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 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 geographical 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 paper, we present a method to assess the effect of some initial spatial conditions on simulation models, using a systematic spatial configuration generator in order to create density grids with which spatial simulation models are initialised. We show, with the example of two classical agent-based models (Schelling's models of segregation and Sugarscape's model of unequal societies) and a straightforward open-source work-flow using high performance computing, that the effect of initial spatial arrangements is significant on the two models. Furthermore, this effect is sometimes larger than the effect of parameters' value change.

中文翻译：

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空间问题：在地球模拟模型中将灵敏度分析扩展到初始空间条件

尽管地理系统的仿真模型通常是通用模型，尤其是基于代理的模型代表了探索社会空间行为并测试各种公共政策场景的绝妙机会，但是除非能够证明生成结果的可靠性和可靠性，否则生成模型的有效性尚不确定。代表“现实世界”条件。敏感性分析通常包括分析随机性对结果变异性的影响以及参数变化小的影响。但是，初始空间条件通常不会在地理模型中进行系统地修改，因此，尚未探讨初始空间安排对主体之间以及主体与主体之间的相互作用的影响。在本文中，我们提出了一种使用系统空间配置生成器来评估一些初始空间条件对模拟模型的影响的方法，以便创建用于初始化空间模拟模型的密度网格。我们以两个经典的基于主体的模型（舒林的种族隔离模型和不平等社会的糖景模型）以及使用高性能计算的直接开源工作流的示例为例，表明初始空间安排对两种模式。此外，这种影响有时大于参数值更改的影响。以两个经典的基于代理的模型（舒林的隔离模型和不平等社会的糖景模型）以及使用高性能计算的简单开源工作流为例，初始空间排列对这两个模型的影响很大。此外，这种影响有时大于参数值更改的影响。以两个经典的基于代理的模型（舒林的隔离模型和不平等社会的糖景模型）以及使用高性能计算的简单开源工作流为例，初始空间排列对这两个模型的影响很大。此外，这种影响有时大于参数值更改的影响。