verb ( –fies, –fied) [ trans. ] formal
make (something abstract) more concrete or real
Imagine if an alien landed on Earth to study modern society and you were assigned the task of being its local guide. You get to the subject of money and the alien is perplexed. What is money? Is it paper currency? Clearly not, since you can exchange that paper for other forms of currency, such as coins, foreign bank notes, electronic funds, treasury bills, and all sorts of derivatives, assets (both tangible and intangible, liquid and illiquid), services, promises, and so on. After hearing all of the various aspects of money, the alien tells you that money doesn’t really exist.
Money is a fiction, a myth that us humans all believe in. We all act as if money is something real, and this fiction has incredible sway over all aspects of our society, both on the individual scale and in the grand scheme of things. But there’s nothing real about money says the alien. You argue that the very “as if-ness” of money, the fact that we all act as though it were something specific and real, makes it so. Still, you have this nagging suspicion that on some level the alien is right…
One of the epistemological roadblocks of modern science as it (increasingly) studies complex systems is that there is no real place for reification of new concepts that are not already well-understood. You can posit something new as a hypothesis, but then you must reduce that thing to its constituent parts, each of which must be well understood (i.e. the assumptions). But if the magic is in not in the parts but rather in how they fit together, or in a process, then our current scientific method does poorly at explaining the system.
In complex, emergent systems we are tempted as scientists to say what we observe colloquially — the invisible hand of the market, the Darwinian evolution of culture, consciousness itself — is either an artifact (not to be reasoned about) or there exists a heretofore unresolved, better explanation rooted in familiar concepts. But by this logic, we would have to either admit that money doesn’t exist, or that it’s so complicated a thing that in thousands of years of its use, and the many Nobel prizes awarded to those who study it, we just haven’t come up with the right reduction to explain it.
It’s not that reductionism and the current scientific method are wrong, rather they are incomplete. What’s missing is the emergent explanation, the “as if” part of the equation. The mark of a good scientific theory is not how many rigorous equations or categorical reductions it contains. It’s how good the theory is at (a) explaining the observed data and (b) making predictions about what will be observed if we look.
For instance, I would argue that technological change as a process of natural selection is a much better theory than one which tries to deconstruct a piece of technology (say a camera) and explain how it came to be that way or how successive versions might change in the future. But what you won’t get with the evolution of technology theory are specific predictions and particular designs. Rather we must be satisfied with overall trends based on the factors that natural selection works on. Namely, that the users of cameras like to carry cameras with them, so there is selective pressure for cameras to become smaller and lighter, and/or to become integrated into other objects that are being carried already (like phones). While a reductionist might reason about the underlying technology and manufacturing process, an evolutionist might see the camera itself as a somewhat arbitrary manifestation of selective pressures, namely that people want to access and share visual remembrances.
Darwin’s idea* has been hailed by many as the single biggest triumph in scientific thinking in the last 200 years. We sometimes forget that evolution is not a reductionist model but rather an emergent one. It says that given a population of organisms with heritable variation and a mechanism that selects certain organisms for reproduction over others, a picture emerges of how that population changes over time. Viewing an individual organism in isolation at a particular point in time or looking at the group via statistical averaging (i.e. the reductionist method) will never produce this picture. Yet we are so steeped in reductionism that we fail to see that evolution has nothing to to do with organisms per se. It’s the process that results when the preconditions are satisfied. Technological and cultural evolution are just as real as biological. If you believe in the latter as something real, there is no rational argument for disqualifying the former.
We must be willing to reify models that have great explanatory and predictive power, regardless of whether there yet exist good formalisms with which to quantify and calculate. Sometimes (I would say quite often) formalism can be our biggest stumbling block to better understanding and hence better science. Either an existing formalism has been mistaken for the thing itself (and is thus needlessly sacrosanct), or we fear to tread uncharted territories without our trusty mathematical swords.** The first step in conquering the dragons of complexity is to reify. Practically speaking this means to first act as if a phenomenon is real, and then explore whether a world in which such a thing exists is more believable (explanation-wise and prediction-wise) than one in which it does not. If so, there will be time for formalism, and for comparison to (and reconciliation with) reductionist models.
In many systems that are studied scientifically the act of reification is a fait accompli before the science begins. Economics doesn’t bother with the question of whether markets exist, whether they are real entities that can be reasoned about and formalized. Everyone knows that markets are real, heck, many people shop on a daily basis. Does biological evolution exist though, does it deserve to be reified, reasoned about, formalized? What about technological evolution, or cultural?
As we attempt to explain and predict complex systems behavior, we must be willing to reify concepts and explore the consequences thereof. This is especially true in fields where existing models do a poor job. Einstein discovered/invented relativity by imagining something very simple: that the speed of light is constant and that time is therefore relative to an observer. The math and confirming observations followed from this simple exercise in reification. In the end what separated Einstein from his peers was his willingness to challenge an assumption so basic (absolute time) that nobody seriously considered that it might not be true.
Imagine what other deep discoveries could be made by throwing out old assumptions and reifying new ones.
* Ironically, natural selection was an idea that was ripe or “in the air”; had Darwin not been alive someone else (probably Alfred Russel Wallace) would have gotten the credit. This suggests that great ideas are not created by singular geniuses, but rather are themselves emergent phenomena. See the New Yorker article on Intellectual Ventures for more evidence of this.
** If math is the talisman of reductionism, simulation is becoming that of emergent science.