Post #10 in a series seeking to answer "What is Understanding made of?"

“”Don’t let complexity stop you. Be activists. Take on the big inequities. It will be one of the great experiences of your lives.”
- Bill Gates

Every model takes into account a limited number of variables. The younger we are, the fewer dimensions we are asked to manage at both home, school and work. Invariably over time we will be asked to handle more complex problems. That is when we begin to realize we each have unique limitations on how many dimensions (or variables) we can manage. Building models that accommodate the complexity we see in life could help us gain deeper understanding and solve seemingly intractable problems. However, when the number of dimensions we need to handle surpasses our capabilities, complex models may instead make us confused, paralyzed and bitterly angry.

It doesn’t have to be that way. The ability to handle complexity can be taught. Indeed it should be taught. But it is not. Instead when we confront complexity, we either:

• Try to accommodate things we don’t have a firm basis to handle yet, with 3 possible outcomes:
  • we add nuance and depth to our existing understanding
  • we undermine our existing understanding, leaving us vulnerable to inaction, or
  • we overwhelm our capacities and begin to look crazy
• …or fearing complexity, we force fit everything we see into the models that form the basis of our current understanding by:
  • downplaying some input as outliers and irrelevant, or
  • distracting people’s attention from data-that-doesn’t-fit by ridiculing it or demonizing its source.

Both responses to complexity are played out daily in US politics. The modern Democratic Party under Barak Obama tries to take into account the complexity of today’s society when crafting their policies and practices. In the process of challenging conventional (simpler) wisdoms without helping the rest of us come to terms with the complexity they are facing, these Democrats often create confusion and sometimes even look incompetent and crazy. The Republican Party, on the other hand, seems almost maniacal in its attempts to simplify everything to one dimension only, a dimension usually centered around money and the ability to make, take and hoard it. Though these modern Republicans can look decisive (which is very easy when you are one-dimensional), they are ignoring the complexity of issues we are facing, meaning that their solutions will fall short for those whose behavior and needs falls outside of the norms they define.

This doesn’t mean that we all need to use tools designed for the most complex problems. It is okay to live by the KISS principle, i.e. ”Keep it Simple, Stupid.” But the purpose of this post is to ask us to recognize when “simple” is not enough.

A result of our bias towards simplicity is that most of us are good at linear, “cause and effect” thinking… for every effect we observe, we feel we know what is going on if we can attribute it to just one cause. We are so good at this that we will often make up a single cause for the calamities of life (e.g. Pat Robertson blaming the people of Haiti for the earthquake!) because it makes us feel better and validates our simpler approaches. Occasionally some of us are even able to understand and manage two dimensional thinking in which we willingly admit that reality derives from differing combinations of two variables such as Nature AND Nurture. But only in a limited number of circumstances usually associated with our chosen profession, are we able to master models that have 3 or more dimensions or significant causes (e.g. fire will not occur unless there is something in a form that can burn, plus oxygen from a gaseous or chemical source, plus a spark or flame that provides and maintains a minimum temperature… take away any of these, and there can be no fire, leaving it to professional fire fighters to decide which to take away first).

In addition while we may be able to understand and live according to one or two complex models, very few people can excel in three. For example, how many people do you know are simultaneously great at managing work (which requires us to be disciplined and adhere to specific models taught to us in school and by our bosses), great at marriage (which requires us to be fluid and compromising in the models we follow), and great at parenting (which requires us to be disciplined AND to allow our growing children to choose their own models AND still give them love and respect even if we don’t like their choices)? Every woman I have ever met has told me there are probably a few women in this elite cadre, but definitely no men. They would say this typically while unbuttoning my shirt, making me realize how easy it was to get me to stop thinking about anything outside of the one dimension that my mind naturally tends towards!

So when is it necessary to put aside our simpler approaches and begin to deal with complexity? The answer lies in determining how much precision and accuracy we need, and in how much trust we are willing to place in the special few who regularly handle complexity.

Assume that every model we use was created to hit a target from which you get a desired benefit. Thus choosing a model is like choosing a gun. We take our favorite model, aim at that benefit, and shoot. The likelihood of our model delivering the benefit is based on its inherent precision and accuracy. Precision is the ability to always provide the same result independent of the environment that the gun finds itself in. Accuracy is the ability to give results that are somewhere in the realm of what we want even if the results have a wide spread. Looking at it another way, accurate models always hit their target even if they do not touch the center of the bull’s-eye (like many a shotgun); precise models on the other hand always hit a very narrow circle, but there may be times when that circle isn’t even near the target (like many a pistol).

When we need both precision and accuracy, we need models that account for lots of factors, i.e. have many dimensions. For subjects where our individual understanding is one-dimensional, perhaps even superficial (e.g. fighting terrorism) this may require us to rely on experts.

Experts have two responsibilities in this world… to understand the applications, limits and nuances of the models they specialize in, and to communicate the depth of their insights to the rest of us in simple and respectful language, albeit rich in symbols. They are even allowed to rely on statistics, the science of reducing complex data to symbolic quantities that we have been taught to understand, like averages and variances. Finally, it would help if the experts acknowledged to people who don’t trust statistics the limits and impacts of their simplifications.

When experts don’t communicate simply and don’t provide a path for others to explore their deeper understanding, then we have the right to question the need for the complexity they present… is it to illuminate, to confuse or to gain/maintain power? While I believe the burden is on the expert to prove his value, there is also a risk that we will ignore needed complexity at the wrong times because many experts are less competent in communications than we need.

We have often been told that there is a self-serving agenda driving those who tackle complex problems. (“Don’t trust the intellectuals!” has been a rallying cry of conservative forces everywhere, from the Chinese Cultural Revolution through the Reagan-initiated US Conservative Era.) But could it be that the people giving this warning are actually the ones to beware of? Perhaps one way to determine the validity of a position derived from complex methods is to look at the words and reasoning of those who attack it.

• Are the attackers denigrating the messenger? If so, the problem is personal or the attackers are not experts but entertainers; I propose their messages are not ones that the rest of us need be concerned about (think Rush Limbaugh, Bill O’Reilly and almost every political commentator on the airwaves)
• Are the attackers saying that classical viewpoints are better?
  • If they define “better” as more accurate, then we should evaluate if the classical view is indeed more accurate.
  • If “better” really means simpler and “good enough,” then we need to evaluate if “good enough” is acceptable.
  • If “better” is synonymous with “the way things have always been” then we need to ask who benefits most from keeping those views constant.
  • Or are the attackers experts in their own right, challenging only a few elements of the complexity and not the need for complexity itself?
    • Such esoteric debates should occur with limited public (non-expert) input. If a debate is intrinsically technical, we should ask who benefits from making the disagreements public.
    • If the impact of another expert’s challenge is to delay action, we need to ask who benefits from the delay.
    • And if the impact of another expert’s challenge is just to expose that the new model is not accurate or precise enough, then we need to ask if the new accuracy and precision is at least better than what we previously had.

In other words, a challenge to complexity does not necessarily invalidate it for there are times when the challengers themselves are only trying to create confusion from which they themselves will continue to benefit.

Finally, there is a cycle that has been common throughout human history be it in science, art or religion. Our understanding always starts simplistically, at least until we need more precision and accuracy. Then we add complexity… but at some point the added complexity becomes too burdensome for mere mortals. That signals an opportunity for a smart person to begin wrapping elements of the more complex understanding in symbols that simplify the whole equation and become part of the modern culture. In other words, just because we don’t understand a model now does not mean it will always remain strange to us. At some point, we might even consider it simplistic.

Complexity is not a cause of confusion. It is a result of it.”
- Jeff Hawkins (co-founder of Palm Inc.)

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