Mathematical and Computational Modelling of World Data

In 2015 the UN has passed a set of 17 Sustainable Development Goals, placing all countries of the world under the obligation to overcome extreme poverty, ensure socio-economic inclusion while protecting the planet. Almost five year into this ambitious 15-years agenda, are countries around the world on the right track to achieve these goals? And how can the conflicts that may arise between the various goals be overcome? With rich cross-country-time-series data increasingly available on various economic, social, demographic, environmental, political and administrative indicators, provided by the UN (http://data.un.org/), World Bank API (https://goo.gl/UpL3t6), Human Rights Data project (http://www.humanrightsdata.com/), V-Dem project (https://www.v-dem.net/en/data/data-version-6-2/) etc. we want to better understand the pathways on which countries are currently developing and how these pathways can be steered towards the vision expressed in the Sustainable Development Goals. Specifically, we want to develop a mathematical modeling approach that would allow us to better understand interactive and often non-linear dynamics of political, social, economic and environmental change, testing on the one hand conflicting theories e.g. on sustainable development and on the other hand exploring and inspiring new theoretical thinking. The PhD student on this project will be expected to create and constantly update a comprehensive cross-country, time-series dataset from the various sources such as World Bank, UN etc. But their main task will be to develop a new R and/or Python package, building on the Bayesian Dynamical Model R package bdynsys and building on the recent work by Dr. Richard Mann and Dr. Viktoria Spaiser, in collaboration with researchers at the Uppsala University in Sweden (Prof. David J.T. Sumpter & Björn Blomqvist) and Virginia Tech in USA (Dr. Shyam Ranganathan). The goal is to develop appropriate computational and mathematical tools to analyze the collected data and generate insights that will enhance our understanding of complex international and national processes and dynamics and that may be potentially relevant to policy making (e.g. monitoring, supporting and steering the implementation of the UN Sustainable Development Goals, http://www.undatarevolution.org/).