Bayesian dynamical systems modelling in the social sciences.
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.
Department of Mathematics, Uppsala University, Uppsala, Sweden.
Shyam
S
Ranganathan
Institute for Futures Studies, Stockholm, Sweden.
Viktoria
V
Spaiser
Department of Mathematics, Uppsala University, Uppsala, Sweden ; Institute for Futures Studies, Stockholm, Sweden.
Richard P
RP
Mann
Department of Mathematics, Uppsala University, Uppsala, Sweden ; Institute for Futures Studies, Stockholm, Sweden.
David J T
DJ
Sumpter
D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D012942:Social Sciences / Q000379:methods*