In a comment last week concerning a U.S. government controversy, someone said that the opposing parties were being forced by national security constraints “to fight over shadows on the wall.”
It sounded like a reference to Plato’s “Allegory of the Cave” and it seemed to me a compliment to the writer’s audience that she felt free to use it without explanation. I found myself thinking, “This is something that ought to be used more often,” and please don’t be offended if I do explain it now; if you already know what I’m talking about, you can skip the next two paragraphs and go straight to where I will try to add value.
In Plato’s scenario, we’re asked to imagine people imprisoned from birth in a cave, shackled so that they can’t see what’s behind them and can’t even see each other. Behind them, though, a fire continuously burns so that its light illuminates the wall that is the prisoners’ entire field of view; a transverse walkway, between the prisoners and the fire, allows the passage of objects that cast shadows on the wall. Those moving shadows, and the faceless voices of other prisoners, are all the reality that they know: they develop elaborate theories to explain and predict what they’ll see.
Plato imagines one of the prisoners being freed, and being forcibly taken out into the world to see the reality that the shadows only poorly represent; he suggests that the newly aware prisoner, upon being returned, would be unable to convey any of his new understanding to those who had remained behind. They might even believe he had been rendered insane by his journey to wherever he had been, and violently resist any attempt to subject them to the same (as they imagine it) trauma.
Where does one begin, to make the connections between this 2,400-year-old thought experiment and the things that we are doing to ourselves today?
For one thing, it’s been observed by Very Smart People like Robert Reich that we live today in “a global labor market, in which the better-educated people who can solve and identify and manipulate symbols—and solve and identify problems—are doing better and better.”
Jobs that involve anything in the physical world are being displaced by robots (and, to some extent, their controlling algorithms): the people who used to have those jobs, Reich says, “are finding themselves in the local service economy…and these jobs do not pay much.” (He made those observations twelve years ago, when these views were not yet what everyone else was saying. He got there pretty early.)
Being a shadow-watcher, in short, may pay better than being an actual mover of the actual physical objects that cast the shadows, and that may seem a little strange—but even earlier, Reich’s 1991 book The Work of Nations offered a description of the new kinds of real value that arise from symbolic strengths. Symbol manipulations, he pointed out, “reveal how to more efficiently deploy resources or shift financial assets, or otherwise save time and energy.”
This is exactly what we’re seeing today in the use of “big data” feeds from devices, events, and processes. We want to keep both hands on the wheel, not merely let the data drive us in easy downhill directions, but there’s certainly much real value to be found in data that we previously could not affordably use.
Even so, Reich is at least as much a skeptic as a cheerleader. He notes the unstable, positive-feedback behavior in the markets that symbolic gatekeepers both create and serve: the greater the supply of their services, the greater becomes the demand. He mentions the folk tale of “the starving solitary lawyer in a small Kentucky town, whose strategy of attracting another lawyer to town eventually brings them both vast riches.” More seriously, he accurately observes that “Financial ploys become more complex; the computers and software in trading rooms, more powerful and expensive. Clients feel compelled to spend ever more in order to gain a bit of ground, or at least avoid a costly defeat.”
Precisely this behavior was noted in the late 1980s, when expert systems were introduced into the business of foreign-exchange currency trading: the earliest adopters of advanced trading aids enjoyed a momentary advantage, but talent is mobile. Developers were readily wooed with attractive offers to pollinate other flowers – and since foreign-exchange trading is absolutely and unavoidably a zero-sum game, the eventual outcome was that traders were doing about as well as they did before, but were spending huge sums on mutually offsetting technology to do it more quickly. We can only hope that this will be the exception, not the rule, when new technology is adopted broadly beyond its novelty stage.
There is, however, another caution that should be present in all discussions of symbol-driven decisions. As excellently observed here on diginomica by Denis Pombriant, the symbol manipulations at the heart of the “Moneyball” story are perhaps dangerously seductive if attempted in less well-behaved and less crisply defined worlds. “Moneyball” Denis writes, “depends, as all analysis does, on good, clean, and valid data and for that reason should only be applied in its pure form outside of baseball with care.” Real-world data is rarely as clean as that which flows from currency exchanges or baseball diamonds.
The paradox, I suggest, is that we often call what we’re attempting a “digital transformation” when we should, perhaps, be calling it "digital approximation". Of course, we can argue that data will be more real and accurate when it comes from direct measurement from a sensor, rather than relying on a human observer. On the other hand, many (most?) data presentations intentionally strip away some pieces of reality, whether we call it “noise reduction” or merely make a necessary decision to use only some finite number of bits to represent a value.
We will tend to find symbols where they are most abundant, with highest apparent quality of representation, at lowest cost. Only with determined effort, and a willingness to tolerate occasional and varied pain, will we see beyond the shadows.