Thinking doesn't make it so
Nora Ephron skewered power-of-positive-thinking experts and cornucopians in one fell swoop in a recent HuffPost:
So I had lots of fun saying that I was going to do something about the oil crisis and talking about ethanol. Nobody really understands ethanol. Nobody really understands it takes more energy to make ethanol than it actually saves. But who cares? It sounds good and therefore it's good.So what could it hurt to add my own bit of positive thinking to the mix? A commenter at TOD named "memmel" claimed climate change models had approximately the same level of complexity as oil depletion models. No way! I contend that scientifically modelling peak oil actually shows orders of magnitude less complexity than predicting global warming. Consider this self-help motto:
- Oil Depletion: An exercise in extraction of fluids from a container.
- Climate Change: An exercise in non-linear fluid dynamics of N-dimensionality.
I know it has nothing to do with coming up with new forms of renewable energy, but for this small corner of the simulation universe we can hold out hope to make sense out of nonsense and extract signals from the noise. As Robert Rapier indirectly points out, why use empirical formulas while we have a fighting chance to use some real theory?
Thanks monkeygrinder for the pep-talk.
posted by @whut at March 14, 2007
5 Comments:
True, but the climate scientists have reliable scientific data from which to generate their models and draw conclusions. Modellers of oil theory do not have reliable data, or in many cases, any data at all.
As they say, garbage in = garbage out.
The quality of the data is much better for oil depletion. A single value for quantity (of oil) that can be worked in a myriad of ways versus a huge set of variables which control climate.
You may argue that the oil numbers are not as reliable but I will take 20% uncertainty in production estimates any day in comparison to the fraction of a percent variation in temperatures and concomittant tolerances required in climate predictions. The reliability of the data has to be taken with respect to the precision of the outcome needed.
Part of the problem is that people don't understand order-of-magnitude estimates, as the fundies and home schoolers argue in terms of black&white. Check out the HuffPo today:
http://www.huffingtonpost.com/david-roberts/a-long-rant-about-facts-_b_43604.html
There is actually a global extension to the fundamental energy depletion models, which is really a natural continuation to the work on this site (the oil shock model) and that is to model how energy flows around the world.
I looked at approaches for this a few years back and actually looked into how the climate models were designed and boy did my brain bleed.
But it isn't necessary to follow the lead of climate models for this problem.
It is a graph problem, and a pretty straightforward one all things considered. Energy flows from node to node (Directed, edges connecting vertices.
A vertice needs to be typed - an oil well, a tar sand facility, a sugarcane field, a refinery, an oil tanker, etc etc. Vertices could also be weighted be EROEI.
Rules get applied to how energy flows, and assumptions may be tested to generate scenarios.
By simply focusing on modeling how energy moves between countries, the set of nodes could almost be measured on fingers and toes. (Ok that is an exageration but it ain't tricky, like climate modeling. Those poor bastard slice up the world into cubes and try and kill butterflies.)
I suppose one could use "Hubbert Linearization" or my competing, simpler method of applying a 2-D topology "Monkey Protracterization" But really one would need a generic interface and the oil shock model would be at the head of the line.
That is it exactly, the equivalent of a data flow model. Another thing to search on is "compartmental models", as the idea is premised on the flow of material between compartments.
both of them are equally hard to make sense of.
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