We will never approach the vision of Isaac Asimov[2], whose ‘psychohistorian’, Hari Seldon made detailed predictions over millennia with an accuracy and precision approaching those of orbital mechanics. The data involved is too chaotic for even a remote approximation to that. Hypotheses can, however, be generated that are testable within bounds useful and valuable in budgetary and policy planning terms – and even an indicative pattern of association, with no hypothesised explanation, can improve the effectiveness of resource targeting. [more]
Image: Visualising individuals’ movement through time and space (from Malleson, 2008[13]; GeoTime® software used courtesy of Oculus Info Inc. All GeoTime rights reserved.)
3 comments:
Impressive. I like SWARM.
Great article! It's really interesting and I think you've really captured the essence of why we use ABM in social sciences. Thanks again for using my work as a case study, if you want any pointers/advice/info in the future please let me know.
Felix, is there a real difference between SWARM and SIMILE? It seems like both permit multiple, independently running collections of processes (I assume represented by differential equations) with cross communication.
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