY
metrics, for which a model of time evolution has been recently proposed. We
find confirmation for the original interpretation of the logPRODY model as
describing the change in the global market structure of products with new
insights allowing a new interpretation of the Complexity measure, for which we
propose a modification. Third, we explore new effects of regularisation on the
data. We find that it reduces noise, and observe for the first time that it
increases nestedness in the export network adjacency matrix
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