Title |
Bayesian Dynamical Systems Modelling in the Social Sciences
|
---|---|
Published in |
PLOS ONE, January 2014
|
DOI | 10.1371/journal.pone.0086468 |
Pubmed ID | |
Authors |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 20% |
Ecuador | 1 | 20% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 80% |
Scientists | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Australia | 1 | 2% |
Sweden | 1 | 2% |
United Kingdom | 1 | 2% |
United States | 1 | 2% |
Croatia | 1 | 2% |
Unknown | 60 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 17 | 26% |
Researcher | 11 | 17% |
Student > Master | 9 | 14% |
Student > Doctoral Student | 4 | 6% |
Professor | 3 | 5% |
Other | 10 | 15% |
Unknown | 11 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Social Sciences | 12 | 18% |
Psychology | 8 | 12% |
Mathematics | 5 | 8% |
Computer Science | 3 | 5% |
Physics and Astronomy | 3 | 5% |
Other | 19 | 29% |
Unknown | 15 | 23% |