If readers
find certain areas in this chapter difficult to comprehend, don’t be upset. The
methodology of science involves logic and the theory of knowledge in
philosophy. These are close to the most profound knowledge in mankind’s
cultural history. Though I had studied under an expert, my knowledge in this
area is limited, thus am not necessarily capable of thorough explanation using
simple words. So highly sophisticated is the methodology of science that
experts in logic do not necessarily agree among themselves, while achievements
in science often bear no relationship to this knowledge. There are indeed quite
a number of scientific experts who know nothing about the methodology of
science. On the other hand, specialists in the methodology of science are
seldom accomplished scientists. Logic often probes to the extreme of the ivory
tower, marvelous though at its sophisticated realm, a lot of sacrifice is
always required in getting there.
From the
viewpoint of rigorous philosophical logic, what I know of is only sketchy. It
was fifty years ago when I worked on this knowledge. However, scientific
methodology can also be viewed from another perspective – the empirical
approach linking abstract theory and the real world. This I know more. The
content of this chapter, combining philosophical logic with empirical linkage,
is different from the methodologies referred to in common textbooks.
Ultimately, only out of the ivory tower can science be practical.
I have
named “scientific methodology” as the title of this chapter and indeed
elaborated on it. It is not that this knowledge has any indispensable
significance on this book. Rather, China’s cultural traditions often talk about
benevolence, brotherhood and morality, yet without the spirit of empirical
testing, there is a deep-rooted misunderstanding of the nature of science. In
the twentieth century, the influential “Three
Principles of the People” and “Marxism”
– or other doctrines – added an opaque membrane to our students’ understanding
of science. As earlier mentioned, this book is written for Chinese students. I
consider scientific methodology having a more profound impact on Chinese than
on certain Western ethnic groups. Do not drill into the dead end of the
methodology of logic, albeit a rough grasp is needed.
Economic
explanation is an empirical science. Its nature is identical to that of natural
science, with both adopting identical scientific methods. However, in terms of
the nature of content, economics is rather different from natural science,
hence when approaching issues, their scientific methods have different
emphases. There are two reasons. On one hand, economics’ laboratory is the real
world which is neither built by economists nor controlled by researchers, thus
its difficulty in observations is distinct from that in natural science. On the
other hand, economics is for explaining human behavior, but economists are also
human, therefore it is inevitably used to a certain extent in explaining themselves.
Objective judgment is thus more difficult than in natural science.
In terms
of methodology, the focus of economics is different from that of natural
science. First, I believe economics should not be so deeply influenced by
physics. As aforementioned, the so-called equilibrium and disequilibrium are
real phenomena in physics, whereas equilibrium and disequilibrium do not exist
in the real world of economics. As explained before, they are concepts at best.
Certainly, some economists consider that equilibrium and disequilibrium refer
to observable market phenomena. This is an embarrassingly fatal mistake.
Readers should note that when I mention equilibrium in “Economic Explanation”, I mean sufficient constraints are in place
in deriving a refutable hypothesis, not that there exists certain observable
equilibrium point.
I regard
as unimportant the use of mathematics in economic explanation, though
professional economic articles nowadays apply mathematics even more than those
in physics. Besides physics, other natural sciences seldom employ mathematics.
I am not saying mathematics is not useful for economics, yet mathematics is no
economics. Mathematics is a miraculous language: the logic of every solvable
equation must be correct. However, correct logic does not necessarily match
with correct content. Some people are adept in using equation to deliberate, I
on the other hand regard that as a barrier hindering my emancipated style of
thinking. Though not using mathematics, my logical reasoning is seldom wrong. I
believe our fellow students should learn more mathematics, but in deliberation
ought to consider which section of our own brain is more superior. With more
practice, deliberation without using mathematics can leap to and fro more
readily, hence is way better in respect of imagination.
There is a
common misconception. Some people consider it more accurate using mathematics
or statistical equation for reasoning or testing. This is not correct.
Measurement is the ranking of numbers, and accuracy in measurement depends on
common acceptance of such ranking. This is another topic of philosophy, and I
will demonstrate how this is to be approached when analyzing transaction costs
in Volume III.
Second, I
have not undertaken hypothesis testing in natural science, yet hypothesis and
testing in economic explanation always start from changes in constraints, which
is equivalent to starting from changes in test conditions. Saying the
occurrence of “A” implies the occurrence of “B” is in fact saying changes in “A”
will lead to changes in “B”.
As earlier
said, to predict is the same as to explain, though they differ as to ex-ante or ex-post. To predict is to infer what phenomenon will occur after
noting changes in constraints; whereas to explain is to trace the cause of the
occurrence of a phenomenon to identify the changes in constraints. Logical
structures being the same, to predict is therefore the same as to explain.
Given different approaches in investigation, it is hard to judge which method
is more demanding. Let’s have a think. Noting a phenomenon, in order to
explain, we need to trace its cause to identify the changes in constraints. But
there are countless constraint changes in this world, which item or which
combination of items should we choose? Tracing changes in constraints ex-ante requires theoretical guidance –
not an easy task. What about prediction? Noting changes in constraints, we
therefore theorize to infer what phenomenon would follow suit. The problem is
that changes in constraints ex-ante
are not necessarily stable. They could keep on changing to overturn the
prediction which was originally failsafe. The inference I made in 1981 that
China would go down the road of market economy was based on certain observable
and reasonably stable changes in constraints. Nevertheless, to have been
correct, needed was God’s blessing in keeping the changes stable.
This
raises another important yet related topic. The aforementioned ex-ante inference or ex-post explanation requires a good
application of underlying theories and concepts. However, in the so-called
applied economics textbooks that we find, their approach is first proposing a
theory, then stuff into it real-world examples. This basically in search of
“right” contravenes the principle of empirical science: in search of “wrong” in
the hope of not being refuted by facts. In a stricter sense, empirical science
looks for refutability, i.e., a theory or hypothesis has to be potentially
refutable by facts. Refutable and testable are the same. Having different
starting points and different intentions, it is therefore no easy matter to
learn a great deal from these “applied” textbooks.
The last
point is “if invisible, then non-testable”. Economics these days frequently
contravenes this simple philosophy, leading to disastrous developments. If we
say the occurrence of “A” will lead to the occurrence of “B”, both A and B must
exist or at least be visible or touchable in principle. We have said that the
starting point of a theory is often abstract, allowing the existence of
in-principle non-observable variable. We should avoid non-observable or
non-realistic variables as much as possible. After years of investigation,
there is only one unreal variable we have to accept as inevitable: quantity
demanded. My research has gone through thousands of miles, and this is the only
exception.
Throughout
today’s developments in economics, there are innumerable non-observable
variables or behavior: game, motive, shirking, blackmailing, threatening,
concealing, laziness, opportunism, etc. Since in practice they are
non-observable, non-measurable and non-testable, only God knows if they exist.
As stories, they can be told very logically and credibly, and may contain
certain religious flavor. Yet invisible means non-testable. Science that cannot
be tested has no explanatory power.
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