Eric Dignum

Eric Dignum

PhD student Computational Science interested in understanding socio-economic systems

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Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Mechanisms for increased school segregation relative to residential segregation: a model-based analysis

Published in Computers, Environment and Urban Systems, 2022

Excess school segregation is a phenomena observed across many countries and one common explanation from the literature is the hypothesis that parents might want to live in a diverse neighbourhood, but when it comes to their children, they are less tolerant with respect to school compositions. This study uses an agent-based model where households face residential decisions depending on neighbourhood compositions and make school choices based on distance and school compositions. Results indicate that increased school segregation relative to residential segregation can be observed in large parts of the parameter space, even when the tolerance for households belonging to the other group is equal for neighbourhood and school compositions. Our results demonstrate that asymmetric preferences are not a requirement for excess school segregation and show that residential segregation combined with distance preferences play a key role in this increase.

Recommended citation: Dignum, E., Athieniti, E., Boterman, W., Flache, A., & Lees, M. (2022). Mechanisms for increased school segregation relative to residential segregation: A model-based analysis. Computers, Environment and Urban Systems, 93, 101772. https://doi.org/10.1016/j.compenvurbsys.2022.101772

Modelling school choice as a complex system: understanding school segregation

Published in Working paper, 2022

We revisit literature about school choice and school segregation from the perspective of complexity theory. This paper argues that commonly found features of complex systems are all present in the dynamics of school choice. These features emerge from the interdependence between households, their interactions with school attributes and the institutional contexts in which they reside. While the dynamics of school choice exhibit features of a complex system in general, context is found to be important for how to apply complexity concepts exactly. We propose that a social complexity perspective can add to providing new generative explanations of resilient patterns of school segregation and may help identifying policies towards robust school integration. This requires a combination of theoretically informed computational modeling with empirical data about specific social and institutional contexts. We argue that this combination is missing in currently employed methodologies in the field. Pathways for developing it are discussed.

Unintended school segregation? An empirically calibrated agent-based model of Amsterdam primary school choice

Published in Working paper, 2022

Theoretical agent-based models of residential and school choice have shown that substantial segregation can emerge as an (unintended) consequence of interactions between individual households and feedback mechanisms, despite households being relatively tolerant. However, for school choice, existing models have been highly stylized, leaving open whether they are relevant for understanding school segregation in concrete empirical settings. To bridge this gap, this study develops an empirically calibrated agent-based model focusing on primary school choice in Amsterdam. Consistent with existing models, results show that substantial school segregation emerges when schools are chosen based on a trade-off between composition and distance, also when households are relatively tolerant. Additionally, findings of (hypothetical) policy simulations suggest that it is important to understand which preferences for school composition and distance households have and how these interact. We find that the effects of policies aiming to reduce school segregation through geographical restricting mechanisms are highly dependent on those interacting preferences. Also, we assessed the contribution of residential segregation to school segregation. Our findings may have implications for methodologies aiming to estimate school choice preferences, such as discrete choice models, as these methodologies do not explicitly control for implications of these interactions and feedback mechanisms, which might lead to incorrect inference.

talks

Empirical calibration of full scale agent-based models of school choice: computational challenges and model validation

Published:

School segregation is widely associated with existing inequalities and their reproduction. Although it has been studied for decades and using various methods/techniques, it still is a persistent problem in society. Currently employed methodologies often treat households on the micro-level as utility maximising individuals that decide in isolation or analyse macroscopic trends and correlations. However, these methodologies might miss important interactions within and between these levels. For example, parents rely on their social networks, observe current school compositions, school profiles, live in segregated neighbourhoods and are subject to institutional rules, hence they interact with each other and their environment. Simulation-based techniques, such as Agent-Based Models (ABM) provide a way to explicitly model these features and have shown promising results in other fields of science. Existing ABM of school choice are mostly based on theoretical rules and smaller scales, hence do not take the full scale of the city into account. On the other hand, currently used fully data driven methods, such as discrete choice analysis, do not consider the potentially complex interactions. Therefore, we present results of one of the first empirically calibrated ABM of school choice on the Amsterdam scale. However, key challenges of ABM are empirical calibration and validation of their simulated (household) behaviour. These are important for confidence in the model and could inform potential policy, but requires a lot of data and computation. Multiple runs are needed to grasp how sensitive the model is to its input parameters, to quantify uncertainty and analyse the impact of specific scenarios. We show some of the benefits of our modelling approach as well as some of the computational difficulties and existing challenges we encountered while modelling school segregation at the Amsterdam scale.

teaching

Agent-Based Modelling

Master course, University of Amsterdam, Institute for Informatics, 2020

Teaching Assistant

Agent-Based Modelling

Master course, University of Amsterdam, Institute for Informatics, 2021

Teaching Assistent