Generative modelling and geosimulation

Generative modelling entered the field of geography and spatial analysis some 35 years ago. Besides allowing spatial analysts and geographers to build operational models for transportation and planning, it has represented the opportunity to take causal inference from the traditional (statistical) analysis of empirical models to the design of causal mechanisms simulated in virtual environments. It is a major epistemological shift, whose scientific contribution still needs advocacy in the wider community of geographers and spatial analysts. In parallel, the expansion of both computing power and available data have made it easier for isolated teams and individuals to build ad hoc models fit for their specific research questions, leading to a cacophonous development of generative models unrelated to one another. Since the introduction of generative modelling in geography and spatial analysis, hundreds of models have been developed; all may have been wrong, but some were surely useful. However, most models built over the years have been abandoned, their program either inoperable with today’s technology or lost entirely. Over 35 years, this should strike us as vastly wasteful of ideas, time and energy. In this chapter, I will lay out possible paths towards sustainable, modular and painless generative models, and the expected impacts in terms of geographical theory development and scientific reproducibility. In this way, generative modelling done right can contribute both to a new form of causal inference and to the larger programme of social sciences: the simultaneous search for generalised explanations of social phenomena and recognition of the uniqueness of historical events.