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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
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'''Summary:''' Our ability to predict is the key to our success. Whether we are predicting the future of the market, future weather, future disease outbreaks, or long-range trends in technology and culture, we stand to gain by improving the accuracy of our forecasts.
'''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.
The inability to predict timing and outcome magnitudes imposes massive costs in terms of cultural and economic life.  
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.
In the United States alone, from 2010-2015, 58 major weather events inflicted an approximate total of 800 billion dollars in damages. Much of this cost derives from severe storms, flooding and droughts. Some fraction of this cost could have been defrayed by effective preparations.  The healthcare system is exposed to similar liabilities. Unanticipated hospital infections account for 10 billion dollars a year in treatment in the USA. War is in another category altogether, and the annual cost to the USA of engaging in counter-terrorism amounts to around 100 billion. The recent 2008 financial crisis has been estimated to have cost around 22 trillion dollars.
Even small improvements in our ability to predict the behavior of complex systems such as the weather, disease, the economy, and cultural shifts, would lead to huge reductions in cost and significant value to our economy and quality of life.
It is our belief that significant increases in predictive power are achievable by combining what are now largely independent methods of prediction. By sharing data, tools and theories across domains that span meteorology, epidemiology, machine learning, economics, and evolution, we stand to generate a significant shift in our ability to accurately predict the future.
Discussing this theme and promoting mechanisms for achieving this goal are the subjects of this topical meeting.

Latest revision as of 22:33, 8 August 2016

ACtioN Navigation

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.

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