(This passage follows an exposition of Ayn Rand's theory of concepts and continues building an argument against the doctrine of pure and perfect competition, source of the monopoly power criticisms of advertising.)
Science Is Not (Numerical) Measurement
The development of any science necessarily requires the formation of concepts and, by extension, propositions (which are combinations of concepts), some of which are laws and principles. Consequently, the first implication of Rand’s theory of concepts is that theory formation requires the conceptual process of implicit measurement, or rather, of measurement omission.
Now the use of explicit, numerical measurement was and is an invaluable tool in the development of the physical sciences—numerical measurement does give us more information about the facts of reality than we obtain through qualitative concept formation—but note that even those principles of physics and chemistry that are stated as algebraic equations also omit the measurements of the specific concretes that led to the discovery of the equations. Concepts of measurement, after all, are concepts, and their specific measurements are omitted in the process of forming the concept of measurement, or equation, in question. In a sense, we can say that science is primarily measurement omission; it involves implicit measurement, not explicit, numerical measurement. This point in itself should cast doubt on the assertion that the goal of theoretical research in the so-called social sciences, such as economics and marketing, is the quantification of propositions.
The tendency to equate science with measurement (“it’s not science unless it can be quantified”) is at least as old as Pythagoras. Rand’s theory of concepts cuts this whole approach to science off at its roots, because measurement omission is what gives us the universality of our concepts and, by extension, of our laws and principles. The assertion by the positivists that all laws are probabilistic does not differ from the claim skeptics make that certainty is impossible; both statements are self-contradictions, because the former is a claim to universality and the latter to certainty. The conclusion one must draw from this “self-excepting” fallacy (which is a species of Ayn Rand’s “stolen concept” fallacy) is that both universality and certainty are possible; the challenge is to formulate a theory that correctly corresponds to the facts of reality, not to abandon knowledge or water it down with “law-like generalizations.”
What the explicit, numerical measurements provide us are the differences among individual concretes. Or, to put it in the terminology of the psychological sciences: science is not numerical measurement; rather, numerical measurement is the essence of individual differences. For example, the laws of motion are generalizations that apply to all types of bodies at rest or in motion, past, present, or future. However, the measurements of motion that the planets in our solar system make are unique to our solar system, as are the measurements of motion that certain atoms make in a given molecule unique to that molecule. Specific measurements are a unique point within the range of measurements that constitutes the concept in question.
Thus, the goal of the mathematical economists to make economics “more scientific” by quantifying perfect competition was fundamentally misguided.
No Quantitative Laws in Human Science
Applied to the human sciences—that is, the humanities or so-called social sciences, which include economics and marketing—numerical measurement represents something more specific. It represents a unique, historical point in time that is unrepeatable. It is unrepeatable because the human faculty of volition gives rise to man-made facts that could have been otherwise. The faculty of volition itself—free will—is a metaphysical fact that stands at the very base of all human sciences. . . .
The Role of Statistics in Economics and Marketing
Man-made facts are facts that could have been otherwise; they form the subject matter of the science of human history, a human science, which studies past events as caused by man’s choices and actions. Metaphysical facts are facts that could not have been otherwise; they form the subject matter of the theoretical sciences, both physical and human, and of natural history.
One metaphysical fact of the human sciences is that man possesses free will, but man’s actions, nevertheless, tend to be similar to what they were in the past. “People are consistent” is a principle of psychology. Thus, if we know the choices other men have made in the past, we can make predictions, within a range, about what they will do in the future. Consequently, numerical measurement in the human sciences, as a tool of history, can be helpful in making predictions about the specific actions men will take in the future—but, again, please note, the predictions are not theoretical predictions; they are extrapolations from historical data. Theoretical prediction in the human sciences takes the form of a general, qualitative principle, such as: men will tend to act in the future similarly to the way they did in the past, or: quantity demanded varies inversely with price.
Thus, numerical measurement is used in the human sciences, but only in situations in which we do not or cannot have complete knowledge of the causal factors involved, and the equations derived from the data are historical concretes, not theoretical universals. Consider, for example, the quantification of the law of demand. Historical data can be collected and an equation can be derived from the data, say, Q = 2000 – 2.5P, where “Q” stands for “quantity demanded” and “P” for “price.” Any prediction, however, of the quantity demanded tomorrow based on this formula is inherently approximate, because volition is involved in what constitutes both demand and supply and in the formation of prices; in other words, prices, both relative and absolute, are a function of the choices—the value judgments—of consumers and producers, not of mathematical models. The data of the market, as the Austrian economists have propounded as an integral part of their theory for a hundred years, are constantly changing.
Consider market research, the kind of data-collection activity and measurement making that marketing practitioners perform. Market research data is often quantitative, but it also is historical data. It is numerical measurement of the “state of the market,” the identification of what the competition and customers are doing at one point in time (or several recent points). On the basis of this historical data, but guided by the principles or theory of the human sciences, including psychology, economics, and marketing, marketing managers then extrapolate—that is, forecast—what the future state of the market might be. On the basis of these forecasts, managers make decisions and take actions.
The one branch of mathematics that market researchers find most useful—the one that collects and interprets numerical facts about groups and, on the basis of a sample taken from one segment, measures how accurate our projections are about all members of the group—is statistics.
Statistics is a branch of mathematics and, as such, is a method of measurement. Statistical inference, which is not the same as induction, is used only in contexts in which we do not know—or there do not exist—universal laws that could explain the causal relations of the variables. Thus, the meteorologist makes an expected frequency forecast based on historical data (the data of natural history) because he does not have sufficient information concerning the relevant variables with which to formulate universal laws. Similarly, the medical researcher makes an expected frequency prediction about the survival rate of a particular operation because he also does not have sufficient information concerning the relevant variables with which to formulate universal laws. Both the meteorologist and the medical researcher, however, could, in principle, someday know the universal laws that describe the cause and effect relationships of phenomena within their respective domains.
The human scientist, on the other hand, including the quantitative, perfect competition economist and market researcher, will never discover universal laws that explain deterministically every concrete act of human beings. The market researcher uses historical data, taken from a study of consumer behavior at a given point in time or over a period of time, in order to make expected frequency forecasts about consumer purchase behavior in the relatively near future. This historical data and the accompanying forecasts are extremely helpful to the marketing manager who must make decisions on the basis of what he expects consumers will do in this relatively near future. The historical data has little value to anyone else. Some of it may be interesting from a historical perspective, and therefore it may be appropriate to present the data at an economic or marketing history conference or in an economic or marketing history journal, but most of this historical data does not belong in academic journals, pretending as much of it does today to be theoretical research. This last, it is not.