Multidimensionality: Dissolving dichotomies

As much as I want to get through the rest of the basic systems principles, there’s more to the point of multidimensionality than the story explained in the last post. What we did cover was how it can suggest that a system has more states than previously perceived, which can be discovered by understanding the underlying dimensions. Another way to think of it is analyzing the structure behind emergent phenomena. Breaking things down. The complement of that is putting things together. If the last post was a top-down example, what would be the bottom-up example? Dissolving dichotomies.

Mankind likes to think in terms of extreme opposites. It is given to formulating its beliefs in terms of Either-Ors, between which it recognizes no intermediate possibilities. When forced to recognize that the extremes cannot be acted upon, it is still inclined to hold that they are all right in theory but that when it comes to practical matters circumstances compel us to compromise.
–John Dewey

John Dewey is talking about our natural tendency to generalize possibilities into win-lose conflicts of being. That is to say something is either X or Not X, a conflict treated as a dichotomy. He then points out that when we realize dichotomies don’t really represent reality, we move on to treat the conflict as more of a continuum; a non-binary dimension with shades of grey that let us settle somewhere between. Thus, win-lose becomes more along the lines of a 50-50 compromise.

The problem with compromise is that it’s usually a give-and-take struggle. If the forces of each pole are strong it ends up being an extremely unstable mixture. The conflict still exists, you’re just trying to work around it. Ideally you’d remove the conflict all together, creating a win-win situation. This is what Ackoff described as “separately infeasible parts making a feasible whole.”

Say you have two competing tendencies: A vs B. It seems an unstated assumption that you can only have one or the other. The alternative is to struggle with a compromise, or you can look at it differently. Using multidimensionality, we can take the previously understood “or” relationship and suggest further possibilities, including a complementary “and” relationship as illustrated below:

For this to be a useful model, the difference between the low/high regions of a particular dimension is assumed to have a significant impact on the behavior of the system coproduced by that dimension. Just to be clear, since we’ve been throwing it around for a while, a dimension is a quantifiable variable that reflects a facet of a system. And certain quantifiable increases in one dimension can have qualitative effects on the system. Think of it like a tipping point, and the point of distinction between low and high is that tipping point.

Each relationship mode represents completely different behavior whose character can only be understood in its own right. For example, here we have various behaviors of a system based on concern for change and concern for stability.

The high-high relationship is a mature system, searching for stability through change. The high-low relationship is a radical system, interested in change at any price. On the other hand, low-high represents a conservative state, preferring the status quo. But the low-low is anarchy with a low concern for both change and stability, opposed to government in any form. So each different combination of levels for concern (low or high) map to very different emergent behavior.

Now another example of a false dichotomy is the battle of the sexes. Arguing men vs women is a pointless debate because they are both right. However, the public perception doesn’t see it that way. Supposedly, the best solution has been that of compromise. The truth is they are complementary dimensions and a complementary relationship is possible, in fact ideal. But it’s an ideal that can’t be worked towards if it’s not considered a possibility.

Keep in mind multidimensionality is not confined to pairs of variables. We’ve just been using examples with two dimensions to keep things simple, but you can easily have systems with three or even three thousand dimensions. It’s really up to how you want to model it. Unfortunately, as Adam suggested in the comments of the last post, complexity will increase exponentially as you add dimensions or regions in the dimensions.

In the end, it really comes down to looking at things in a way that will afford more choice, using dimensionality as the framework and multidimensionality as the catalyst. How to dissolve conflicts that prevent the system from behaving more ideal is another topic, but with this you can dissolve dichotomies that prevent you from seeing more ideal possibilities.

The bottom line is that complex systems are complex partly because they are multidimensional. For whatever reason, we seem to take for granted the perceived simplicity of things, when more often than not… they’re actually quite multidimensional.