Feeding the beast: can computational demographic models free us from the tyranny of data?

Since its inception, ALife has moved from producing large numbers of highly-idealised, theoretical models towards greater integration with empirically collected data. In contrast, demography — the interdisciplinary study of human populations — has been largely following the principles of logical empiricism, with models driven mainly by data, and insufficient attention being paid to theoretical investigation. Such an approach reduces the ability to produce micro-level explanations of population processes, which would be coherent with the phenomena observed at the macro level, without having to rely on ever-increasing data demands of complex demographic models. In this paper we argue that by bringing ALife-inspired, agent-based methods into demographic research, we can both develop a greater understanding of the processes underlying demographic change, and avoid a limiting over-dependence on potentially immense sets of data