Eric Dignum

Eric Dignum

Postdoctoral researcher in Computational Science interested in understanding socio-economic systems

Posts by Collection

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.

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Modeling Mechanisms of School Segregation and Policy Interventions: A Complexity Perspective

Published in International Conference on Computational Science, 2023

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 mechanisms of school segregation. These features emerge from the interdependence between households, their interactions with school attributes and the institutional contexts in which they reside. 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 and challenges for developing it are discussed and examples are presented demonstrating how new insights and possible policies countering it can be obtained for the cases of primary school segregation in the city of Amsterdam.

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A data-driven agent-based model of primary school segregation in Amsterdam

Published in The Journal of Mathematical Sociology, 2024

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 mostly 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, and 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.

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talks

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