Co-hosted by Willis Towers Watson
In what situations are there absolute limits to prediction, and how are those limits determined? There is a relatively short list of high-level reasons that a system can be hard to predict: (1) the dynamics may be in some sense inherently unpredictable (e.g. chaotic), (2) the financial or energetic cost of measuring the system to sufficient accuracy may be prohibitive, (3) the computational problem of prediction may be difficult (e.g. NP-complete, or worse, uncomputable), even when provided with unlimited data, and/or (4) the state space itself may be unknown, as is the case in systems that adapt, evolve, or innovate.
This Topical / Business "Vitamin B" meeting reported out on a SFI scientific workshop, which convened researchers who work on the mathematical, algorithmic, and practical aspects of prediction. This practitioner-oriented meeting included members of the original scientific workshop, as well as relevant social scientists and decision-makers from ACtioN member organizations.
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