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Bayesian dynamical systems modelling in the social sciences.

Shyam Ranganathan1, Viktoria Spaiser2, Richard P Mann3

  • 1Department of Mathematics, Uppsala University, Uppsala, Sweden.

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Summary
This summary is machine-generated.

We introduce a novel differential equation method to analyze complex social science data, revealing non-linear interactions between variables like democracy and economic growth for better model explanatory power.

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Area of Science:

  • Social Sciences
  • Econometrics
  • Political Science

Background:

  • Social systems data often exhibit complex, non-linear relationships between macro-level variables.
  • Analyzing longitudinal or panel data requires methods capable of capturing these intricate dynamics.

Purpose of the Study:

  • To present a novel method for analyzing complex longitudinal/panel social science data using differential equations.
  • To identify optimal non-linear functions and interaction terms for enhanced model explanatory power.

Main Methods:

  • Utilized differential equations to model non-linear relationships in social system data.
  • Employed Bayes factor to determine the optimal number of interaction terms, penalizing model complexity.
  • Applied the method to the democracy-economic growth relationship as a case study.

Main Results:

  • Successfully identified non-linear relationships between democracy and economic growth.
  • Demonstrated the method's ability to account for multiple variables and time lags.
  • Developed an R toolbox for practical implementation of the proposed analytical approach.

Conclusions:

  • The differential equation approach provides a robust framework for analyzing complex social system dynamics.
  • The method effectively balances model complexity with explanatory power, offering superior insights.
  • This approach facilitates a deeper understanding of interdependencies between key social and economic variables.