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Old 05-31-2013, 04:34 AM
Elroch Elroch is offline
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Join Date: Mar 2013
Posts: 143
Default Re: Data snooping and science

This issue of the relationship between machine learning and the scientific method is a fascinating one. To me, machine learning abstracts the scientific method and can be considered to include its purest (and perhaps only epistemiologically valid) form. We forget how good we are naturally at the job of building extremely high level models of the world, which often allows scientists (and all of us) to shortcut a mechanical implementation of the learning cycle, but it is implicit in all scientific knowledge (and a lot of other conscious and subconscious understanding of the world).

I believe the point in the lecture is this: there is no uncertainty about the laws of physics. Climate change is a direct consequence of physics, without any additional assumptions. The only difficulties in applying the laws of physics to global warming predictions are:
(1) the computations are hard, and can only be approximated. This is the reason the most powerful computers ever built are used to do them, and why the differences between predictions and the error bars on them have declined over time.
(2) the past data is not as detailed as we would wish (and, to a lesser extent, not quite as precise as we would wish)
(3) there is uncertainty about some of the other data for the future (such as the minor effect of small fluctuations in the solar constant and other anthropogenic effects, such as SO_2 emissions).

I think it is the complete certainty about the physics that allows the strong statement made by Dr. Muller, where I presume he was referring to the effect of variation in CO_2 emissions on temperatures, a one parameter relationship. This effect can be isolated even if one only has an approximation to the boundary conditions and the fluctuations in the solar constant. You can simply do different runs, with everything being the same except for different CO_2 emission profiles over time. As Michael points out, there is acknowledged uncertainty in the extrapolation of the one parameter relationship, because of the details of positive feedback mechanisms (eg methane emissions from melting permafrost, lowered albedo of polar regions) and negative feedback mechanisms (eg from increased cloud cover).

Here is an early example of checking a prediction against what happened after it was made, safe from the slightest chance of snooping:
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