The complex dynamics of products and its asymptotic properties

Angelini, Orazio; Cristelli, Matthieu; Zaccaria, Andrea; Pietronero, L.

doi:10.1371/journal.pone.0177360

Cited by 13 publications

(36 citation statements)

References 33 publications

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“…One such attempt compared economic inequality measurements with Fitness [ 19 ]. This paper contributes to the latest developments of the Complexity and Fitness measures and it follows up mainly from the earlier work by Angelini et al [ 20 ] focusing on the Complexity measure. In particular, the Complexity index has been paired with logPRODY ( L , see Section 3.2 ) to obtain an interesting insight.…”

Section: Introductionmentioning

confidence: 99%

“…It is possible to represent product classes as points on the Complexity-logPRODY plane. Their motion on said plane can be modelled with a potential-like equation [ 20 ] (see Section 3.3 for more details). In this work, we report the results of the application of SPSb and HMM regularization on the Complexity measure, and we show how HMM affects the

matrices.…”

Section: Introductionmentioning

confidence: 99%

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Complexity of Products: The Effect of Data Regularisation

Angelini

Matteo

2018

Entropy

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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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Section: Introductionmentioning

confidence: 99%

matrices.…”

Section: Introductionmentioning

confidence: 99%

Complexity of Products: The Effect of Data Regularisation

Angelini

Matteo

2018

Entropy

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“…In order to test the predictive power of our measure of Impact, we split our dataset, which comprises 22 months of data, in a training set and a test set. While the Impact is computed using the first 16 Figure 2. Future activity (i.e., number of posts) of Facebook pages as a function of Impact ranking.…”

Section: Impact Predicts Users' and Pages' Activitymentioning

confidence: 99%

PopRank: Ranking pages’ impact and users’ engagement on Facebook

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The advent of social networks revolutionized the way people access to information sources. Understanding the complex relationship between these sources and users is crucial. 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 future posts. Pages’ impact turns out to be slightly dependent on the quality of pages’ informative content (e.g., science vs conspiracy) but independent of users’ polarization.

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“…The basic idea is to define an iteration process which couples the fitness of a country to the complexity of a product and then obtain the fixed point values [314,315,[320][321][322][323][324][325][326][327]. For the fitness, this is proportional to the sum of the products exported weighted by their complexity.…”

Section: Fitness and Complexity Indexmentioning

confidence: 99%

Structure-oriented prediction in complex networks

2018

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Complex systems are extremely hard to predict due to its highly nonlinear interactions and rich emergent properties. Thanks to the rapid development of network science, our understanding of the structure of real complex systems and the dynamics on them has been remarkably deepened, which meanwhile largely stimulates the growth of effective prediction approaches on these systems. In this article, we aim to review different networkrelated prediction problems, summarize and classify relevant prediction methods, analyze their advantages and disadvantages, and point out the forefront as well as critical challenges of the field. Contents

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The complex dynamics of products and its asymptotic properties

Cited by 13 publications

References 33 publications

Complexity of Products: The Effect of Data Regularisation

Complexity of Products: The Effect of Data Regularisation

PopRank: Ranking pages’ impact and users’ engagement on Facebook

Structure-oriented prediction in complex networks

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