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Growing Models from the Bottom Up. an Evaluation-Based Incremental Modelling Method (EBIMM) Applied to the Simulation of Systems of Cities

Clémentine Cottineau, Paul Chapron and Romain Reuillon
Journal of Artificial Societies and Social Simulation 18 (4) 9

Abstract: This paper presents an incremental method of parsimonious modelling using intensive and quantitative evaluation. It is applied to a research question in urban geography, namely how well a simple and generic model of a system of cities can reproduce the evolution of Soviet urbanisation. We compared the ability of two models with different levels of complexity to satisfy goals at two levels. The macro-goal is to simulate the evolution of the system’s hierarchical structure. The micro-goal is to simulate its micro-dynamics in a realistic way. The evaluation of the models is based on empirical data through a calibration that includes sensitivity analysis using genetic algorithms and distributed computing. We show that a simple model of spatial interactions cannot fully reproduce the observed evolution of Soviet urbanisation from 1959 to 1989. A better fit was achieved when the model’s structure was complexified with two mechanisms. Our evaluation goals were assessed through intensive sensitivity analysis. The complexified model allowed us to simulate the evolution of the Soviet urban hierarchy.

Space Matters: Extending Sensitivity Analysis to Initial Spatial Conditions in Geosimulation Models

Juste Raimbault, Clémentine Cottineau, Marion Le Texier, Florent Le Nechet and Romain Reuillon
Journal of Artificial Societies and Social Simulation 22 (4) 10

Abstract: Although simulation models of socio-spatial 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 unless their results are proven robust and representative of 'real-world' conditions. Sensitivity analysis usually includes 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 not modified systematically in socio-spatial models, thus leaving unexplored the effect of initial spatial arrangements on the interactions of agents with one another as well as with their environment. In this article, we present a method to assess the effect of variation of some initial spatial conditions on simulation models, using a systematic geometric structures generator in order to create density grids with which socio-spatial simulation models are initialised. We show, with the example of two classical agent-based models (Schelling's model of segregation and Sugarscape's model of unequal societies) and a straightforward open-source workflow using high performance computing, that the effect of initial spatial arrangements is significant on the two models. We wish to illustrate the potential interest of adding spatial sensitivity analysis during the exploration of models for both modellers and thematic specialists.