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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.


Nestedness & Assortativity.The distributions for the nestedness values (obtained employing NODF, the definition by [32]) and assortativity index r (obtained employing the definition by [33]) for 50 simulations with initial conditions Nroots = 20 and P0 = 0.3 and parameters α = 1.55, β = 0.8, γ = 0.3, k0 = 4. In a)) the total NODF, in b) the NODF for rows and in c) the one for columns. The red line is the observed value for the year 1980 from the dataset of [25, 26], the blue dashed lines bind the area between the second and the first 3-quantiles, while the purple line the area between between the 975th and 25th permilles. For the 4 distributions, real values easily fit in the 95%; anyway, for NODF values the real values lie just outside the central third of the probability. Notice the similar distributions for NODFt and NODFp, as explained in Eq S6 in Supporting Information in S1 File. In d) the distribution for the assortativity values (obtained employing the definition by [33]): Even if the distribution is quite weird, the value measured on the real matrix is just outside the area containing the 33% of the distribution.
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pone.0140420.g004: Nestedness & Assortativity.The distributions for the nestedness values (obtained employing NODF, the definition by [32]) and assortativity index r (obtained employing the definition by [33]) for 50 simulations with initial conditions Nroots = 20 and P0 = 0.3 and parameters α = 1.55, β = 0.8, γ = 0.3, k0 = 4. In a)) the total NODF, in b) the NODF for rows and in c) the one for columns. The red line is the observed value for the year 1980 from the dataset of [25, 26], the blue dashed lines bind the area between the second and the first 3-quantiles, while the purple line the area between between the 975th and 25th permilles. For the 4 distributions, real values easily fit in the 95%; anyway, for NODF values the real values lie just outside the central third of the probability. Notice the similar distributions for NODFt and NODFp, as explained in Eq S6 in Supporting Information in S1 File. In d) the distribution for the assortativity values (obtained employing the definition by [33]): Even if the distribution is quite weird, the value measured on the real matrix is just outside the area containing the 33% of the distribution.

Mentions: As it can be seen in the Fig 4, panels c, the NODFp of real data is well replicated by our algorithm. This means that our model is able to catch the main features of the hierarchical organization of products; this result is due to the assumption of the presence of a products network, i.e. a hierarchical structure among products. The result for NODFc, in the panel b of Fig 4, is much more non trivial: in this case the values from the simulations reproduce the same quantity for the real matrix, even though no explicit structure was imposed on the set of countries.


From Innovation to Diversification: A Simple Competitive Model.

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

Nestedness & Assortativity.The distributions for the nestedness values (obtained employing NODF, the definition by [32]) and assortativity index r (obtained employing the definition by [33]) for 50 simulations with initial conditions Nroots = 20 and P0 = 0.3 and parameters α = 1.55, β = 0.8, γ = 0.3, k0 = 4. In a)) the total NODF, in b) the NODF for rows and in c) the one for columns. The red line is the observed value for the year 1980 from the dataset of [25, 26], the blue dashed lines bind the area between the second and the first 3-quantiles, while the purple line the area between between the 975th and 25th permilles. For the 4 distributions, real values easily fit in the 95%; anyway, for NODF values the real values lie just outside the central third of the probability. Notice the similar distributions for NODFt and NODFp, as explained in Eq S6 in Supporting Information in S1 File. In d) the distribution for the assortativity values (obtained employing the definition by [33]): Even if the distribution is quite weird, the value measured on the real matrix is just outside the area containing the 33% of the distribution.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4636243&req=5

pone.0140420.g004: Nestedness & Assortativity.The distributions for the nestedness values (obtained employing NODF, the definition by [32]) and assortativity index r (obtained employing the definition by [33]) for 50 simulations with initial conditions Nroots = 20 and P0 = 0.3 and parameters α = 1.55, β = 0.8, γ = 0.3, k0 = 4. In a)) the total NODF, in b) the NODF for rows and in c) the one for columns. The red line is the observed value for the year 1980 from the dataset of [25, 26], the blue dashed lines bind the area between the second and the first 3-quantiles, while the purple line the area between between the 975th and 25th permilles. For the 4 distributions, real values easily fit in the 95%; anyway, for NODF values the real values lie just outside the central third of the probability. Notice the similar distributions for NODFt and NODFp, as explained in Eq S6 in Supporting Information in S1 File. In d) the distribution for the assortativity values (obtained employing the definition by [33]): Even if the distribution is quite weird, the value measured on the real matrix is just outside the area containing the 33% of the distribution.
Mentions: As it can be seen in the Fig 4, panels c, the NODFp of real data is well replicated by our algorithm. This means that our model is able to catch the main features of the hierarchical organization of products; this result is due to the assumption of the presence of a products network, i.e. a hierarchical structure among products. The result for NODFc, in the panel b of Fig 4, is much more non trivial: in this case the values from the simulations reproduce the same quantity for the real matrix, even though no explicit structure was imposed on the set of countries.

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.