Economic resources (typically wealth and income) are distributed very unevenly among people. In parallel, people with similar economic levels tend to concentrate spatially, in similar cities, in cities of similar sizes, and in similar neighbourhoods, in which case we call it urban segregation. Economic inequality and urban segregation are considered top priority challenges by the United Nations. Tackling them is urgent because economic inequality and urban segregation restrict the ability of the poorest individuals to get by in life, but they also affect life expectancy, social justice and cohesion for everyone.
One way to understand and address urban economic segregation is to model their dynamics, and run policy scenarios on how to reduce it. An abundant literature uses statistical modelling for that purpose, and struggles to assess causality in the processes at play. A complementary approach to statistical modelling is generative modelling and the simulation of causal rules of action on an articial society of computational agents. Agent-based modelling, whilst far from dominant, has long proven its potential to represent diverse societies of individuals and the emergence of unexpected collective behaviours in the field of economic inequality and spatial segregation (cf. Schelling or Sugarscape for basic examples).
This PhD represents an exciting opportunity to develop cutting edge methods of agent-based model building and analysis on a socially relevant topic. You will be expected to translate theoretical mechanisms of economic inequality and segregation into programmable rules of actions for agents within an agent-based model. You will implement these mechanisms and integrate them into a modular agent-based model. This (multi-)model will be initialized, calibrated and validated against unique empirical microdata (i.e. the exhaustive register data from the Dutch register, provided by CBS). The ambition is to create a theoretically sound model which is able to represent and reproduce the evolution of economic segregation in The Netherlands over the past 10 years. Building blocks the model will correspond to the implementation of middle theories of how inequality and segregation are (re)produced through space. The calibrated model will be dedicated to compare policy scenarios of inequality reduction.
Modular model integration and reusable building blocks are a hot (and unresolved) topic of agent-based modelling at the moment. This case will present the challenge of involving multidisciplinary and multi-scale building blocks to integrate, but it will be backed by a strong theoretical support and exceptional individual, longitudinal, exhaustive and spatial data from CBS. The completion of this research program should therefore lead to three areas of innovation. First, you will advance research on economic inequality and urban segregation by providing a simulation tool that integrates multiscale explanations and provides scenarios of inequality reduction. This generative approach will complement the existing literature on economic inequality and urban segregation, which relies for the most part on statistical analysis of empirical data. Second, you will advance the current state of modelling methodology by approaching it from the start with the idea of reusable building blocks and their integration, from initialization to calibration, which is ambitious and new. Thirdly, you will find a way to calibrate the model with quality individual longitudinal and spatial microdata accessible from a secure environment.
You will be supported in this research by the PhD supervisor Dr. Clémentine Cottineau, the PhD promotors (Prof. Tatiana Filatova and Prof. Maarten van Ham) and a post-doctoral researcher to be recruited on the same project.
The successful candidate for this position has:
Nice to have: