There’s an old saying that if you put one foot in a bucket of ice and the other in a bucket of boiling water, on average you’re comfortable. Sometimes analyzing information in the aggregate obscures rather than enlightens.
A statistician named Francis Anscombe pointed out this same principle in a more visual (though less colorful) manner more than forty years ago:
Matt Warren (@matthewwarren) April 19, 2016
It’s an idea that I’ve been meaning to write about for a while, but was brought back to mind last week while reading an article on the Austrian school of economic theory posted on a site about medical practice and health care in the U.S. (diversity of interests and a very broad reading list is something I find useful, but that’s a topic for another day). The relevant passage:
When Ludwig von Mises began to establish a systematic theory of economics, he insisted on what he called the principle of methodological dualism: the scientific methods of the hard sciences are great to study rocks, stars, atoms, and molecules, but they should not be applied to the study of human beings. In stating this principle, he was voicing opposition to the introduction into economics of concepts such as “market equilibrium,” which were largely inspired by the physical sciences, and were perhaps motivated by a desire on the part of some economists to establish their field as a science on par with physics.
Mises remarked that human beings distinguish themselves from other natural things by making intentional (and usually rational) choices when they act, which is not the case for stones falling to the ground or animals acting on instinct. The sciences of human affairs therefore deserve their own methods and should not be tempted to apply the tools of the physical sciences willy-nilly. In that respect, Mises agreed with Aristotle’s famous dictum that ” It is the mark of an educated man to look for precision in each class of things just so far as the nature of the subject admits.”
I find myself agreeing and disagreeing with this at the same time. Human behavior is far from being as predictable as gravity and I agree with this for exactly the reason I disagree (at least in part) with the second paragraph. It is a mistake to characterize human action as intentional and rational. That’s not to say that all our choices are irrational and reactionary, but that there is a blend. Not only will different people respond in different ways to the same circumstance for different reasons, but the same person may react differently with different motivations on another occasion. Human nature isn’t rigidly deterministic and we consider it so at our peril.
Tom Graves post “Control, complex, chaotic” makes the same observation:
Attempting to ‘control’ complexity just doesn’t work: we need to treat the complex as complex, not as a ‘controllable problem for which we don’t quite know all the rules (but will know them all Real Soon Now, honest…)’.
Yet I’m also noticing another deeper problem: misguided attempts to apply complexity-theory to things that are neither rule-bound control nor pattern-based complexity, but are inherently ‘chaotic’ – a ‘market-of-one‘. Although we can identify definite patterns in health and health-care – that’s the whole basis of epidemiology, for example – neither rules nor statistics can help us deal with the blunt fact the everyone is different. The kind of patterns that we’d use in a complexity-model – probabilities, Bell-curve distributions, outliers, all that kind of thing – can all too easily mask the real underlying fact of uniqueness, from which that supposed ‘pattern’ will actually arise: somewhat like the barely-visible deep-randomness that underlies the visible patterns of Brownian-motion.
Trying to force something into a mold which it doesn’t fit is unlikely to work well.
Abstraction can be useful in understanding the contexts that influence the architecture of the problem. Designing an effective solution, however, will involve not just integrating the concerns of those contexts, but also dealing with any emergent challenges. The variability of human nature (in other words, sometimes the members of those contexts will not all think and act alike) can be one such emergent challenge.
Tom Grave’s again, this time from his “On mass-uniqueness”:
In practice, the scope of every system will comprise a mix of sameness and uniqueness – of predictable and unpredictable, certain and uncertain. If we design only on an assumption of sameness – as IT-systems often are – we set ourselves up for guaranteed failure. The same applies if – as is all too common – we say that our IT-system will handle all of the ‘sameness’ part of the context, and that the ‘not-sameness’ will Somebody Else’s Problem – without giving any means for that supposed ‘somebody else’ to be able to address the rest of the problem, or to link it up with the parts of the context that our system does handle.
The first requirement to make something that works in the real-world is to design for uniqueness, not against it.
In other words, a solution based on a poorly understood problem is unlikely to be a good fit. Abstraction is one tool to understand the problem, but doesn’t provide the whole picture. Shades of gray (black and white) is more likely than black or white.