Unfortunately, these conclusions are misleading for a couple reasons: First, in both the 2021 Capitol riot and the COVID-19 pandemic, we came uncomfortably close to collapse – if there had been a coup or if we didn't have any way to quickly design, manufacture, and distribute vaccines, we'd be having a very different conversation about robustness. Second, our knowledge is historical, based on past experiences. When we ground our predictions and preparations in outdated estimates, we're setting ourselves up for nasty surprises. Climate change is shifting the ground underneath our feet, with wide-ranging effects, and continuing to plan the future based on old data is dangerous and foolish.
That was the TLDR, but in the rest of this post, I'm going to discuss examples of robustness and then how our changing environment is undermining “robustness” that relies on historical data.
To better understand how we usually design robustness, we'll start by looking at computers. Computers are used for critical systems in healthcare, energy, financial, and military institutions, so by examining how we make computer systems robust, we can learn about how robustness is ensured.
So, how do we ensure robustness in computer systems? Failures in a complex assembly like a computer are the results of random processes: variations in the quality of the components, the external stresses they face, and the interactions with other internal components all contribute. Making a system more robust typically consists of a few approaches: building redundancy for when failures inevitably occur, minimizing the risk of failure, and minimizing the cost of (quickly) repairing the system. Different scenarios present different constraints and different understandings of what is robust: there isn't really any way to fix computers in space, so the computers that NASA sends into space probably aren’t optimized to minimize the cost of repair. However, they do have multiple, redundant central computers and are specially treated to be more radiation resistantWithout protection from the Earth's magnetic field, radiation significantly shortens a computer's lifespan in space.. Digitized financial systems, housed in server farms on Earth, benefit from being fairly easily repairable due to the highly-developedBut not that robust computer industry. Redundancy is built in by running critical processes across distributed networks that can accommodate the failures of individual computers. Furthermore, the computers are kept in isolated and climate-controlled warehouses that insulate them from external disturbances.
Qualitatively, both of NASA's rovers and bank's computer systems are quite robust. But with robustness, context is king. A NASA rover dropped into the ocean would fare poorly. Given that robustness obviously has its limitations, how robust can our creations be?
The robustness of our creations often shows up in their longevity, and there are few that last for many centuries and continue functioning. Cities, and their longevity, are triumphs of robustness. The robustness of cities is due to the great redundancy they are granted by the many social and physical structures that comprise them and their repairabilitySee here for an interesting discussion,Notably, there isn’t really much that cities can do to protect themselves, (i.e. from natural disasters or wars.). Even when cities "die" and shrink their populations by orders of magnitude they very frequently recover. London after the fall of the Roman Empire was almost deserted, and is now a thriving global metropolis. The 1906 San Francisco Earthquake, 1871 Chicago Fire, and the bombings of Hiroshima, Dresden and Warsaw are all examples of colossal catastrophes that each of the cities recovered from. It takes more than a single localized disturbance to end a city. As cited in the Reddit post linked above, "Burn down your cities and leave our farms, and your cities will spring up again as if by magic. But destroy our farms and the grass will grow in the streets of every city in the country."Originally from William J Bryant's 'Cross of Gold' speech The scale of development that humanity has undertaken over its history ensures that when even when most cities are leveled, the surrounding infrastructure enables them to spring back.
Obscured within the previous discussions of cities and computers are assumptions of normality. Building something that is impervious to everything is impossible, because there is always an unknown event that can overwhelm our best efforts to engineer robustness. So in the process of engineering robustness we reason about how probable an event is. For example, NASA's rover probably won't fly off Mars into the ocean on Earth, so while a rover's durability in the ocean represents a real limit on its robustness, we (very reasonably) assume that we don't need to worry about how waterproof it is or how resilient it is to corrosion by salt water. These assumptions are based on physical laws, so unless the laws of gravity change or a meteorite strikes Mars flinging ejecta in the direction of EarthIf either of these things happen, we have much bigger problems than the robustness of a rover, we don’t need to worry about the Mars rover, which is intended to stay on Mars, finding itself in the ocean on Earth.
However, many other less ironclad assumptions of normality are littered throughout the social and physical infrastructure of our societies. The COVID-19 pandemic has violated many of these assumptions. Many relatively harmless examples of these assumptions being violated were experienced in supermarkets. Shortages of toilet paper resulted from violated assumptions about normal variations in the rate of consumption. Toilet paper is typically consumed at a very stable per capita rate, especially when averaged over large populations. However, panic generated by the pandemic prompted extreme stockpiling which, as we all likely experienced, led to widespread shortages. Toilet paper producers simply weren’t prepared for the demand to spike like it did, and as a result, supply chains took some time to recover. Fortunately, toilet paper producers had made a pretty good assumption; the true, underlying rate of toilet paper useMeaning, how often people were actually wiping their butts did not change, people were simply stockpiling, creating an apparent crisis, while there was still in fact enough toilet paper for all.
Unfortunately, the violation of other assumptions led to deaths. Hospitals and medical suppliers can't afford to keep an infinite amount of medical supplies on hand, so they make inventory decisions based on disease statistics, with some allowance for variations. With the onset of a pandemic, their predictions became totally irrelevant and as a result contributed to the death count due to shortages of ventilators and other medical equipment. The eventual pandemic was certainly not a surpriseNote that this article is from 2009., and though robustness is difficult precisely because we can’t foresee exactly what challenges we are yet to face, we can certainly do a much better job preparing a rapid response to emerging infectious diseases, such asBut certainly not limited to! designing more scalable medical supply chains.
At a bigger picture level, the sub-optimal pandemic response was likely facilitated by the fact that human populations have become increasingly interconnected. The emphasis here is on the rate of change; old precedents for the appropriate magnitude and speed of response to pandemics are invalidated as the conditions change.
Nowhere are the changes to our environment more universal than those caused by climate change. Cities, once the pinnacles of robustness in an uncertain world, are threatened in endless ways. Cape Town is one of countless examplesIn case you've been living under a rock, we are facing rising temperatures, rising sea-levels, wildfires, collapsing forest and aquatic ecosystems, among other things. of cities facing existential threats due to climate change. In 2018, Cape Town nearly ran out of water. While climate change continues, we can expect these threats to only increase in severity.
Building robustness into our society means abandoning past notions of what is normal and preparing for the future. But in many regions in the US, we're still investing more resources into developing climate-change threatenedIn some cases, threatened = guaranteed to be underwater in a decade or so. buildingsFor example, California and Florida, instead of future-proofing ourselves, our cities and our supply chains.
The point of discussing robustness in computers and cities was to illustrate that we are really good at engineering robustness when we understand the context we are designing for. The trouble with climate change is that the conditions are changing underneath our feet so it is hard to build an intuition for what is normal without help from experts. We, the scientific community, need to do a better job of communicating the environmental conditions that we find ourselves in. However, the burden doesn't lie solely with scientists. It also lies with insurance companies, (housing) developers, and governmental regulators to more faithfully depict the risks and protect people against them.
We face a challenge to upgrade our decision-making frameworks. We, as humans, have been successful at creating robust infrastructure in mostly static environments. Now we are confronted with a changing environmentThrough our own faults. and must update our practices to account for this paradigm shift. Otherwise, even systems designed to be robust will constantly fail.
]]>Why are such common phenomena described by negation? Can't we come up with better terminology?
I am going to pose several responses to this question. This is an exercise in exploration and isn't in any way meant to be definitive. I invite you to send me an email with your own responses if you have something to add.
Language can change strongly influence how we think and behave. It has been repeatedly observed that differences in how a language divides the color spectrum affects the perception of colors (in Russian, Greek, Turkish, Berinmo, and others). Discussing a topic with vague, unspecific terms will lead us to be less precise in our thinking.
Additionally, identifying phenomena by their lack of a trait impoverishes the discussion by omitting or marginalizing the actual details that distinguish that phenomena.
Historically, all of these non's were introduced because they represented more (mathematically) complex phenomena: Newtonian fluids, Euclidean surfaces, linearly elastic materials, and equilibrium systems were all well-behaved "spherical cows". Due to their (apparently) greater tractability, the "spherical cows" received a lot of scientific attention and as a result, many new interesting phenomena were observed occurring in systems containing strictly "spherical cows". As a result, there is a significant disconnect between the topics that are being (and have been) studied and those that are common in reality.
None of this is to say that studying "spherical cows" first was wrong or bad. In fact, in many cases, it was not possible to seriously attempt studying the more complicated phenomena described by non's until a more powerful framework was built by generalizing the understanding attained by studying simpler systems. The historical division of these topics into tractable and intractable by semantic negation provided a map that described the scientific landscape. By claiming convenience is responsible for the continuing use of non's in our terminology, we claim that we have never escaped this original conceptual map, (perhaps because of social or linguistic inertia).
Though these dichotomies are, in part, historical artifacts, they are not arbitrary: they represent a deliberate decision to separate the simple from the complex. This is a necessary part of science: nature is infinitely complex, so translating from observations to a scientific understanding involves discarding unnecessary details. Another understanding of our question is that it is only by using these negations that we, as scientists, can make our observations scientifically tractable.
Yet, many of these fields have progressed a long way since the dichotomies were first conceived. It would seem that we no longer need these negations to simplify our models since we already know how to model more complex phenomena.
However, in some cases, we keep using the framework provided by these negations to simplify seemingly complex systems, ie if a system is approximately a "spherical cow".
Relying too much on the historical dichotomies can be misleading. It's unlikely that the entirety of interesting variation present in nature is captured by dichotomies based on mathematical ease of use, (though following the math has worked pretty well for us historically, characterizing the scientific landscape by how hard the math was hundreds of years ago takes this observation too far.) And it turns out that the historical dichotomy isn't particularly robust: ie in the case of (non-)Newtonian fluids, a Newtonian fluid could be more similar to one kind of non-Newtonian fluid than that non-Newtonian fluid is to another non-Newtonian fluid. (A shear-thinning fluid at a low shear rate is much more like a Newtonian fluid than it is like a Bingham plastic.) Conceptually lumping physical phenomena that are more different than they are similar simply because they depart from the simplest mathematical formulism will hinder science. So in some cases, the system is better understood by devising a new dichotomy.
At other times, we continue relying on the historical dichotomy because we aren't very good at integrating multiple descriptions of physical phenomena into a single cohesive model. Part of this is due to incentives in academia. But it is also sometimes due to computational barriers or a lacking framework for how to compose different phenomena.
You may have noticed that my examples have all been drawn from physics. Many of my sweeping generalization above really apply best to physics. The semantic patterns discussed have been entirely drawn from physics. Biological terminology is quite different. Perhaps due to the different role of models in biology, the vocabulary is primarily an amalgamation of names. r/K selection theory is the only biological classification I am familiar with that offers a framework for understanding the behaviour of a biological entity. Such a classification represents a success in distilling some general characteristics that are shared across an entire class of entities. In this sense, there are many categorizations in biology, but they tend to be historical and descriptive (ie phylogenetics) rather than based on quantitative properties (ie physiology).
It's also worth noting that the r/K dichotomy is not predicated on negation. However, r/K selection theory is presented as classification that is "fit to data" in some sense as it primarily functions to explain existing observations and was constructed in its entirety from the available data. The dichotomies from physics function primarily as prescriptions for understanding the infinite variation observed in different materials. The physical dichotomies are really frameworks for constructing classifications that have more than two categories; these classifications are progressively built by extending the simplest case provided by a dichotomy. This continual growth from dichotomy (with two categories; the simple and the complex) to a classification (with many categories) also seems to function as a test of the categorizing model’s robustness. If the simple theory can't be extended to explain the more complex phenomena stemming from the same principles, then it is likely missing some essential aspect of the natural phenomena. Physics has been quite successful following this paradigm, perhaps in part because there is a small number of important variables; in biology it is often not entirely clear what variables are important. (Though physicists studying biology are attempting to replicate past successes in physics by finding the analogous biological variables.)
While there are historical reasons for the use of negating dichotomies in describing common phenomena, these dichotomies help construct important conceptual frameworks that help advance science. However, as we learn more, these dichotomies must be amended and adjusted to avoid building conceptual blindspots into our science.
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