ArticlePloS one2014
Bayesian dynamical systems modelling in the social sciences.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Coupling between Blood Pressure and Subarachnoid Space Width Oscillations during Slow Breathing.Entropy (Basel, Switzerland) · 2021Article
- Time Window Determination for Inference of Time-Varying Dynamics: Application to Cardiorespiratory Interaction.Frontiers in physiology · 2020Article
- Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
- Synchronization transitions caused by time-varying coupling functions.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
- Sequential Requisites Analysis: A New Method for Analyzing Sequential Relationships in Ordinal Data.Social science quarterly · 2019Article
- Self-Determined Motivation and Competitive Anxiety in Athletes/Students: A Probabilistic Study Using Bayesian Networks.Frontiers in psychology · 2019Article
- Using Bayesian dynamical systems, model averaging and neural networks to determine interactions between socio-economic indicators.PloS one · 2018Article
- Choice modelling with Gaussian processes in the social sciences: A case study of neighbourhood choice in Stockholm.PloS one · 2018Article
- Women's rights in democratic transitions: A global sequence analysis, 1900-2012.European journal of political research · 2017Article
- Neural Cross-Frequency Coupling Functions.Frontiers in systems neuroscience · 2017Article
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- The dynamics of democracy, development and cultural values.PloS one · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.