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Using Bayesian dynamical systems, model averaging and neural networks to determine interactions between socio-economic indicators

Overview of attention for article published in PLOS ONE, May 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 tweeters

Citations

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5 Dimensions

Readers on

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18 Mendeley
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Title
Using Bayesian dynamical systems, model averaging and neural networks to determine interactions between socio-economic indicators
Published in
PLOS ONE, May 2018
DOI 10.1371/journal.pone.0196355
Pubmed ID
Authors

Björn R. H. Blomqvist, Richard P. Mann, David J. T. Sumpter

Abstract

Social and economic systems produce complex and nonlinear relationships in the indicator variables that describe them. We present a Bayesian methodology to analyze the dynamical relationships between indicator variables by identifying the nonlinear functions that best describe their interactions. We search for the 'best' explicit functions by fitting data using Bayesian linear regression on a vast number of models and then comparing their Bayes factors. The model with the highest Bayes factor, having the best trade-off between explanatory power and interpretability, is chosen as the 'best' model. To be able to compare a vast number of models, we use conjugate priors, resulting in fast computation times. We check the robustness of our approach by comparison with more prediction oriented approaches such as model averaging and neural networks. Our modelling approach is illustrated using the classical example of how democracy and economic growth relate to each other. We find that the best dynamical model for democracy suggests that long term democratic increase is only possible if the economic situation gets better. No robust model explaining economic development using these two variables was found.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 33%
Student > Master 4 22%
Student > Bachelor 3 17%
Librarian 1 6%
Researcher 1 6%
Other 0 0%
Unknown 3 17%
Readers by discipline Count As %
Social Sciences 3 17%
Psychology 2 11%
Engineering 2 11%
Physics and Astronomy 2 11%
Agricultural and Biological Sciences 1 6%
Other 4 22%
Unknown 4 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 May 2018.
All research outputs
#11,449,179
of 20,582,425 outputs
Outputs from PLOS ONE
#84,773
of 177,649 outputs
Outputs of similar age
#134,008
of 294,989 outputs
Outputs of similar age from PLOS ONE
#1,321
of 2,494 outputs
Altmetric has tracked 20,582,425 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 177,649 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.4. This one has gotten more attention than average, scoring higher than 52% of its peers.
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 294,989 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 2,494 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.