Mistakenly Projecting Blame for Human Confusion and Associated Difficulties onto the Outside Environment

When it is really just a simple matter of ‘garbage in, garbage out’

Following the reflexive human habit of blaming some ‘other’ for human difficulties, Kahneman (2011) himself uses the term, “low-validity environments”, to project internal human confusion and problem-solving incompetence away from the ‘guilty’ onto the blameless natural environment. Low-validity environments, according to Kahneman, are those that pose “…a significant degree of uncertainty and unpredictability” for those facing them (ibid., p. 223).  More simply and directly put (and dropping the useless human habit of reflexive projection), a low-validity environment is just a confusing one.  

Kahneman (ibid., pp. 224-225) states that there are two reasons that [confused] people facing novel problems find it difficult or impossible to make good problem-solving decisions and predictions about them:

1.  They strive to be clever and ‘think outside the box’, and therefore vainly construct and consider huge, overly complex and/or irrelevant models of the problematic system and situation; and,

2.  They are wildly and reliably inconsistent in forming summary assessments (e.g., making measurements) of information emanating from complex problematic natural systems. Unfortunately, unreliable and inconsistent basic observations and measurements of the various possible controlling factors surrounding a problem situation cannot be utilized as valid predictors of anything.

Again putting it much more simply and directly, both of these Kahneman explanations for human confusion boil down to the single “garbage in, garbage out” (GIGO) principle.  Note here that it’s not just bad observational data alone that can prevent the formation of good decisions and solutions.  A too-erroneous model -- or even a whole, venerable, still fairly reliable scientific paradigm -- used to process information can produce complete garbage from absolutely perfect data.

With regard to an example of Kahneman’s first case, consider the modern controversy surrounding humanity’s effects on the earth’s climate. Rather than first consider and come to plain grips with the simplest, most predictable and most critical aspect of the natural system concerned – that is, the second-order, extremely negative effects on the whole human population of abruptly and strongly reducing the human use of fossil fuels – most of those worrying over the potential climate problem entirely skip this most critical and least confusing aspect of the entire tableau, and instead just blithely proceed to backasswardly and mightily strive to justify such a precipitate, definitely harmful action on the weak basis of climate models strongly attenuated by irreducible uncertainty. Scientific arm-waving can not only be economically and culturally costly, but it can also kill.

To give an example of Kahneman’s second case above, consider, for example, the multitudinous measurement errors caused by foreshortened normally FDA-mandated vaccine testing periods, limited vaccine test subject sample population sizes and test subject variability, and misclassified COVID-19 deaths and infections. All of these measurement errors are now recognized to underlie the somewhat mistaken 2019-2022 centralized government and industrial management policies used during the COVID19 pandemic.

History shows that human decision-making and problem-solving can be a wonderful thing or a very dangerous thing.  Lately, the latter seems to be the predominant case.  Much, much more care is evidently needed.

So, carefully! Carefully with the plates!