Initial spatial conditions in simulation models: the missing leg of sensitivity analyses?

Abstract : Although simulation models of geographical systems in general and agent-based models in particular represent a fantastic opportunity to explore socio-spatial behaviours and to test a variety of scenarios for public policy, the validity of generative models is uncertain until their results are proven robust. Sensitivity analysis usually include the analysis of the effect of stochasticity on the variability of results, as well as the effects of small parameter changes. However, initial spatial conditions are usually taken for granted in geographical models, thus leaving completely unexplored the effect of spatial arrangements on the interaction of agents and of their interactions with the environment. In this contribution, we present a method to assess the effect of initial spatial conditions on simulation models, using a systematic generator controlled by meta-parameter to create density grids used in spatial simulation models. We show, with the example of two very classical agent-based models (Schelling's models of segregation and Sugarscape) that the effect of space in simulation is significant, and sometimes even larger than parameters themselves. We do so using high performance computing in a very simple and straightforward open-source workflow.
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Conference papers
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https://halshs.archives-ouvertes.fr/halshs-01841272
Contributor : Juste Raimbault <>
Submitted on : Tuesday, July 17, 2018 - 10:59:06 AM
Last modification on : Wednesday, July 17, 2019 - 2:34:38 AM
Long-term archiving on : Thursday, October 18, 2018 - 12:55:43 PM

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Clémentine Cottineau, Juste Raimbault, Marion Le Texier, Florent Le Néchet, Romain Reuillon. Initial spatial conditions in simulation models: the missing leg of sensitivity analyses?. 2017 International Conference on GeoComputation: Celebrating 21 Years of GeoComputation, Sep 2017, Leeds, United Kingdom. ⟨halshs-01841272⟩

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