by The limits of formal models The practical limits of formal models become especially apparent when attempting to integrate diverse information sources. Neither statistics nor medical science begins to capture the uncertainty attendant in this process, and in fact both encourage pernicious overconfidence by failing to make adequate allowance for unmodeled uncertainty sources. Instead of emphasizing the uncertainties attending field research, statistics and other quantitative methodologies tend to focus on mathematics and often fall prey to the satisfying – and false – sense of logical certainty that brings to population inferences. Meanwhile, medicine focuses on biochemistry and physiology, and the satisfying – and false – sense of mechanistic certainty about results those bring to individual events. Bad training and traditions coupled with human desires for clear-cut conclusions have led some impressively credentialed (and often otherwise competent) teams into nonsensical but established statistical malpractices. The common practice of inferring no association, or no effect, or no effect modification because P > 0.05 or the confidence interval contains the null shows that even basic statistics are beyond proper understanding and use of many researchers.
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Lars Pålsson Syll considers the following as important: Economics
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The limits of formal models
The practical limits of formal models become especially apparent when attempting to integrate diverse information sources. Neither statistics nor medical science begins to capture the uncertainty attendant in this process, and in fact both encourage pernicious overconfidence by failing to make adequate allowance for unmodeled uncertainty sources. Instead of emphasizing the uncertainties attending field research, statistics and other quantitative methodologies tend to focus on mathematics and often fall prey to the satisfying – and false – sense of logical certainty that brings to population inferences. Meanwhile, medicine focuses on biochemistry and physiology, and the satisfying – and false – sense of mechanistic certainty about results those bring to individual events.
Bad training and traditions coupled with human desires for clear-cut conclusions have led some impressively credentialed (and often otherwise competent) teams into nonsensical but established statistical malpractices. The common practice of inferring no association, or no effect, or no effect modification because P > 0.05 or the confidence interval contains the null shows that even basic statistics are beyond proper understanding and use of many researchers. The full benefits of more refined causal methods will not be realized if their outputs are abused in this way, and there is a sound basis for fears that new methodologies may worsen overconfidence problems thanks to their more sophisticated appearance.
The mathematization of economics since WW II has made mainstream (neoclassical) economists more or less obsessed with formal, deductive-axiomatic models. Confronted with the critique that they do not solve real problems, they often react as Saint-Exupéry’s Great Geographer, who, in response to the questions posed by The Little Prince, says that he is too occupied with his scientific work to be be able to say anything about reality. Confronting economic theory’s lack of relevance and ability to tackle real probems, one retreats into the wonderful world of economic models.
Modern mainstream economics is sure very rigorous — but if it’s rigorously wrong, who cares?
Instead of making formal logical argumentation based on deductive-axiomatic models the message, I think we are better served by economists who more than anything else try to contribute to solving real problems. And then the motto of John Maynard Keynes is more valid than ever:
It is better to be vaguely right than precisely wrong