Reductionism
Two Forms of Reductionism
There are at least two ways of interpreting the term reductionism. One interpretation of reductionism is the “nothing but” mode of explanation. The second is the “divide and study” method of research. These two different notions of reductionism are described below.
1. Philosophical Reductionism: A Belief about the World
The more contentious notion of reductionism may be called the “nothing but” mode of explanation. According to this view, one attempts to explain complex phenomena as merely the interaction of simpler underlying phenomena; explanation proceeds by accounting for complex wholes in terms of simpler components. In this form of reductionism, the researcher aims to make statements of the form “X is nothing but Y.”
Used in this sense, reductionism can be contrasted with what is sometimes called holism. A `holist’ expects to explain phenomena as being greater than the sum of its parts (a process famously dubbed synergism by architect Buckminster Fuller). Frequently, synergism leads to “emergent properties” where complex phenomena cannot be predicted even when a thorough understanding exists of the underlying constituent phenomena. A simple example of an emergent property is the mixing of oxygen and hydrogen to form water: it would be difficult to predict in advance that combining two explosive gases might lead to a non-flammable colorless liquid.
In contrast to holism, the “nothing but” form of reductionism seeks to explain all complex phenomena as convoluted manifestations of a smaller number of fundamental causes or interactions. In its most sweeping formulation: culture is just sociology, sociology is just psychology, psychology is just biology, biology is just chemistry, and chemistry is just physics.
Not everyone is comfortable with this way of interpreting the world. If such a scientific reductive synthesis is true, it might represent one of the pinnacle achievements of human inquiry. If it is false, it might represent one of the preeminent intellectual blunders in human history.
Many humanities scholars of have derided this reductionist project. Much of the objection originates in the unsavory esthetic repercussions of nothing but' reductionism. It is argued that the purpose of explanation should be to enrich a phenomenon, not to trivialize it. The word "explanation" itself is suspect --- deriving from the Latin *ex planum*, meaning to smooth-out or make flat. The aim of scholarship should not be to "flatten" phenomenon. When a scholar claims that *X is nothing but Y*, the world as a rich enchanting place is transformed into a prosaic, colorless, and seemingly senseless enterprise. Among humanities scholars, musicians and musicologists have been among the most vocal critics ofnothing but’ reductionism. Many music scholars explicitly embrace complexity and scorn simplicity. Composer John Cage cautioned strongly against such “logical minimizations.”
2. Methodological Reductionism: A Strategy for Discovery
There is another way of interpreting “reductionism” — namely, as a potentially useful strategy for discovery rather than a belief about how the world is. This alternative interpretation might be described as the “divide and study” method of inquiry — where the researcher examines one factor with the hope that manipulating this single factor will help shed light on a complex phenomena by isolating constituent relationships. For example, a psychologist might use the assumption of a recessive gene as a technique to help analyze a personality trait. The scholar need not believe that the theory is the reality, only that the theory is a useful tool for understanding.
Classically, the principal research tool for “divide and study” reductionism is the concept of “control.” It is commonly thought that control entails holding one or more factors constant while the “independent variable” is manipulated and the “dependent variable” is observed. However, control more commonly entails randomizing the potentially confounding variables. In taking a political poll, for example, pollsters expect that the number of variables influencing people’s opinions is very large. It is hopeless to assume that one can hold constant such a large number of factors. Consequently, researchers seek a random sample with the hope that unknown influences will tend to cancel each other out. The formal statistical argument in support of random sampling is (mathematically) quite compelling, so there is considerable merit to this method of control.
Using such methods of control, it becomes possible for a researcher to investigate the effect of a given factor on some complex phenomenon. By investigating one factor at a time, it is often possible to build a sophisticated model or theory of the phenomenon in question. When the number of factors is more than five or six, the divide and study strategy often becomes intractable due to the explosion of possible interactions between purported factors. Nevertheless, the approach can still help identify important relationships in real-world phenomena.
A Story About Farming
Throughout the nineteenth century, the British government maintained a number experimental farms scattered throughout various parts of the country. The goal was to better understand how to increase crop yields and provide appropriate research-based advice to farmers. Which crops grow better in which regions? Which varieties grow better at different altitudes, with different amounts of sunlight, rainfall, and fertilizers. What combination of soil-type, moisture, etc. are most productive?
Throughout the century, employees kept detailed records for each field they planted. They measured the amount of sunlight, the average temperature, and the amount of rainfall, They recorded when the crop was planted, when they applied fertilizer, when it was harvested, and the total amount harvested. After nearly a century of collecting data, they had leger after leger of information. But they didn’t know how to analyze the data. One year, they would plant variety A of wheat and harvested 2,900 bushels. The following year they planted variety B of wheat and produced a slightly lower harvest. However, the second year received more rainfall, but had less sunlight and a lower average temperature. Was variety A better than variety B? Or would variety A also have done worse if the amount of sunlight was less and the temperature lower?
In order to try to make sense of this data, the British government hired statistician Ronald Fisher in 1919. Fisher took a long look at the records and concluded that no conclusion was possible in many cases. Each year, too many things had varied at the same time so it was impossible to figure out what factor or combination of factors was responsible for higher or lower harvests. Fisher changed the way in which the experimental plots were used. Instead of planting a single crop in each field, the government farmers planted two crops. The first row would be variety A, the second row would be variety B … and so on, alternating across the field. In this way, both variety A and variety B would receive the same rainfall, the same amount of sunlight, the same soil, etc. Fisher understood that if you want to understand the effects of many factors, you need to manipulate only one factor at a time.
Much of the data that had been collected over the course of a century was useless because too many variables changed at the same time. Within a few years after Fisher was hired, the experimental farms produced highly useful data — establishing, for example, that variety A was better than variety B, except in areas that receive high levels of rainfall, etc.
Fisher demonstrated that if you want to understand a complex system, manipulate only one variable at a time and observe what happens. After observing the effect of one variable, move on to another variable (like the amount of fertilizer). As you gain more knowledge, you can increase the number of simultaneously manipulated variables. But if you want to make progress, researchers must start by manipulating just one variable at a time.
What We Learned from Crop Researchers
The naturalist John Muir famously noted “When we try to pick out anything by itself, we find it hitched to everything else in the universe.” The whole world is interconnected. In explaining the world, we must ultimately account for all of the interconnections. However, we can’t start by trying to explain the whole world at once. We simply don’t have enough knowledge.
Instead, we must begin by examining a small part, using a small theory. Theory must begin with unreasonable simplifications. Everything may be interconnected, but it is impossible to begin with a rich theory.
Starting with a simple theory doesn’t mean you should believe the world is simple. In knowledge-related enterprises, the philosopher Alfred North Whitehead offered the following apt advice: “Seek simplicity and distrust it.” As the anthropologist Clifford Geertz wrote: “Scientific advancement commonly consists in a progressive complication of what once seemed a beautifully simple set of notions but now seems an unbearably simplistic one.” (The Interpretation of Cultures, p. 33.)
The heart of methodological reductionism is the “divide and study” approach to problem-solving. Take a problem and divide it into manageable pieces. Simplifying a problem does not mean we believe the problem is simple. In fact, we simplify problems because we believe them to be complex. Simplifying a problem is not a statement about how the world is, it is merely a strategy for discovery. There is no guarantee that a complex whole is merely the sum of its parts — or even that a complex whole is primarily the sum of its parts. But the divide-and-study strategy gives us a place to start.
Methodological reductionism attempts to form good theory by progressively refining simplistic theories. Do not be afraid to start with a theory that is unreasonably simplistic. In fact, we must begin with unreasonably simplistic theories if we have any hope of understanding a phenomenon in its full richness.
Slogan: In research, reductionism is a method, not a belief.
First Things First
When simplifying a problem, there are many possible simplifications. How do you know which simplification to begin with? The experience of researchers provides clear advice on this: start with what you think is the most important factor.
Consider, for example, the phenomenon of musical taste. Why does a person prefer the music he/she likes? We might suppose that age, sex, personality, nationality and socio-economic background all play a role in determining taste. If you think that the most important factor influencing musical taste is teenage peer influence, then begin there.
Similarly, consider the question: In orchestration, why do composers selected the instruments they do? Again, we might identify lots of factors that could influence a composer’s choice of instrumental combinations. These might include the ability of instruments to “blend” with each other; the distinctiveness or novelty of different instrumental colors; the suitability of some instruments to be more noticeable than others; or the availability of especially talented performers on a given instrument. However, if you think that the most important factor influencing a composer’s choice of instruments is simply the loudness of the instrument, then start with loudness.
Before you begin any study, you will know that many factors influence a phenomenon. Do not start by trying to deal with all factors simultaneously. This will lead you to large volumes of uninterpretable results. Instead, start with what you think is the most important factor first. First things first. Then move on to what you think might be the next most important factor.
Avoiding Complex Theories
There is another reason why we should avoid beginning with complex theories. Complex theories are commonly difficult to test. Because of the proliferation of variables in a complex theory, and because the relationships between the variables are usually not fixed, most complex theories can be used to account for any set of observations.
By way of illustration, imagine two nineteenth-century British agronomists talking about crop varieties A and B. The first agronomist advocates a complex theory that variety A is superior to variety B, but only when there is high sunshine, low rainfall, and low soil acidity. So why, asks the second agronomist, did variety B in field #3 in 1893 do better than variety A in field #18 in 1887? Ah, that’s because field #18 has a higher elevation and too much fertilizer was applied. The second agronomist then recalls another case: If variety A is sensitive to too much fertilizer, how do you explain the high yield in field #18 in 1883 when the same amount of fertilizer was applied? Ah, that’s because the crop was planted earlier in the season and the fertilizer was administered twice rather than all-at-once. In short, the first agronomist advocates a theory that variety A is superior to variety B, but only when there is high sunshine, low rainfall, low soil acidity, and moderate fertilizer applied in multiple applications planted early in the season at lower elevations.
As a theory becomes more complex, it allows more “weasel room.” Nearly any set of observations can be seemingly explained using a suitably complex theory. Despite the appearance of usefulness, such theories are actually useless. Only by narrowing the theory, adding restrictive conditions, and limiting interactions, do we allow the world to tell us that we’re wrong.
Occasionally you’ll hear someone praise a theory because it can’t fail: the theory accounts for all possibilities. Remember, it’s not good research if you don’t invite failure. When we have an unfalsifiable theory, we are no longer inviting the world to tell us that our theory is wrong. That is, once we have an unfalsifiable theory, we have stopped doing research.
Simple theories are dangerous because people might erroneously believe them to be true — even though they don’t tell the whole story. Complex theories are dangerous because they are difficult to test, and so it is hard to shake people’s erroneous belief in them when they are wrong. Complex theories are best built through piece-wise additions to a simpler theory. Each addition component to the theory is tested along the way. Later, we will talk about how to formulate complex theories in a way that allows the world to tell us that the theory is wrong.
Our slogan reminds us to avoid undue complexity:
Slogan: Don’t try to explain the whole world at once.