Multiagent geographical models integrate very large numbers of spatial interactions. In order to validate these models a large amount of computing is necessary for their simulation and calibration. Here a new data-processing chain, including an automated calibration procedure, is tested on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimize three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. Thus the model is a useful tool for further multiple applications in empirical historical situations.
The aim of this chapter is to present the main issues with embedding territorial representation and territorial dynamics in simulation models . First we depict current scientific practices, illustrated and illustrate them with select model examples. Second, propose a singular and reproducible modelling strategy, which aims specifically at describing a territorial system and its evolution. . This strategy relies on multi-modelling or incremental modelling. This chapter ends with a presentation of the limits and opportunities of this approach, with a discussion of its applicability interest to different case studies.
