Blomqvist, BRH, Mann, RP orcid.org/0000-0003-0701-1274 and Sumpter, DJT (2018) Using Bayesian dynamical systems, model averaging and neural networks to determine interactions between socio-economic indicators. PLoS ONE, 13 (5). e0196355. ISSN 1932-6203
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Blomqvist et al. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Apr 2018 09:36 |
Last Modified: | 25 Jun 2023 21:19 |
Status: | Published |
Publisher: | Public Library of Science |
Identification Number: | 10.1371/journal.pone.0196355 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129809 |