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Uncertainty in Causal Neighborhood Effects: A Multi-Agent Simulation Approach (Short Paper)

Author Cécile de Bézenac

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Cécile de Bézenac
  • University of Leeds, UK
  • The Alan Turing Institute, London, UK

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Cécile de Bézenac. Uncertainty in Causal Neighborhood Effects: A Multi-Agent Simulation Approach (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 26:1-26:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.26

Abstract

Interaction between individuals within an environment can result in complex patterns that a statistical analysis is unable to disentangle. The resulting social structure may pose important challenges for the identification of causal relations between variables using only observational data. In particular, the estimation of contextual or neighborhood effects will depend on the spatial configuration under study and the morphology of the areas used to define them. The relevant interpretation of estimates is hence put into question. I suggest adopting a Agent Based Modeling (ABM) approach to study the uncertainty of neighborhood effect estimations within complex spatial systems. An Approximate Bayesian Computing algorithm is used to quantify the uncertainty on the underlying processes that may lead to such estimations. An ABM model of spatial segregation is implemented to illustrate this method.

Subject Classification

ACM Subject Classification
  • Human-centered computing
Keywords
  • Spatial causal inference
  • neighborhood effects
  • uncertainty
  • Agent Based Modeling
  • Pattern Oriented Modeling

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