Around this same time, a young physicist named Isaac Newton was thinking about how objects move and how light works. While Newton was studying at Trinity College, Cambridge, a plague began to sweep through the country. As a precaution, the university closed. So Newton spent the next couple of years primarily at home, back in Woolsthorpe in the countryside. During this time, he made fundamental advances in calculus, optics, and understanding the motions of the planets. He conducted mathematical work, performed experiments such as poking himself in the eye socket to understand the nature of color, and even apocryphally observed the apple falling from the tree. Like Fairfax, Newton cataloged observations, but he also uncovered a set of universal principles, often mathematically described, that govern our physical world.
In a way, the work of these two men, occurring in the same country during the same time period, embodies two competing approaches to understanding the universe’s complexity.
Newton’s approach seeks to unify all the different things that we see around us, simplifying this variety and diversity through a set of elegant explanations, and often a small set of equations or principles. We see this in Newton’s formulation of the law of gravitation. In a single equation we find insight into falling objects, the ebb and flow of the tides, and the motions of the planets. The hope of such unity—a desire to discover an order underlying every aspect of the universe that we are aware of, and to place each component and particle in its place—is what drives physicists to search for a unified Theory of Everything. It’s revealed as well in the tone of the scientist Thomas Henry Huxley, who felt that the “great tragedy of Science” is “the slaying of a beautiful hypothesis by an ugly fact.” An elegant theory is the goal, and it is a tragedy of the highest level when something is found that contradicts it, or complicates it.
Fairfax’s approach, on the other hand, eschews the pursuit of elegance to embrace diversity and complexity. This tendency accepts a certain messiness to the world, and celebrates learning new details, even if they are hard to immediately fit into a single framework. This approach is easily ascribed to, say, the butterfly collector, who catalogs and describes the many butterflies he discovers. We also find here the modern physician, the intellectual descendant of Fairfax, who marvels at level after level of the workings of the human body, from the complicated steps involved in our blood-clotting process to the intricate nature of enzyme cascades; or the astronomer who immerses herself in the many types and categories of galaxies revealed by powerful space telescopes.
The naturalist recoils at Huxley’s complaint, for there is no such thing as an ugly fact. All facts and bits of knowledge provide new information on the wondrous complexity and diversity of the world around us. Rather than being upset when facts fail to conform to our mental models, we can delight in the unexpected, and find a new way to explain such surprising developments.
The physicist Freeman Dyson has described the Newtonian approach as the science of classical Athens, noting that this mind-set “emphasizes ideas and theories . . . [and] tries to find unifying concepts which tie the universe together.” The diversifying approach, he writes, can be described as that of Industrial Revolution–age Manchester: it “emphasizes facts and things; it tries to explore and extend our knowledge of nature’s diversity.”
Dyson further notes that there is an additional way of identifying these two perspectives, with biology as the domain of the diversifiers and physics as that of the unifiers. I term these two perspectives physics thinking and biological thinking. Within physics there is a distinct trend toward unifying and simplifying the phenomena observed. This is embodied by the work of Einstein or Newton or James Clerk Maxwell, who developed a handful of equations to explain the workings of electricity and magnetism. Simplification, even oversimplification, is revered within the realm of physics.
On the other hand, biologists, as a rule, have a greater comfort with diversity and bundles of facts, even if they are left unexplained by any single sweeping theory. A smaller, more qualified and modest model is just fine. Of course, this is not always true, as Charles Darwin was clearly a unifying force within biology, and many molecular biologists, applied mathematicians who specialize in mathematical biology, and many other types of biologists also tend toward this unifying approach.
In the end, both of these traditions seek the development of theories that are general and predictive. However, the two modes of thinking go about this in different ways, and their differences, driven by the properties and relative complexity of the systems they study, can be examined through their relative comfort with abstraction. For example, the use of mathematics to abstract away details at a grand level is found everywhere in physics, but less often in biology.
This is clear from the following version of an old scientific joke. A dairy farmer, interested in increasing the milk yield of his cattle, brings in two consultants to help him: a biologist and a physicist. The biologist goes off and after a week comes back with a detailed report on what to do for each cow, depending on weather conditions, the size and type of the cow, and so forth. The report is over 300 pages long, but the farmer is assured that following the various procedures will result in an average increase in milk yields of about 3–5 percent. The physicist goes off and comes back three hours later, announcing a general and powerful solution to increase yields by over 50 percent. How so? asks the farmer. “Well,” replies the physicist, “first, you assume a spherical cow. . . .”
Abstraction has its place, but it is not in assuming spherical cows. When details are abstracted away in biology, not only is information lost, but you often end up losing significant portions of what the world contains and fail to explain what’s important, such as the edge cases.
Biological thinking and physics thinking are distinct, and often complementary, approaches to the world, and ones that are appropriate for different kinds of systems.
The Kind of Thinking That Technology Requires
How should we think about complex technologies? Are they biological systems, or physics systems? Which mode of thinking does technology require? It’s time to explore the characteristics of each type of system and compare them to what we know about technology.
First, biological systems are generally more complicated than those in physics. In physics, the components are often identical—think of a system of nothing but gas particles, for example, or a single monolithic material, like a diamond. Beyond that, the types of interactions can often be uniform throughout an entire system, such as satellites orbiting a planet.
Not so with biology. In biology, there are a huge number of types of components, such as the diversity of proteins in a cell or the distinct types of tissues within a single creature; when studying, say, the mating behavior of blue whales, marine biologists may have to consider everything from their DNA to the temperature of the oceans. Not only is each component in a biological system distinctive, but it is also a lot harder to disentangle from the whole. For example, you can look at the nucleus of an amoeba and try to understand it on its own, but you generally need the rest of the organism to have a sense of how the nucleus fits into the operation of the amoeba, how it provides the core genetic information involved in the many functions of the entire cell. As our technologies become more complex and intertwined, it’s clear that they resemble biological systems more than those of physics.
Second, biological systems are distinct from many physical systems in that they have a history. Living things evolve over time. While the objects of physics clearly do not emerge from thin air—astrophysicists even talk about the evolution of stars—biological systems are especially subject to evolutionary pressures; in fact, that is one of their defining features. The complicated structures of biology have the forms they do because of these complex historical paths, ones that have been affected by numerous factors over huge amounts of time. And often, because of the complex forms of living things, where any small change can create unexpected
effects, the changes that have happened over time have been through tinkering: modifying a system in small ways to adapt to a new environment.
For example, many of the most important sequences of DNA in a human cell, such as the ones that power how our genetic code is translated or how we use energy, are the same ones that other, far different organisms—separated from us by eons—also use. This biological legacy code sometimes remains unmodified, but often, through evolutionary time, these systems are also tinkered with and changed.
On the macro scale of an organism, this means that new functions are often layered on top of old ones, which can sometimes lead to problems. For example, we may walk on two feet now, but our skeletal structure initially evolved for more quadrupedal locomotion, that is, on all four legs. Evolution tinkered with the bodies of our ancestors, giving us a “good enough” means of walking with an upright spine. But the solution is far from perfect, and many representatives of our species suffer from back pain.
What is the result of this phenomenal complexity and dependence on the path taken over the course of evolution? When it comes to understanding these systems, biologists cannot live by aesthetics. As the biologist Steven Benner notes, an evolved system works, but it needn’t be beautiful. It can be utilitarian, which we sometimes confuse with beautiful, but it can be far from elegant or simple. Benner even notes that the structure of DNA is not really beautiful, just rendered so by artists through the use of abstraction. Biological systems are generally “hacks” that have evolved to be good enough, rather than pretty, designed systems. They are kluges.
Evolution can even leave us with obsolete legacy code, just as technology does. My backyard in the Midwest is home to a number of honey locust trees, which sport an array of large, dangerous-looking thorns. These thorns seem to have no purpose other than making me worried that I will inadvertently impale myself upon them. So why do they exist? One theory is that they are there to protect the leaves of the tree from being eaten by a mammoth, a giant ground sloth, or another species of North American megafauna. Except, of course, these creatures are long extinct. The information that codes for the honey locust’s seed pods seems also to have evolved under the evolutionary pressure of those same now-extinct megafauna, yet it still exists.
Similarly, while there is still a great deal of debate on this matter, some scientists argue that there is a lot of extra material in genomes that seems to have accumulated and persisted, despite lacking any particular biological function (what is sometimes referred to as “junk DNA”). Just as many complex technological systems, including software, contain features that no one uses and that might even be completely obsolete, many biological systems also have vestigial features whose original functions are no longer relevant.
Of course, the parallels between biology and technology are not perfect. Biology handles legacy code differently: the honey locust might eventually lose its thorns. If this trait is truly useless, then producing thorns is a wasteful expenditure of energy for the honey locust. Over evolutionary time, the thorn trait will be swept away by the success of variants of the tree that lack thorns and are therefore “fitter.” My descendants, freed from the risk of being impaled by this tree, will be thankful. No such parallel exists with most of our technologies: software programs don’t automatically sweep away their own legacy code because it’s outdated and inefficient.
Finally, the similarities between biology and technology can be seen in the concept of highly optimized tolerance, mentioned in the previous chapter. Technologies can appear robust until they are confronted with some minor disturbance, causing a catastrophe. The same thing can happen to living things. For example, humans can adapt incredibly well to a large array of environments, but a tiny change in a person’s genome can cause dwarfism, and two copies of that mutation invariably cause death. We are of a different scale and material from a particle accelerator or a computer network, and yet these systems have profound similarities in their complexity and fragility.
Overall, there is a deep kinship between biology and technology—which means there is something to be learned from how biologists think.
Field Biologists for Technology
As our technological systems become more complicated, we often are left with only the extremes of understanding: either a general notion of how the thing works, even if its innards are at best murky to us, or an examination of its bits and pieces, without an inkling of how it all fits together and how we can expect it to behave. The first is a tendency toward physics thinking, while the latter leans toward biological thinking.
In the face of increasing complexity, some choose to rely on the physics approach, abstracting away details to get a general sense of the system. For example, when looking at a complex social system, such as a company or a city, a physics-style approach for making sense of it might be to graph one of its properties and see if it conforms to a specific mathematical curve. This can yield some insight—or at least a hint of what is going on—but when there are so many different reasons that this system might fit that curve, you can be left with more questions than answers. These systems are often not amenable to immediate and large-scale abstraction; they are too messy and too complicated.
Biological thinking, therefore, with its focus on details and diversity, is a critical perspective for dealing with a messy evolved system that can be completely understood only through a lot of initial prodding and testing. The way biologists, particularly field biologists, study the massively complex diversity of organisms, taking into account their evolutionary trajectories, is therefore particularly appropriate for understanding our technologies. Field biologists often act as naturalists—collecting, recording, and cataloging what they find around them—but even more than that, when confronted with an enormously complex ecosystem, they don’t immediately try to understand it all in its totality. Instead, they recognize that they can study only a tiny part of such a system at a time, even if imperfectly. They’ll look at the interactions of a handful of species, for example, rather than examine the complete web of species within a single region. Field biologists are supremely aware of the assumptions they are making, and know they are looking at only a sliver of the complexity around them at any one moment.
Similarly, when encountering a complicated tangle of a technological system, whether a piece of software, a country’s laws, or the entirety of the Internet, a physics mind-set will take us only so far if we try to impose our sense of elegance or simplicity upon its entirety. If we want to understand our technological systems and predict their behavior, we need to become field biologists for technology.
What does this mean? When we’re dealing with different interacting levels of a system, seemingly minor details can rise to the top and become important to the system as a whole. We need “field biologists” to catalog and study details and portions of our complex systems, including their failures and bugs. This kind of biological thinking not only leads to new insights, but might also be the primary way forward in a world of increasingly interconnected and incomprehensible technologies.
As discussed in the previous chapter, we can learn from bugs in technological systems, just as biologists learn from genetic errors. But biologists do much more than simply learn from these glitches. To better understand how to think like a biologist, we must look at how they conduct their work more generally.
One of the major advances of recent years in genetics is RNA interference, or RNAi. This involves using short pieces of RNA (a “cousin” of DNA that our cells produce and use for myriad purposes) to deactivate the production of proteins. By constructing the right RNA “text,” you can effectively switch off certain genes.
How was this mechanism discovered? One of the initial steps in the discovery involved an attempt by some geneticists at a biotech start-up to make a more purple petunia. They had identified the gene responsible for making the purple in the petunia, and thought that adding another copy of the gene w
ould yield a flower with a richer hue. Instead, when they attempted this, they got a white flower—the opposite of what they’d expected. The inserted genetic information ended up making no purple color instead of making more of it. Rather than sweep the unexpected outcome under the rug as something to be ignored, the researchers noted it for further examination, and it eventually led to the development of RNAi.
This kind of thing happens all the time in science, biology or otherwise. Isaac Asimov is reputed to have noted the following: “The most exciting phrase to hear in science, the one that heralds new discoveries, is not ‘Eureka’ but ‘That’s funny . . .’” Penicillin was discovered when Alexander Fleming saw something odd on a petri dish. The atomic nucleus was discovered when scientists noticed unexpected results in an experiment that involved shooting radioactive particles at a thin piece of gold foil. The scientists could have discarded these results. Instead, they looked more closely—and saw the basis for an entirely new understanding of atomic structure. By cultivating this natural history mind-set of cataloging and collecting the bits and pieces that don’t make sense, we can learn new things about whatever we’re studying.
But simply waiting to observe the unexpected may not be enough. Biologists will often be proactive, and inject the unexpected into a system to see how it reacts. For example, when biologists are trying to grow a specific type of bacteria, such as a variant that might produce a particular chemical, they will resort to a process known as mutagenesis. Mutagenesis is what it sounds like: actively trying to generate mutations, for example by irradiating the organisms or exposing them to toxic chemicals.
While this sounds harsh, it has a purpose. When a system is so complex that it is hard to anticipate how, exactly, it might respond—and what changes in a genome might yield the desired effect—one often needs to use a certain amount of randomness to find out what the system can do. Essentially, these systems are so highly nonlinear and complicated that we must actively use an evolutionary process of tinkering in order to discover how they work.
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