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Bayesian Dynamical Systems Modelling in the Social Sciences

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  • Shyam Ranganathan
  • Viktoria Spaiser
  • Richard P Mann
  • David J T Sumpter

Abstract

Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach.

Suggested Citation

  • Shyam Ranganathan & Viktoria Spaiser & Richard P Mann & David J T Sumpter, 2014. "Bayesian Dynamical Systems Modelling in the Social Sciences," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-9, January.
    • Handle: RePEc:plo:pone00:0086468
    DOI: 10.1371/journal.pone.0086468
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    References listed on IDEAS

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    Cited by:

    1. Viktoria Spaiser & Shyam Ranganathan & Richard P Mann & David J T Sumpter, 2014. "The Dynamics of Democracy, Development and Cultural Values," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-11, June.
    2. Ranganathan, Shyam & Bali Swain, Ranjula & Sumpter, David, 2014. "A Dynamical Systems Approach To Modeling Human Development," Working Paper Series 2014:9, Uppsala University, Department of Economics.
    3. Orazio Angelini & Matthieu Cristelli & Andrea Zaccaria & Luciano Pietronero, 2016. "The complex dynamics of products and its asymptotic properties," Papers 1610.00274, arXiv.org, revised May 2017.

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