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Laundering Scientific Fallacies

From climate change to economic chaos, experts can be relied on to get things wrong. Does spending too much time in universities make people stupid or does the computer science used to model reality for fail to distinguish between reality and wishful thinking? This analysis blows apart the business of laundering reality to mask the hoplessness of scientific predictions.

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Laundering Scientific Fallacies

by Xavier Connolly

20 June 2012

Since The Daily Stirrer replaced Little Nicky Machiavelli in 2009 we have criticized the practice of scientific researchers to use mathematical modelling and evidence obtained from computer models as a substitute for empirical evidence to extend our knowledge of the climate, oceans, the cosmos, earthquates and incongruously the global and national economies. Scientists increasingly present shaky assumptions, guesstimates and simple fabrications of certain constants and natural variables and feed them into computer as data from which the software will produce projections with triple-decimal precision. We, the taxpayers whose hard - earned cash must pay for this bullshit production process are then expected to treat the output with the reverence usually reserved for stuff written on tablets of syone. Needless to say, the reality is the conclusions of these experiments mathch the quality of the input information. GIGO as the old computer programmers mantra goes, Garbage In, Garbage Out.

Finding the right words to illustrate the dodginess of this sort of data laundering is never easy, absolutes are simply inadequate. One is reminded of Physicist Wolfgang Pauli's comment on string theory, "So worthless it is not even wrong." An excellent example comes from the field of climate science. This is an excerpt from a recent study overseen by courtesy of Roger Pielke, Sr. which attempted to discover if a high climate sensitivity to CO2 can be reconciled with the lack of ocean warming over the last 10 years.

“Observations of the sea water temperature show that the upper ocean has not warmed since 2003. This is remarkable as it is expected the ocean would store that the lion’s share of the extra heat retained by the Earth due to the increased concentrations of greenhouse gases. The observation that the upper 700 meter of the world ocean have not warmed for the last eight years gives rise to two fundamental questions:

1.What is the probability that the upper ocean does not warm for eight years as greenhouse gas concentrations continue to rise?

2.As the heat has not been not stored in the upper ocean over the last eight years, where did it go instead?

These question cannot be answered using observations alone, as the available time series are too short and the data not accurate enough. We therefore used climate model output generated in the ESSENCE project, a collaboration of KNMI and Utrecht University that generated 17 simulations of the climate with the ECHAM5/MPI-OM model to sample the natural variability of the climate system. When compared to the available observations, the model describes the ocean temperature rise and variability well.”

Pielke goes on to deconstruct the study, but just compare the two bolded statements. First, that there is not sufficiently extensive and accurate observational data to test a hypothesis. BUT, then we will create a model, and this model is validated against this same observational data. Then the model is used to draw all kinds of conclusions about the problem being studied.

In this case, the study seems to validate the model by saying it correctly simulates past upper ocean heat content numbers (you remember, the ones that are too few and too inaccurate to validate a hypothesis). But the point of the paper seems to be to understand if what might be excess heat (if we believe the high sensitivity number for CO2) is going into the deep ocean or back into space. It is possible to come up with a number of combinations of assumptions to match the historic ocean heat content numbers. The point is finding the right one, and to do that requires validation against observations for deep ocean heat and radiation to space. Therefore no modelling exercise using this method can be regarded as reliable.

This is as perfect an example of they way scientists launder pure speculation and pretend it is proven fact as any you are likely to see. When there is not sufficient data to draw conclusions about how a system operates, then how can there be enough data to validate a computer model which, in code, just embodies a series of hypotheses about how a system operates?

A software model is no different than a theory written in computer language. If somebody has a hypothesis that football results of six leading teams in the UK Premier League (or substitute any sport and teams you like) are driving stock market prices, then to somebody like me (a computer professional for almost 30 years) devising a program that predicts stock market prices falling when two of these teams have a run of poor results or rising when they hit their best form does nothing to increase my accuracy of this my stock market predictions though it may be get enough media attention to persuade people to invest in my system. The model is merely a software implementation of a weak and fallacious theory.

This is not just a problem in natural sciences. Governments, banking corporations and international agencies base their projections on computer models. This is how they miscalculated so comprehensively the effects of the stimulus following the 2008 crash.

They had a hypothesis that government deficit spending would increase total economic activity. After they spent the money, how did they claim success? Did they measure changes to economic activity through observational data? No, they had a model that was programmed with the hypothesis that government spending increased job creation. When the program was run it produced a number that inducated,(get ready to step back in amazement here), the stimulus created millions of jobs (despite falling employment). And the press dutifully reported as fact.

The mistake built into this particular model was that it used a flawed measure to calculate economic activity. Gross Domestic Product is not a measure of economic activity but of "churn", the movement of money in the economy. Therefore these statistical thinkers assumed that if the government raised the equivalent of 5% of GDP by selling bonds and pumps that money into the economy, the economy has grown by five per cent. To compound that fallacy they then assume that if the churn of money has increased by five per cent new jobs must have been created. This is not even logical.

Brtitain's Labour government in the 1970s tried the same trick, to produce an illuson of economic good times by means of tax and spend. Having raised taxes to obscene levels they not only emparked on a programme of public works and vanity projects but also increased entitlements so that many families who would otherwise have been impoverished by tax increases were rescued by the government handouts to which they were entitled. But taking money with one hand and giving it back with the other is not the same as economic growth.

To assume it is only results in Garbage In Garbage Out.

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