Recently, I went riding at the margins of the Olympic National Park. The plan was to loop through a couple of scenic forest roads, but eventually, I ran into a closed gate. So, had to turn around and ride back through the same bridge I had crossed earlier. And not just the same bridge; I crossed paths with the same hiker who was now walking back from the other end. Clearly, even he was amused at the coincidence. How often do paths of a motorcyclist and a hiker in the wilderness converge on a bridge — twice!

In a way, coincidences or accidents are just separate causal chains intersecting at some point. For instance, ferry times, riding patterns, and outdated GPS maps are all preceding links in my causal chain. If we go further back, there are other causal sequences that explain why ferry times are the way they are, or how I ended up riding in a specific way. But we can only speculate about the causal events related to the mysterious hiker.

All we can say is – every event has a causal chain with known and unknown preceding events. Even in my case, we can only speculate whether the map was incorrect and led to a private road or if someone just shut that gate on that particular day. Maybe my riding pattern was immaterial. That means if all the other factors remained the same, all types of riders would have faced the mysterious hiker twice! To estimate causal weight, we’d need to replay events and control for variables – practically impossible. That implausibility hints at the layered, multidimensional nature of the system.

 Causal chains operate at multiple levels as well. For instance, the riding pattern could be attributed to upstream causes of motorcycle gear ratios. The engineering of gearboxes has its own causal chains. But a motorcycle spare part is an abstract, reusable node involved in multiple chains – linking several motorcycles, models, and riders. Such a part’s functions are abstracted enough to serve multiple similar purposes. Eventually, the world itself can be interpreted as an interconnected network of abstract sub-systems, each with its own contextual functions. Larger sub-systems are composed of smaller systems and nodes with similar abstract qualities. Imagine building blocks with clear, logical, and physical boundaries, but operating without explicitly articulating their internal structure.

All kinds of systems — biological, chemical, or even social frameworks can be modeled through such a view. In the case of societies, components involved are individuals, families, organizations, and their workflows. For instance, the same individuals when at work serve a different contextual purpose than at home — becoming time-shared abstract nodes within different domain-specific workflows. The structural parallels between larger social institutions and a lonely motorcyclist in the wilderness illustrate the concept of organized complexity. They both function with a level of efficiency that hides the chaos of their own causal chains.

Within any such order — recurring patterns typically indicate underlying drivers. With complex systems, there is a difficulty in isolating them. There are even more challenges with designing optimal solutions for obvious problems. For instance, rising college tuition, health care costs, or government deficit spending are recurring and contentious phenomena. But there are also recurring beneficial patterns – like plummeting smartphones or fast-food prices! Rarely do we see political rallies about unaffordable fries.

The dominant agency causing these patterns can be a specific group of people, natural forces, or an incentive structure. But general discourse is rarely about correcting complex causal factors that led to a contentious pattern. This happens because it’s expensive to dive deep, isolate causality, then arrive at a hypothesis that fits multiple contexts, and then propose and test a generic solution. The general tendency is to introduce new factors into the mix as workarounds — like price/licensing controls, a new tax, maybe a trade war, or even an actual war. There is also a strong political incentive not to do root cause analysis of social problems — often, they ruffle too many feathers. The benefit of a complex interconnected system is overall productivity, and there is even a romantic side to all this.

Republished at ridersmodel.com