Learning for the Future
The example of the Maya work described in Progress with Prior Support above is a first attempt to move beyond learning from the past to learning for the future (van der Leeuw et al., 2011) about the complex adaptive system that is Earth, at the regional scale. Our first tasks in the current project will therefore be:
- To widen the set of case studies by completion of the modeling of the case studies on Australian hunter-gatherer societies and the Roman Empire, and addition of examples from the circum-Arctic and South-East Asian tropical areas, and for island societies (in the Pacific). We will do this by linking up with the NABO (McGovern, PI) , Greater Angkor Project (Fletcher, PI) and two Oceanian projects (Kirch, PI) respectively.
- Other suitable linkups will actively be promoted, with the aim of having the widest possible coverage of different environments and different kinds of societies, from the tropics via the temperate zone to the Arctic, from hunter-gatherers to urban societies, and both island and continental cases.
Regional modelling of complex adaptive socio-environmental systems
The second set of tasks will concern the exploitation of these models to better understand the general nature of such regional complex adaptive socio-environmental systems.
- To evaluate the models against existing datasets, in particular by comparing model-based with data-based reconstructions of past trajectories in similar circumstances
- To compare and experiment with the models in order to identify (a) general dynamic trends (second-order dynamics), (b) evaluate alternative scenarios in each case-study, and (c) look for the unintended consequences of systemic ‘decisions’ made, which later led to challenges.
- Link the results of these steps to some of the IAM models (below) in order to (a) show some of the understanding that is gained by looking at the IAM cases over the long term, and (b) see which (and how) insights derived from the long-term studies may contribute to our understanding of the future of IAM’s.
Overall aim: Developing theory and specific techniques for applying knowledge gained from historico-environmental research to future conditions
(Lead: Sander van der Leeuw)