The Data Monoculture 0


For almost a year I have been trying to alert readers to the strong possibility that  organizations charged with estimating future energy usage are consistently underestimating totals. I think the source of their error lies in miscalculating the take-up of energy in the developing world, compounded by their ignoring current latent demand and, perhaps most importantly, their stubborn refusal  to acknowledge that energy demand is not very elastic. It’s the last bill to go unpaid, so to speak.

Because of these errors, I think they have underestimated short term energy demand (through 2030) by a third. And I’ve spent a lot of time here showing why and discussing the possible consequences.

How do organizations like the U.S. Department of Energy’s Energy Information Administration, the International Energy Administration, World Resources Institute, the IMF, the World Bank and companies like Exxon, BP and others who produce energy estimates get tied up in knots about things like this?

I think the answer, implied by the title of this post, is fairly easy to illustrate and explains a lot of problems with forecasting both within and outside the energy sector.

Take the DOE’s EIA. They spent a lot of time and energy developing a model of future growth of energy. I’m sure they were diligent and thought hard about it. (I think the defect is that it’s based on energy supplies, not demand, and that it thinks that people will quit using energy if it gets expensive.)

But the problem with their recent forecasts lies in their model-dependent analysis. After spending all that time and energy building their model, obviously they’re going to use it a lot. Sadly, it seems that they’ve tunneled their vision onto the model to the exclusion of a lot of data in the real world. (This is then exacerbated by the confirmation bias of looking at friendly analysis of the same sources by other organizations and feeling relieved when they arrive at similar results.)

Model dependency crops up in other areas as well. Economics, climate change–both are examples where tunnel vision is hurting analysis. We saw in my recent post that nobody had published a simple mash-up of CO2 emissions and recent temperature trends and my modest posting of the two together got quite a reaction. In finance, many of the great and the good seem determined to ignore Nassim Nicholas Taleb in charting paths to economic recovery, with one side making the (almost forgiveable) error of wanting to adopt one-half of the Keynesian prescription (deficit spending for investment in the face of a liquidity trap) without making a good faith commitment to practicing the other half (creating a budget surplus) when times are good.

The other side (the ECB and U.S. Republicans) are making the far more grievous error of looking at deficit numbers in isolation, thinking that the gross totals and their increase are reasons to abandon social programs and reduce debt at any cost. It makes me wonder if any of them have ever had a mortgage on a home. The cost to borrow money for governments with floating currencies has never been lower and the U.S. and the UK in particular should be spending their way to recovery. It will be tougher for the Eurozone countries, but they need to find a way.

In climate change discussions both activists and skeptics have found a comfort zone of data they are willing to use to advance their arguments. Activists like models, as real world observations are not exact enough to help them make their case. Skeptics like statistical rules and laws, which highlight the deficiencies and call arguments into question. Neither side has spent enough time examining the sources of data used by the other team. Activists still, in the waning days of 2012, show a surprising naivete and ignorance about statistics, while skeptics stubbornly refuse to acknowledge that models can be useful, if users keep their limitations in mind and a copy of The Black Swan on their desks.


The other fields that suffer from the same tunnel vision–healthcare, gun control, genetically modified organisms and their utility/safety or lack thereof–tend to comprise, with those examined above, most of the things we fight about.

I don’t consider that surprising. I don’t consider it unintentional. I do consider it as potentially fatal to the cause of solving any of the real problems that confront modern society.

Original Article on 3000 Quads

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