In a previous post, I tried to make a case for a separation between economists as engineers and economists as scientists. In this post, I make my view of these two roles more precise in a general sense. I will dedicate several posts to examples of engineering and science in economics.
For a scientist, the only thing that matters is
whether a given law holds true. For example, I only care whether
Newton's laws are true or not. They are not,
so I just should discard them as a way to explain the world. And that
is basically what physicist have done. The fact that Newton's law can be
approximately true in some conditions does not matter for the
scientist. It is interesting for learning purposes or for engineering,
but it does not say anything about the true behavior of the world. We
have better representations of the world that have a wider range of
applicability. The truth is not convenient nor simple, it is true. For a
scientist, the ultimate criteria is whether a theory survives a crucial
experiment.
For an engineer, the only thing that imports is that a plane flies.
Whether he can explain why it does does not really matter. Sometimes,
engineers tweak machines based on experience and obtain good performance
without being able to explain how. Sometimes, engineers use laws that
have proven to be wrong (e.g. Newton laws) because they offer convenient
simplifications. They will only use the more complex (and true) version
of the law if it provides sufficient improvement. For example,
engineers in charge of the GPS switched from Newton to Einstein
relativity because it provided much better location performance.For
en engineer, the ultimate criteria is the performance of the device:
does it do what it is supposed to do, as efficiently as possible?
Scientists and engineers are also easily differenciated by the way they deal with the problem of induction.
We know at least since Hume that it is not because some phenomena has
happened in the past that it will happen in the future. Hence, every
scientific law is provisional. Since Popper, we know that truth in
science means "non refuted yet." So scientists are aware of the
provisional nature of knowledge. This is not a problem as long as you
are contemplating the universe in search of an explanation of how it
works. For engineers though, this is a tough problem, because it means
that what has worked in the past might not work in the future. All their
devices might fail for an unkown reason and they have to accept that
and live with it.
A final difference between science
and engineering is how they deal with Cartesian slicing. Cartesian
slicing is the idea that the best way to study a problem for a scientist
is to slice it into smaller and smaller problems that can be studied
independently. A consequence of this is the ever increasing
sophistication and complexity of scientific explanation in every
subfield of science. Engineers cannot slice too much, because they have
to deal with the fact that all the separated phenomena might interact in
the real world and have an influence on their devices. For example, it
is hard for an engineer to ignore frictions. Engineers face
computational limitations, and they therefore have to make useful simplifications, like ignoring one phenomenon, or one side of it, for
the sake of implementation. When diregarding a phenomenon, they assume,
and very often check, that it does not alter the efficiency of their
device too much.
Overall, science is about
provisional knowledge of non refuted laws on sliced phenomena while
engineering is about making device that work, sometimes using useful
simplifications.
I am not saying that engineers and
scientists do not talk to each other or live in completely separate
worlds. Engineers constantly seek to use more recent laws to perfect
their devices. Scientists try to understand why some of the enginner's
tricks work, or why sometimes something they predict should work does
not. There is a fruitful and fertile dialogue between scientists and
engineers. All that I'm saying is that scientists and engineers have
distinct aims, distinct criteria for success and distinct methods.
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