I said earlier that I had a few new thoughts about chaos theory, and so we delve back into this mess, but at least I’m giving you fair warning.

We refer back to The Forces of Chance article by Brian Klaas, and he uses the example of how the Secretary of State during Word War II had vacationed in Kyoto, Japan many years previously, and his fond memories of that visit prompted him to campaign against it as a target for the atomic bombs, which was ultimately successful. But during the second actual bombing run, the aircrew was thwarted by obscuring clouds over the primary target of Kokuro, forcing them onto their secondary target of Nagasaki; they nearly abandoned that target due to the same circumstances, but a break in the clouds allowed them to sight accurately and release destruction onto that city.

This illustrates two key points where we could say chaos theory reigned. First, the human element of emotional attachment to Kyoto, which kept it from becoming one of the targets despite its strategic value. And second, the vagaries of weather that blotted out sight of Kokuro but also allowed Nagasaki to be targeted. Between them, they dictated the events that led to Japan’s surrender.

Kind of. For instance, targeting Kyoto instead might have made the Japanese surrender before a second bomb was dropped – or it might have tightened their resolve. Playing, “What if?” with events during WWII is a common, but ultimately meaningless, pastime among professional and amateur historians. There are alternatives to anything everywhere that might lead to ‘alternate histories,’ and people are fond of imagining how things might have turned out. In this way, we imagine that events are wildly variable and so deterministic physics is all wet, but it’s not true in any sense that we can actually prove.

To go back to the Secretary of State, it seems likely (in fact, highly probable given all that we know of physics) that his experiences during his trip were what created the emotional resolve to save Kyoto, and this could have happened no other way – at least, not without other input that would have swayed his emotions in a different direction; had he been mugged while there, it could easily have been different. So the past history of one individual can have a stunning effect – but this is true of any circumstances beholden to human vagaries; the attitudes that we all hold are shaped by our past experiences. It is also entirely possible that anyone who knew the Secretary of State fairly well would have predicted themselves what his reaction might be – we do this all the time with people we’re close to. So is this chaos, or simply complication, too many factors to collate and calculate? Is that all that chaos theory highlights?

Not to keep flogging the same points, but we’ll turn back to the weather example again. The problem with the weather is that it takes very little input, very little energy, to affect what direction it might take. And we know this, because all meteorologists provide their predictions with a margin of error; this is not calculating chaos theory into the mix, but the enormities of the data needed to become more accurate, to reduce this margin of error. We often know when the conditions exist for a wildfire, but not where lightning will strike or what idiot will start an improper campfire in the middle of those conditions. The resulting wildfire then introduces more energy into the atmosphere that will behave exactly as physics dictates it would, even when we cannot possibly calculate how much energy is entering and exactly where. Yet when we think about how much this is happening all the time, it becomes rather astounding that the weather reports are anywhere near as accurate as they are. We might expect or hope that they were more accurate, thinking science is failing us if they’re not, but the handle we have on it is actually pretty impressive – and the averaging out of small effects works fairly well. Even when some storm or system starts down a ‘chaotic’ path, the moment we recognize it we’re already refining our predictions.

So there are two main bodies of thoughts that occurred to me. The first is that, is it possible to identify the circumstances that may go ‘non-linear’ (or against expectations)? Are there key areas where a random variable is more likely to push things farther afield than expected? Initially, I was thinking that the lower the energy needed to effect change, the greater the chance of change occurring – not especially deep I know, but it’s more along the lines of, if the ground temperature rises two degrees above expectations or predictions, is this more likely to create rising air that develops into a storm, or a front that will deflect the storm? In other words, are there key areas that we should be watching more closely? Though I can’t say that this isn’t already being done, or at least attempted. And this isn’t just with the weather but say with economics; what stocks are likely to trigger reactions from traders with small variations? For sociology, what observable factors from a culture tend to give indications of impending changes?

Klaas provided the example of the Arab Spring, begun when one merchant reached his breaking point with oppressive government and immolated himself, touching off a backlash of reaction, frustration, and revolution. Though quite frankly, anyone familiar with the cultures involved likely knew they were a powder keg. And so we reach thought number two, which is, how often human emotions are tied into specific examples of chaos theory? In this case, not so much the revolutionaries themselves, but more the government attitudes that, if nothing has happened so far, people are okay with it, and nothing will continue to happen. This type of thinking is enormous within humans, and responsible for countless issues that arise. It’s harder and more expensive to plan for contingencies, so we’ll ignore the possibilities and hope that it works out – and every moment that it does is reinforcement for this behavior. When the breaking point is reached, it’s not that this wasn’t predicted, it’s that such predictions were ignored or misunderstood.

This has happened countless times in this country with hurricanes. There’s a range of predictions, often depicted as a cone representing the possible paths of the hurricane – less defined the further away in the future it is. Most of the time, the path falls within the cone, though less often in the center of the prediction; there is also a range of predictions of wind strength as well, with largely the same results. Yet countless people assume that the ‘worst case’ scenario isn’t going to come to pass, or they’re close enough to the edge of the cone that they won’t feel the effects too strongly, or even that it’s too much of a hassle to vacate the area – in essence, they’re betting against the predictions. And for every hurricane, we hear about the people who lost their bet, many of whom complain that they “didn’t expect this.” While we may feel inclined to blame this on imprecise predictions and the failure of physical science to pin things down precisely, pointing to it as a manifestation of chaos theory, it’s more a manifestation of relying on ‘averages’ and past experience, especially those who heard of the ‘worst case’ scenarios that didn’t come to pass and consider this a failed prediction, attempting to produce their own pattern of expectations.

So once again, the human element is a major factor – and many of those in authority recognize this and become even more adamant that evacuations are mandatory. They know that, “Better safe than sorry,” will too often take a backseat to, “That was a waste of time.”

This is not to say that every place where we might point to a manifestation of chaos theory is due to human foibles and behavior, but we should be paying attention to those areas where it does, and realize that this is more a sociological problem than a mathematical one. And again, even if we have utter faith in the value and strength of chaos theory, it doesn’t permit us to improve things – not until it can predict results more accurately than the meteorologists (or economists, or sociologists, et cetera.)

So where does this leave us? Before this article, I had attempted to understand chaos theory with James Gleick’s book, and found it not just uninformative, but confusing and willingly misrepresenting science and determinism by a wide margin. Klaas’ article was significantly better at presenting the theory as instances where we cannot predict results, but this hardly moves us forward in any way, does it? And in fact, I think it may actually do a disservice in giving a specific and official-sounding name to circumstances where, let’s face it, the primary reason that we cannot predict results is that the data are too voluminous. How, exactly, is that a ‘theory?’ Does it predict where and when the ‘law of averages’ will fail to average out? Does it allow us to know how to control this, or anything, really? To all appearances, when we have any circumstances where we might say, “Crap, that didn’t go as expected,” mathematicians may nod knowingly at each other and say, “Chaos theory.” But what’s the value in that, especially when they cannot let us know ahead of time?

Sorry, I’m just going to stick to, “Too much data needed for accuracy,” and not bother with other names for it.