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.