License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.26
URN: urn:nbn:de:0030-drops-189210
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18921/
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de Bézenac, Cécile

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

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LIPIcs-GIScience-2023-26.pdf (1 MB)

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.

BibTeX - Entry

@InProceedings{debezenac:LIPIcs.GIScience.2023.26,
  author =	{de B\'{e}zenac, C\'{e}cile},
  title =	{{Uncertainty in Causal Neighborhood Effects: A Multi-Agent Simulation Approach}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{26:1--26:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18921},
  URN =		{urn:nbn:de:0030-drops-189210},
  doi =		{10.4230/LIPIcs.GIScience.2023.26},
  annote =	{Keywords: Spatial causal inference, neighborhood effects, uncertainty, Agent Based Modeling, Pattern Oriented Modeling}
}

Keywords: Spatial causal inference, neighborhood effects, uncertainty, Agent Based Modeling, Pattern Oriented Modeling
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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