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From Innovation to Diversification: A Simple Competitive Model

Overview of attention for article published in PLOS ONE, November 2015
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Article details
Title
From Innovation to Diversification: A Simple Competitive Model
Published in
PLOS ONE, November 2015
DOI 10.1371/journal.pone.0140420
Pubmed ID
Authors
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.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Italy 1 3%
Unknown 39 98%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 9 23%
Researcher 9 23%
Student > Master 6 15%
Student > Bachelor 4 10%
Professor 3 8%
Other 2 5%
Unknown 7 18%
Readers by discipline
Readers by discipline Count As %
Physics and Astronomy 8 20%
Engineering 7 18%
Economics, Econometrics and Finance 3 8%
Social Sciences 3 8%
Mathematics 2 5%
Other 6 15%
Unknown 11 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 August 2015.
All research outputs
#14,828,199
of 24,021,239 outputs
Outputs from PLOS ONE
#124,120
of 206,042 outputs
Outputs of similar age
#147,069
of 290,131 outputs
Outputs of similar age from PLOS ONE
#2,759
of 5,397 outputs
Altmetric has tracked 24,021,239 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 206,042 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.6. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 290,131 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5,397 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.