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Using Bayesian dynamical systems, model averaging and neural networks to determine interactions between
Björn R H Blomqvist1, Richard P Mann2, David J T Sumpter1
1Uppsala University, Department of Mathematics, Uppsala, Sweden.
Plos One
|May 10, 2018
Summary
This study introduces a Bayesian method to analyze complex social and economic systems. It found that economic growth is necessary for long-term democratic progress, but democracy doesn't reliably predict economic development.
Area of Science:
- Social Sciences
- Economics
- Political Science
Background:
- Social and economic systems exhibit complex, nonlinear relationships between indicator variables.
- Analyzing these dynamics requires advanced statistical methodologies to uncover underlying interactions.
Purpose of the Study:
- To present a novel Bayesian methodology for analyzing dynamical relationships between indicator variables in social and economic systems.
- To identify nonlinear functions that best describe the interactions between these variables.
- To develop a robust model selection process balancing explanatory power and interpretability.
Main Methods:
- Utilized Bayesian linear regression on a large number of models to identify nonlinear interaction functions.
- Employed Bayes factors for model comparison, selecting the model with the highest Bayes factor.
- Used conjugate priors for efficient computation, enabling the analysis of numerous models.
- Validated the approach by comparing it with prediction-focused methods like model averaging and neural networks.
Main Results:
- The best dynamical model identified indicates that sustained increases in democracy are contingent upon economic improvement.
- No robust model was found to explain economic development based on democratic indicators within this framework.
- The methodology provides a robust way to model complex system dynamics.
Conclusions:
- Economic growth is a prerequisite for long-term democratic advancement.
- The relationship between democracy and economic development is asymmetric and not fully captured by current models.
- The proposed Bayesian approach offers a powerful tool for understanding complex social and economic dynamics.
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