Complexity and “Truth”

How do we know is something is “true?” We usually assume that observations supporting an explanation verify truth. Often this is basic. Who needs a formal proof of gravity to realize that a dropped brick may hit a toe?

But when cause and effect become murky, proving causation or even establishing a fact is squishier. Have dams built in Turkey dried up wetlands at the mouth of the Euphrates? Does a guy toting an assault rifle intend to shoot up a shopping mall?

For over a century, biological science has formally wrestled the enigmas of cause and effect while biology has steadily became more complex. All of us may learn from their logical progression. It has gone from what is often called “linear” today, to conclusions that must be reached when a system has many variables, ever changing.

Roughly similar to the Deming Circle, Koch’s postulates, introduced in 1884, were for decades a gold standard of proof, assuming that a single microorganism causes a specific disease:

  1. The microorganism is found in all diseased organisms.
  2. It can be isolated and grown in a pure culture.
  3. The newly grown microorganism will infect a healthy organism.
  4. The microorganism isolated from the newly infected organism is identical to those observed in all diseased organisms.

Until the 1940s, Koch’s postulates assuming one-to-one causation backed many biological discoveries. Gradually, complexity began to overwhelm it: viruses, asymptomatic carrier microorganisms, natural antibiotics… Biologists noted, for example that poliovirus infected far more people than it crippled. While Koch’s one-to-one logic led to an effective vaccine, it obviously did not explain the whole story.

To engage in more complex quests, by 1950 Koch’s postulates had to broaden. For example, does cholesterol really cause atherosclerosis or is it merely an ingredient in plaque build-up, while causes lie elsewhere? Proof has to weigh parallel evidence: delay times, strength of correlation, confounding factors, strength of exposure, and possible effects of unknown factors. Delay times alone fuzz causality, as in adverse effects from smoking, breathing asbestos particles, eliminating biodiversity, and so on. In such cases, insisting on a one-to-one linear smoking gun, direct linkage scientific proof, delays recognition of a hazard.

Hill’s Criteria and similar frameworks pose more possibilities and pathways. These are necessary when for example, probing the causes of various forms of cancer. Genetic predisposition, oncoviruses, cellular growth signaling mechanisms, etc., all seem to be factors, but only partial answers. All too often, partial answers only lead to partial countermeasures that address symptoms, not to actions closer to root causes.

Now researchers must to take into account so many system interactions that isolating a few variables for “linear” analysis maybe even mislead. For example, microbiome research reveals that a disease may come from dysfunction in microbial communities within the body. These communities interact, and each has a unique developmental history. Disease is an unwanted system change, but what system and what changed? Detailed causality is a complex tangle nearly impossible to tease out.

However, if we regard the situation as a system out of balance, a countermeasure may be simple – at least in principle. We may grow a healthier microbiome by intervening – by introducing a healthier mix of microbes into an otherwise sickly gut. Our microbial system then starts to rebalance on its own.

Knowing that we are colonized by more than ten times more microbes than we have cells leads to different concepts about health hazards. We need healthier colonies of microbes rather than complete absence of them. A curious finding prompted by such thinking is that male medical staff with facial hair are less likely to spread staph infections (MRSA) than are shaven ones, and those with the closest shaves are the most likely to carry staph colonies. (Do shaving nicks harbor the critters?)

Causality may appear as simple as Koch’s one-to-one assumption, but when one-to-one “works,” it is rarely the whole story. Other than the time it requires, there is no reason to stop learning. One never knows what is being overlooked, or when outcomes predicted by prior knowledge may stop occurring.

A useful explanation of a disease could be as simple as attack by a single pathogen or weakening of a single resister to that pathogen. However, this explanation may fade if both pathogen and resister are evolving. Ratcheting complexity up several notches, suppose a disease arises from the interplay of multiple evolving pathogens and resisters within a large number of colonies, and that these colonies, acting as combined units, also interact with each other. Drill down to a root cause in this mess!

From finance to farming, technology-driven human processes ooze complexity. They remain pretty complex even when we work to keep them simple.

Human organizations keep changing, and they are influenced by many factors from outside the organization. By contrast, human work processes are designed to be isolated from externalities; we want them to be “predictable and controllable.” One-to-one logic may work well improving work processes – if people concentrate on process and not their own conflicting emotions. However, influencing change in a human organizational culture is intervening in a live system we cannot fully understand.

But even closed production processes, carefully under control, utterly depend on our larger environment, both human and natural. Suppose we consider inputs coming in, and outputs going out. Think “circular economy” and everything is much more connected.

Being widely observant and thinking complex systems may not be as mind-bending as it seems. In their localities, keen farmers and foresters have done it for millennia. We can learn to do it better in a wider world, and we should. That’s one reason we keep pressing to develop Vigorous Learning Organizations.

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