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

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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.

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Blomqvist, B. R. H., Mann, R. P., & Sumpter, D. J. T. (2018). Using bayesian dynamical systems, model averaging and neural networks to determine interactions between socio-economic indicators. PLoS ONE, 13(5). https://doi.org/10.1371/journal.pone.0196355

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