Dr Viktoria Spaiser
University Academic Fellow in Political Science Informatics
I have a background in Sociology (PhD , Bielefeld University, Germany, 2012), Political Science (MA in Conflict, Security and Development, King’s College London, UK, 2008) and Computer Science (German Diploma, University of Applied Sciences Trier, Germany, 2013). I was a visiting researcher in the Computational Social Science Research Group at ETH Zurich in 2012 and a postdoctoral researcher at the Institute for Futures Studies Stockholm (2012-2014) and at the Department of Mathematics, Uppsala University in Sweden (2014-2015). Since August 2015 I am the UAF in Political Science Informatics at the University of Leeds, POLIS. I am also affiliated with the Leeds Institute for Data Analytics (LIDA).
I am interested in applying mathematical and computational approaches (such as Dynamical Systems Modelling, Bayesian Statistics, Agent Based Modelling and Data Science Approaches) to social and political science research questions. I have been working on a wide range of topics, including anti-Semitism, political participation, democratisation, development and segregation, using various data sources such as register data, cross-country panel data and Twitter data. Recently I am increasingly interested in commons dilemmas and in combining data science and experimental methods.
Currently I am assisting in teaching the modules “Approaches to Analysis” (PIED2721) and “Analysing Data in Politics, Development and IR “ (PIED3701). In future, I will offer modules on “Introduction to Programming for Political Scientists” and others.
I am willing to supervise PhD students in the areas Political Science Informatics (Data Science, Big Data etc. in Political Science Research Context), Computational Social Science (Data Science, Big Data, Computer Simulation etc. in Social Science Research Context), Mathematical Modelling in Political Science, Statistical Political Science Research, Democratisation and Sustainability (in particular Commons Dilemmas).
‘The Sustainable Development Oxymoron: Quantifying and Modelling the Incompatibility of Sustainable Development Goals’, International Journal of Sustainable Development and World Ecology 2016,
DOI: 10.1080/13504509.2016.1235624, Repository URL: http://eprints.whiterose.ac.uk/104415/
In 2015, the UN adopted a new set of Sustainable Development Goals (SDGs) to eradicate poverty, establish socioeconomic inclusion and protect the environment. Critical voices such as the International Council for Science, however, have expressed concerns about the potential incompatibility of the SDGs, specifically the incompatibility of socio-economic development and environmental sustainability. In this paper we test, quantify and model the alleged inconsistency of SDGs. Our analyses show which SDGs are consistent and which are conflicting. We measure the extent of inconsistency and conclude that the SDG agenda will fail as a whole if we continue with business as usual. We further explore the nature of the inconsistencies using dynamical systems models, which reveal that the focus on economic growth and consumption as a means for development underlies the inconsistency. Our models also show that there are factors which can contribute to development (health programs, government investment in education) on the one hand and ecological sustainability (renewable energy) on the other, without triggering the conflict between incompatible SDGs.
© 2016, University of Surrey. All rights reserved.Agent-based models and computer simulations are promising tools for studying emergent macrophenomena. We apply an agent-based approach in combination with data analysis to investigate the human development sequence (HDS) theory developed by Ronald Inglehart and Christian Welzel. Although the HDS theory is supported by correlational evidence, the sequence of economic growth, democracy andemancipation stated by the theory is not entirely consistent with data. We use an agent-based model to make quantitative predictions about several different micro-level mechanisms. Comparison to data allows us to identify important inconsistencies between HDS and the data, and propose revised agent-based models that modify the theory. Our results indicate the importance of elites and economic inequality in explaining the data available on democratisation.
‘Identifying Complex Dynamics in Social Systems: A New Methodological Approach Applied to Study School Segregation’, Sociological Methods and Research 2015,
DOI: 10.1177/0049124116626174, Repository URL: http://eprints.whiterose.ac.uk/92891/
It is widely recognized that segregation processes are often the result of complex nonlinear dynamics. Empirical analyses of complex dynamics are however rare, because there is a lack of appropriate empirical modeling techniques that are capable of capturing complex patterns and nonlinearities. At the same time, we know that many social phenomena display nonlinearities. In this article, we introduce a new modeling tool in order to partly fill this void in the literature. Using data of all secondary schools in Stockholm county during the years 1990 to 2002, we demonstrate how the methodology can be applied to identify complex dynamic patterns like tipping points and multiple phase transitions with respect to segregation. We establish critical thresholds in schools’ ethnic compositions, in general, and in relation to various factors such as school quality and parents’ income, at which the schools are likely to tip and become increasingly segregated.
Methods from machine learning and data science are becoming increasingly important in the social sciences, providing powerful new ways of identifying statistical relationships in large data sets. However, these relationships do not necessarily offer an understanding of the processes underlying the data. To address this problem, we have developed a method for fitting nonlinear dynamical systems models to data related to social change. Here, we use this method to investigate how countries become trapped at low levels of socioeconomic development. We identify two types of traps. The first is a democracy trap, where countries with low levels of economic growth and/or citizen education fail to develop democracy. The second trap is in terms of cultural values, where countries with low levels of democracy and/or life expectancy fail to develop emancipative values. We show that many key developing countries, including India and Egypt, lie near the border of these development traps, and we investigate the time taken for these nations to transition toward higher democracy and socioeconomic well-being.
Over the past decades many countries have experienced rapid changes in their economies, their democratic institutions and the values of their citizens. Comprehensive data measuring these changes across very different countries has recently become openly available. Between country similarities suggest common underlying dynamics in how countries develop in terms of economy, democracy and cultural values. We apply a novel Bayesian dynamical systems approach to identify the model which best captures the complex, mainly non-linear dynamics that underlie these changes. We show that the level of Human Development Index (HDI) in a country drives first democracy and then higher emancipation of citizens. This change occurs once the countries pass a certain threshold in HDI. The data also suggests that there is a limit to the growth of wealth, set by higher emancipation. Having reached a high level of democracy and emancipation, societies tend towards equilibrium that does not support further economic growth. Our findings give strong empirical evidence against a popular political science theory, known as the Human Development Sequence. Contrary to this theory, we find that implementation of human-rights and democratisation precede increases in emancipative values. © 2014 Spaiser et al.
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.
Software / Code
The R package bdynsys for panel/longitudinal data combines methods to model changes in up to four indicators over times as a function of the indicators themselves and up to three predictors using ordinary differential equations (ODEs) with polynomial terms that allow to model complex and nonlinear effects. A Bayesian model selection approach is implemented. The package provides also visualisation tools to plot phase portraits of the dynamic system, showing the complex co-evolution of two indicators over time with the possibility to highlight trajectories for specified entities (e.g. countries, individuals). Furthermore the visualisation tools allow for making predictions of the trajectories of specified entities with respect to the indicators.
Media Contact Areas
- Big Data and Policy
- UN Sustainable Development Goals