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  • Systems Science and Data Science are two rapidly developing areas of computational science that have been demonstrated to offer tremendous capacity to inform health understanding, and which are applied by a growing number of projects in health and health care.  While each of approaches tap the power of computational models, they have largely traditionally been pursued in isolation from each other.  This is particularly ironic, because the techniques are not merely highly compatible -- for example, in each using computational or informatics mechanisms to provide temporally and locationally fine grained longitudinal understanding across multiple generative pathways -- but synergistic, with each tradition opening strong opportunities for empowering the other, and with the combination of both yielding opportunities for insight and improved decision making far beyond the sum of what each can bring in isolation.  It also seems increasingly clear that.  We present here a vision and...
  • In the context of a complex and uncertain world, models help us learn more quickly, deeply and reliably from evidence, and reason much more consistently about the implications of our theories of the world. Models represent and help understand effects of theory.  Such models are far more powerful if they are transparent to multiple stakeholders, in which case they can serve as a way to make explicit and share our working hypotheses concerning processes operating within the world.  Simple rules can generate complex emergent behavior and results which logically follow from the theory (as operationalized in the model), but which is not directly evident from that theory.  As such, models can help us think through the implications of an operationalized theory more deeply, reliably and quickly than we could relying on our head alone -- including the consistency of that theory with evidence with regards to factors endogenous to and output by the model.   Critically, such models can also...

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