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Modern theories of government relish the idea of well-informed leadership. Leaders are expected to make decisions based on correct factual knowledge.  A whole industry of fact-producing institutions has been established to gather and publish societal information: reports, articles and statistics of various kinds. The leader’s task is then to assimilate selected bits of this information and put it to constructive use in the game of politics so that society may be steered in the direction that he or she deems beneficial. Even though the information-processing capacity of one person is very limited and most of the available information may be inaccurate, the ideal remains.

Yet factual and scientific knowledge of society is limited and incomplete, as I strive to show in my other essays. System effects disturb assumptions about simple cause-effect relationships, statistics reflect presuppositions rather than reality and surveys produce inconsistent results with disputable results. In short, there’s a gap between the ideal of informed government and the actual knowledge base which stands at its disposal. Only a part of societal information can be considered “true” in a meaningful sense. This state of affairs will be elaborated in more detail in future essays, but in this one it leads me to this question: assuming for the moment that factual knowledge (i.e. scientific knowledge, objective knowledge) can only play a limited role as a guide for governmental decisions, what other forms of knowledge play a part in political decision-making?

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In his excellent book The Mismeasure of Man Stephen Jay Gould provides a great historical critique of intelligence testing. His focus is not so much on the tests themselves but on the fallacious conclusions which early 20th century scientists all too eagerly drew from them. Again and again he shows that supposedly objective proofs for the inequality of races were based on nothing more than prejudiced interpretations of statistical data. Some scientists resorted to direct falsification, but it is striking how many acted in good faith without any particular political agendas on their minds, yet still ended up concocting results which were clearly influenced by racist prejudice.

The generally accepted preconceptions of the times predisposed people to interpret the data in a certain way. Since most Africans lived in huts and utilized only primitive technology it was clearly unthinkable that they could be endowed with mental capacities comparable to the white man. An immediate consequence was that any given study of intelligence had to be defective if it didn’t show a clear difference between white and black. The properly scientific thing to do was then to revise the testing procedure or the results until the differences – objectively true as they were, by virtue of common sense – could be discerned.

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In my first essay Things that grow I argued that the GDP aggregate is an artificial construct with no clear correspondence to the real world. When GDP is calculated, goods and services sold on a market are aggregated with goods and services produced outside of any markets. This aggregation is supposedly justified by the assumption that their values are commensurable. I attempted to show what a specious assumption that is.

The definition of GDP was my main concern in that essay because it’s pointless to speculate on the value or true meaning of the GDP measure if it isn’t an accurate measure of anything at all. But since GDP is nevertheless often interpreted as a happiness or welfare metric by misinformed economists, it might be worthwhile to take a closer look at that misinterpretation. I still don’t see GDP as a well-defined statistical quantity, but in this essay I want to focus on the usage of statistical aggregates, not on their theoretical justifications. And as I wrote in Things that grow, justifications are often dictated by usage, not the other way around. That’s what makes GDP such an incongruous quantity in the first place.

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