2014
DOI: 10.1371/journal.pone.0086468
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Bayesian Dynamical Systems Modelling in the Social Sciences

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 wi… Show more

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Cited by 51 publications
(59 citation statements)
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“…For this, we assume a uniform, non-informative prior on the parameter space, so that every possible model coefficient is weighted equally (conditioned on the corresponding term being included in the submodel). This penalizes more complex models with larger number of terms as the dimension of the parameter space increases with increasing number of terms (see Ranganathan et al, 2014 for a fuller description of the approach).…”
Section: Methodsmentioning
confidence: 99%
“…For this, we assume a uniform, non-informative prior on the parameter space, so that every possible model coefficient is weighted equally (conditioned on the corresponding term being included in the submodel). This penalizes more complex models with larger number of terms as the dimension of the parameter space increases with increasing number of terms (see Ranganathan et al, 2014 for a fuller description of the approach).…”
Section: Methodsmentioning
confidence: 99%
“…We remain here on a relative abstract level, noting however that standard country-specific indicators [17,18] for both democracy and values may be taken as proxies for D and V . Alternatively one may consider the level of economic development, instead of the cultural dimension, as the basic variable interacting with the state of the democracy [19].…”
Section: Modelmentioning
confidence: 99%
“…These methods include frequency counts, a graphical representation of observed changes that can be compared to expected changes, dependency analyses inspired by the logic of Qualitative Comparative Analysis (Ragin 1987, Rihoux andRagin 2009) as well as evolutionary biology (Sillén-Tullberg 1993), and Bayesian dynamical systems Ranganathan et al 2014). This combination of methods is especially useful when determining reform sequences or investigating if there are differences in reform paths between, for example, successful and unsuccessful attempts at, for example, democratization.…”
Section: Introductionmentioning
confidence: 99%