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From Innovation to Diversification: A Simple Competitive Model.

Saracco F, Di Clemente R, Gabrielli A, Pietronero L - PLoS ONE (2015)

Bottom Line: In the present article, we propose a simple dynamical model where countries compete with each other to acquire the ability to produce and export new products.Countries will have two possibilities to expand their export: innovating, i.e. introducing new goods, namely new nodes in the product networks, or copying the productive process of others, i.e. occupying a node already present in the same network.In this way, the topology of the products network and the country-product matrix evolve simultaneously, driven by the countries push toward innovation.

View Article: PubMed Central - PubMed

Affiliation: Istituto dei Sistemi Complessi - ISC CNR UoS "Sapienza" Physics Department Università di Roma, P.le Aldo Moro 5, 00185, Rome, Italy.

ABSTRACT
Few attempts have been proposed in order to describe the statistical features and historical evolution of the export bipartite matrix countries/products. An important standpoint is the introduction of a products network, namely a hierarchical forest of products that models the formation and the evolution of commodities. In the present article, we propose a simple dynamical model where countries compete with each other to acquire the ability to produce and export new products. Countries will have two possibilities to expand their export: innovating, i.e. introducing new goods, namely new nodes in the product networks, or copying the productive process of others, i.e. occupying a node already present in the same network. In this way, the topology of the products network and the country-product matrix evolve simultaneously, driven by the countries push toward innovation.

No MeSH data available.


Model Results.In (a) the scatter plot of Fitness ranking against countries diversification, while in (b) the one for Quality ranking against products ubiquity; the blue points represent the observed values (for the year 1980 from the dataset of [25, 26]). The black line represents the average value on the simulations, while the grey lines bind the area between the second and the first 3-quantiles (dot-dashed) and between the 975th and 25th permilles (dashed). The data obtained are for initial conditions Nroots = 20 and P0 = 0.3 and parameters α = 1.55, β = 0.8, γ = 0.3, k0 = 4. In the ∼82% the observed data fall into the area between 975th and 25th permilles for the fitness distribution, ∼75% for the quality distribution. In (c) the original matrix for 1980 from the dataset of [25, 26]; in (d) one of the synthetic matrix for initial conditions Nroots = 20 and P0 = 0.3 and parameters α = 1.65, β = 1.1, γ = 0.6, k0 = 4.
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pone.0140420.g003: Model Results.In (a) the scatter plot of Fitness ranking against countries diversification, while in (b) the one for Quality ranking against products ubiquity; the blue points represent the observed values (for the year 1980 from the dataset of [25, 26]). The black line represents the average value on the simulations, while the grey lines bind the area between the second and the first 3-quantiles (dot-dashed) and between the 975th and 25th permilles (dashed). The data obtained are for initial conditions Nroots = 20 and P0 = 0.3 and parameters α = 1.55, β = 0.8, γ = 0.3, k0 = 4. In the ∼82% the observed data fall into the area between 975th and 25th permilles for the fitness distribution, ∼75% for the quality distribution. In (c) the original matrix for 1980 from the dataset of [25, 26]; in (d) one of the synthetic matrix for initial conditions Nroots = 20 and P0 = 0.3 and parameters α = 1.65, β = 1.1, γ = 0.6, k0 = 4.

Mentions: Fitness and Complexity In spite of the simplicity of our the model, there is a remarkably good agreement between simulations and real data for Fitness/Complexity (for details about the definition of Fitness and Complexity, see the Supporting Information in S1 File). In particular the shape of the scatter plot for countries fitnesses (products complexities) ranking against countries diversifications (products ubiquities) reproduces well the behavior in the real data; the result is shown in Fig 3(a) (Fig 3(b)). It is possible to see that our algorithm is able to reproduce the “shape” of the original matrix data (blue dots) within the 95%, which is a remarkable result, since these peculiar trends are derived by the highly non-linear algorithm for Fitnesses and Complexities.


From Innovation to Diversification: A Simple Competitive Model.

Saracco F, Di Clemente R, Gabrielli A, Pietronero L - PLoS ONE (2015)

Model Results.In (a) the scatter plot of Fitness ranking against countries diversification, while in (b) the one for Quality ranking against products ubiquity; the blue points represent the observed values (for the year 1980 from the dataset of [25, 26]). The black line represents the average value on the simulations, while the grey lines bind the area between the second and the first 3-quantiles (dot-dashed) and between the 975th and 25th permilles (dashed). The data obtained are for initial conditions Nroots = 20 and P0 = 0.3 and parameters α = 1.55, β = 0.8, γ = 0.3, k0 = 4. In the ∼82% the observed data fall into the area between 975th and 25th permilles for the fitness distribution, ∼75% for the quality distribution. In (c) the original matrix for 1980 from the dataset of [25, 26]; in (d) one of the synthetic matrix for initial conditions Nroots = 20 and P0 = 0.3 and parameters α = 1.65, β = 1.1, γ = 0.6, k0 = 4.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4636243&req=5

pone.0140420.g003: Model Results.In (a) the scatter plot of Fitness ranking against countries diversification, while in (b) the one for Quality ranking against products ubiquity; the blue points represent the observed values (for the year 1980 from the dataset of [25, 26]). The black line represents the average value on the simulations, while the grey lines bind the area between the second and the first 3-quantiles (dot-dashed) and between the 975th and 25th permilles (dashed). The data obtained are for initial conditions Nroots = 20 and P0 = 0.3 and parameters α = 1.55, β = 0.8, γ = 0.3, k0 = 4. In the ∼82% the observed data fall into the area between 975th and 25th permilles for the fitness distribution, ∼75% for the quality distribution. In (c) the original matrix for 1980 from the dataset of [25, 26]; in (d) one of the synthetic matrix for initial conditions Nroots = 20 and P0 = 0.3 and parameters α = 1.65, β = 1.1, γ = 0.6, k0 = 4.
Mentions: Fitness and Complexity In spite of the simplicity of our the model, there is a remarkably good agreement between simulations and real data for Fitness/Complexity (for details about the definition of Fitness and Complexity, see the Supporting Information in S1 File). In particular the shape of the scatter plot for countries fitnesses (products complexities) ranking against countries diversifications (products ubiquities) reproduces well the behavior in the real data; the result is shown in Fig 3(a) (Fig 3(b)). It is possible to see that our algorithm is able to reproduce the “shape” of the original matrix data (blue dots) within the 95%, which is a remarkable result, since these peculiar trends are derived by the highly non-linear algorithm for Fitnesses and Complexities.

Bottom Line: In the present article, we propose a simple dynamical model where countries compete with each other to acquire the ability to produce and export new products.Countries will have two possibilities to expand their export: innovating, i.e. introducing new goods, namely new nodes in the product networks, or copying the productive process of others, i.e. occupying a node already present in the same network.In this way, the topology of the products network and the country-product matrix evolve simultaneously, driven by the countries push toward innovation.

View Article: PubMed Central - PubMed

Affiliation: Istituto dei Sistemi Complessi - ISC CNR UoS "Sapienza" Physics Department Università di Roma, P.le Aldo Moro 5, 00185, Rome, Italy.

ABSTRACT
Few attempts have been proposed in order to describe the statistical features and historical evolution of the export bipartite matrix countries/products. An important standpoint is the introduction of a products network, namely a hierarchical forest of products that models the formation and the evolution of commodities. In the present article, we propose a simple dynamical model where countries compete with each other to acquire the ability to produce and export new products. Countries will have two possibilities to expand their export: innovating, i.e. introducing new goods, namely new nodes in the product networks, or copying the productive process of others, i.e. occupying a node already present in the same network. In this way, the topology of the products network and the country-product matrix evolve simultaneously, driven by the countries push toward innovation.

No MeSH data available.