Author
Farmer, C
Journal title
Proceedings. Mathematical, physical, and engineering sciences
DOI
10.1098/rspa.2017.0115
Issue
2200
Volume
473
Last updated
2024-03-27T20:20:01.39+00:00
Page
20170115-20170115
Abstract
A mathematical model can be analysed to construct policies for action that are close to optimal for the model. If the model is accurate, such policies will be close to optimal when implemented in the real world. In this paper, the different aspects of an ideal workflow are reviewed: modelling, forecasting, evaluating forecasts, data assimilation and constructing control policies for decision-making. The example of the oil industry is used to motivate the discussion, and other examples, such as weather forecasting and precision agriculture, are used to argue that the same mathematical ideas apply in different contexts. Particular emphasis is placed on (i) uncertainty quantification in forecasting and (ii) how decisions are optimized and made robust to uncertainty in models and judgements. This necessitates full use of the relevant data and by balancing costs and benefits into the long term may suggest policies quite different from those relevant to the short term.
Symplectic ID
694029
Favourite
On
Publication type
Journal Article
Publication date
26 Apr 2017
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