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Bayesian Dynamical Systems Modelling in the Social Sciences
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
2014 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 1, e86468- p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2014. Vol. 9, no 1, e86468- p.
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:uu:diva-219971DOI: 10.1371/journal.pone.0086468ISI: 000330240500135OAI: oai:DiVA.org:uu-219971DiVA: diva2:704537
Available from: 2014-03-12 Created: 2014-03-09 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Non-linear dynamic modelling for panel data in the social sciences
Open this publication in new window or tab >>Non-linear dynamic modelling for panel data in the social sciences
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Non-linearities and dynamic interactions between state variables are characteristic of complex social systems and processes. In this thesis, we present a new methodology to model these non-linearities and interactions from the large panel datasets available for some of these systems. We build macro-level statistical models that can verify theoretical predictions, and use polynomial basis functions so that each term in the model represents a specific mechanism. This bridges the existing gap between macro-level theories supported by statistical models and micro-level mechanistic models supported by behavioural evidence. We apply this methodology to two important problems in the social sciences, the demographic transition and the transition to democracy.

The demographic transition is an important problem for economists and development scientists. Research has shown that economic growth reduces mortality and fertility rates, which reduction in turn results in faster economic growth. We build a non-linear dynamic model and show how this data-driven model extends existing mechanistic models. We also show policy applications for our models, especially in setting development targets for the Millennium Development Goals or the Sustainable Development Goals.

The transition to democracy is an important problem for political scientists and sociologists. Research has shown that economic growth and overall human development transforms socio-cultural values and drives political institutions towards democracy. We model the interactions between the state variables and find that changes in institutional freedoms precedes changes in socio-cultural values. We show applications of our models in studying development traps.

This thesis comprises the comprehensive summary and seven papers. Papers I and II describe two similar but complementary methodologies to build non-linear dynamic models from panel datasets. Papers III and IV deal with the demographic transition and policy applications. Papers V and VI describe the transition to democracy and applications. Paper VII describes an application to sustainable development.

Place, publisher, year, edition, pages
Uppsala: Department of Mathematics, 2015. 40 p.
Series
Uppsala Dissertations in Mathematics, ISSN 1401-2049 ; 91
Keyword
Dynamical systems, stochastic models, Bayesian, panel data, social sciences, development
National Category
Mathematics
Research subject
Mathematics with specialization in Applied Mathematics
Identifiers
urn:nbn:se:uu:diva-261289 (URN)978-91-506-2481-6 (ISBN)
Public defence
2015-11-06, Polhemsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 10:00 (English)
Opponent
Supervisors
Available from: 2015-10-14 Created: 2015-09-01 Last updated: 2016-01-27

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Ranganathan, ShyamMann, Richard P.Sumpter, David J. T.

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