Limits to Prediction: Difference between revisions
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{{Limits_to_Prediction}} | {{Limits_to_Prediction}} | ||
Co-hosted by Towers Watson | Co-hosted by Willis Towers Watson | ||
Held at 71 High Holborn, London, WC1V 6TP, United Kingdom | Held at 71 High Holborn, London, WC1V 6TP, United Kingdom | ||
Friday, September 9, 2016 | |||
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'''Summary:''' | '''Summary:''' 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 will report out on an SFI scientific workshop, which convened researchers who work on the mathematical, algorithmic, and practical aspects of prediction. This practitioner-oriented meeting will include members of the original scientific workshop, as well as relevant social scientists and decision-makers from ACtioN member organizations. | |||
Latest revision as of 22:33, 8 August 2016
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Co-hosted by Willis Towers Watson
Held at 71 High Holborn, London, WC1V 6TP, United Kingdom
Friday, September 9, 2016
Summary: 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 will report out on an SFI scientific workshop, which convened researchers who work on the mathematical, algorithmic, and practical aspects of prediction. This practitioner-oriented meeting will include members of the original scientific workshop, as well as relevant social scientists and decision-makers from ACtioN member organizations.