The notion of scale can provide a powerful perspective to understanding. However, “scale” also can create greater confusion and deeply entangled, nasty problems.
Developing an understanding of the scale of the Solar System can be eye-opening. But, if not done appropriately can result in even greater confusion. In textbooks, the solar system may show the Sun and planets in scale according to size, but it is physically impossible to show the arrangement and distances to the same scale in a textbook. And, then there is a third dimension that is almost never represented or discussed, and that is time. To do a truly representative scale model of the Solar System, one has to find a do-able scale for size and distance. With my students, I’ve used a 1 to 20 billion scale. However, to do so makes replicating the size difficult, but the distances are pretty reasonable to scale down. We made the Sun and planets out of modeling clay, with the Sun at 6.95 cm and the Earth at about 0.6 mm (which was difficult to do and when done, was easy to lose). With each object constructed, we walked out the distances, which extended over 2.5 American football fields in length. When we included Pluto (it will always be a “planet” to me), Pluto averaged about 290 meters from the Sun, but Pluto’s orbit is elliptical and extends from inside the orbital path of Neptune to about an equal distance beyond the 290 meter scaled distance. To include the time dimension, we would need to scale down the orbital speeds by the same ratio. You can then add scaled down rotational speeds, as well. But, even doing all of this scaling down, we still misrepresent the actual solar system. And, this problem goes back to Korzybsky’s notion that the “map is not the territory.” Our representations, whether in our minds or with objects, can never completely represent the actual “thing” we are trying to represent. But, we can get close, and working towards accurate scale models can help us to refine our cognitive models.
Physical and mechanical systems are easier to scale, but not without issues. Physical and mechanical systems, such as cars, computers, etc., may be very complicated, but they are not complex. Complex systems are living systems. Such systems are unpredictable, self-regulating, and self-maintaining. Mechanical and physical systems are more predictable, but not entirely. Climate and weather systems are more unpredictable than other physical systems, such as planetary motion. And, this unpredictable quality is due to the interdependencies between climate systems and ecological systems. With mechanical systems, we may scale up some transportation system, say from bicycle to motorcycle to car to semi to ship to train to airplane. At each level of scale and change in context of use, the devices become more complicated. At each level, the variables that affect and are affected by the increase in complicated-ness make it more difficult to fully predict. And, then when we add the human component to the system, the complicated mechanical system becomes a merging of complicated and complex systems, which adds even greater uncertainty to the functioning of the complicated—complex transportation system.
Another application of scale that can be interesting, but which can become problematic involves working across levels of scale. Let’s say we identify some pattern in the dynamics of a relationship between two people or between a person and a dog. Maybe this pattern involves a lopsided control issue. One person tries to control the other or, in the case of the person and dog, the person or dog may be the one trying to control the other (I’ve seen both of these patterns of human—dog relationships). Then, say, you see two nations behaving in a similar way, where one nation is trying to control the other. This comparison across levels of scale can be insightful, but not without issues. The specifics of this more general pattern of relationship are not scalable. The danger is that we may get stuck assuming that there are more similarities to the dynamics than there really are. Within the general pattern of lopsidedness control, there are all sorts of other patterns occurring that are specific to the contexts involved. The dog—person contexts are completely different from the person—person and the nation—nation contexts. So, more generalized patterns may be interesting and informative to compare across levels of scale, while the more contextually specific patterns are much more difficult to compare.
Another version of “scalability” that is problematic from the start involves applying some strategy or approach that works well at a small scale and then trying to apply that same approach at a larger scale. The minute we try to “scale up” some approach that in any way involves living or social systems, all sorts of unexpected problems pop up. We may try to scale up the idea of community gardens then lose sight of the contexts that allowed one community garden to be successful. Every community has different characteristics, dynamics, issues, needs, and so forth. And, every community is comprised of distinctively different people. And, communities exist among diverse types of ecosystems, from deserts to rain forests. The “idea” of scaling up some great approach in one context seems wonderful, but that “idea” does not account for the complexity of each individual context or set of contexts, and especially in terms of the exponential increase in complexity encountered when “scaling up.” Even naturally increasing sizes of “things” creates tremendous difficulties. When a democratic form of government was first established in the the United States shortly after getting its independence, the designers of the system were dealing with a population of about 2,000,000 non-slaves and non-indigenous people. And, of people who could vote, that population was about half that size (women could not vote). The contexts that were at play involved a history of colonization, of a dependence on slavery, of women as of lesser status than men, of the natural and physical environments in which people lived, of the technology of the time, and so on. Even from the beginning, the democratic process was bumpy. And, much of this bumpiness arose from the unpredictability of complex social systems. As contexts change, the entire political system can crumble or, at least, face huge challenges in maintaining its stability and functionality. And, as the population increases — a naturally occurring scaling up — the difficulties of maintaining the original system increase exponentially. These “created” complex social systems never seem to address ways of adjusting to major shifts in contexts, major challenges to the viability of the system, and so forth. In the U.S., we seem to be at just this point of near collapse of the original system, where scalability fails.