Every word, in every language has a life cycle. Words are used by humans in their daily affairs and humans are complicated creatures whose decisions are affected by a complex interplay between reason and emotion. Because of this, words evolve in such a way that, with time, their meanings go through “mutations” which might eventually lead to such a radical change that they cannot be used anymore within their original scope. The word “science” is not different.
Most modern discussions concerning what science actually is, end up falling in the semantic category. The reason for that is the fact that, at some point, the meaning of ‘true knowledge’ was attached to the word science. The study of methods to discern what kind of knowledge is ‘true’ and what is ‘false’ ended up being associated to the word and, as these methods became successful, the word science acquired a respected status. Humans are attracted by reputation as this result in better chances of satisfying emotional goals. Therefore, the importance of guaranteeing being associated with the word and the status it provides.
The original meaning of the word “science” seems to have been very little ambitious, simply meaning any kind of knowledge. Greek philosophers seem to be responsible of seeking a way to separate knowledge that would actually describe how the world works from that which would not. This was when the word science started to acquire its respectability.
Let us forget the word “science” for now and consider the following problem. It is undeniable that there are repeating patterns in nature. That is a trivial observation whose simplest example is the fact that the sun rises with some predictable regularity every single day. In fact, that creates the basis on which we define what a ‘day’ is.
The fact that patterns exist allows us to write down sets of rules for these patterns. The problem I want to propose is that of checking if a pattern we think we found is really there or not. This can be thought in terms of a competition.
The competition consists in the judges writing down a set of rules that generates a sequence of numbers. The judges hire a programmer to create an app that uses the rules to generate the required sequence of numbers. Using that program, the judges generate a dataset which is then given for different groups of people and their task is to find the original pattern, the judges’ rules, that generated the data. Once each group has prepared their entry to the contest, one has to decide which one is the winner. In this case, of course, all that is needed is to check which group gave the correct rules.
The way it is, it is easy to decide who is the winner. Suppose now that, somehow, the judges lost the original rules and cannot remember then. All they still have is the app, but the programmer has already gone on holiday and cannot be contacted. They need to decide which group is the winner. Can they do that?
Indeed, there is a way to select the winner and we will call this the S-Method (‘s’ for ‘selection’). The S-Method is an elimination method. The judges start to generate additional numbers beyond the original dataset and ask the groups to do the same with their rules. Each time a group generates a number which is different from the one generated by the app, the group is eliminated.
Unless two groups have equivalent rules, which means that they always generate exactly the same numbers, the S-Method guarantees that at some point one will find a winner. This can take time, but will eventually happen.
But there is still one limitation of the S-Method. It serves the objective of finding a winner, but it cannot guarantee that the rule given by the winner corresponds to the correct one. No matter how long you test, although you might be able to catch a failure and debunk the winner’s method based on its predicted next number, one will never be able to tell if the generated numbers will always work for sure.
Notice that there is one key idea of the S-Method: it requires each group to make predictions about the next number. That is because one wants to check if the rules, or in other words the inferred pattern, are indeed the correct ones.
There is no way to check if the rules work without testing them against the data. If one of the groups simply created a fancy story that would generate only the original dataset but could not be used to generate additional numbers, they would not have identified the original rules.
The possibility of generating a prediction that can be checked against data generated by the original rule has the name of falsifiability. Entries to the contest which are not falsifiable, cannot be judged. In the case of the contest, they are automatically wrong as the original rules do generate more numbers.
Consider now that we are dealing with nature. We do not really know if there are indeed patterns in every phenomenon. Experience indicates that there is by the simple observation that we were able to find many up to this day. If our guesses about a phenomenon are falsifiable, then we can apply the S-Method to select the best guess and even to eliminate all of them.
However, it might be possible that in nature there are phenomena to which we cannot find a pattern in principle. In those cases, the phenomenon cannot be attacked using the S-Method. It is out of its reach. Fortunately, those situations are rare and do not affect our lives significantly, only emotionally.
The Certified Scientist
You can appreciate that both the effectiveness and the limits of applicability of the S-Method are well established above. It turns out that, at some point in history, the word ‘science’ started to be associated only with knowledge that could be checked using the S-Method.
Because the S-Method is clear, objective and powerful, it started to yield results. Those who dedicated themselves to check which of those guessed patterns up to that point were valid or not using the S-Method succeeded in selecting the rules that actually worked.
It did not take long for people to see that explanations in terms of gods and spiritual entities for the natural phenomena were not falsifiable. This would not be too critical in principle, the greatest problem is that people started to actually find falsifiable descriptions for those phenomena.
Those people who started to dedicate themselves to tailor falsifiable models for natural phenomena then became the new ‘scientists’. They gathered together and started to teach others.
The success of this new meaning of ‘science’ made the title of ‘scientist’ a desirable one. Desirable because of the credibility associated with it. And then the scientists started to give certifications for those who studied with them. They created the ‘certified scientists’ and this was the beginning of a new change in meaning.
The problem with certifications is that, at some point, they stop being about the original qualities of the product and become a matter of politics. Those who receive a certification that cannot be revoked will tend to ignore the very rules that allowed them to earn it in the first place when those rules are against their personal beliefs. Because the individuals themselves have the power of certifying others, the certifications start to become degenerate with time.
The unintended effect is that the original meaning of the very words that defined the certification start to drift away. Because now you have ‘certified scientists’ that will not admit losing their certification, they will lobby to include in the meaning of ‘science’ whatever they personally do or think that they should do.
Finally, the term ‘science’ starts to be associated not with the S-Method anymore, but is now used to describe a profession whose definition bends according to the wills and necessities of those who have the power to give certifications.
Here lies the kernel of all modern discussions about what is ‘science’. Discussing the validity of the S-Method is not an issue, the issue became whether give or not the ‘certified scientist’ title even when one ignores the S-Method.
The S-Method, as powerful as it is, is just a selection procedure. It requires models to select. This guaranteed ‘model engineering’ as an important part of what became known as science.
As more data about natural phenomena accumulated and models of the simpler ones were selected, model engineering became more complex. Whenever complexity increases in an area, specialisation naturally follows. This resulted in many certified scientists becoming specialised in model engineering.
Today’s model engineering is a very sophisticated process and mathematics plays a key role on it. Mathematics allows us to concisely describe patterns in natural phenomena, including the ones used by humans to reason. Once these patterns are codified and selected as valid ones, they can be trusted until a reason appears not to do so.
Model engineering is a very difficult area and requires a lot of ingenuity and creativity. In modern times, it also requires a good knowledge of mathematics and a certain ability to work with it. Many of the most famous certified scientists are theoretical physicists because their mathematical ability is recognised outstanding.
The use of mathematics provided a means to build models that go beyond the practical reaches of the S-Model in terms of economic and technological feasibility. There is no known limits to the kind of models that can be engineered, the only constraint being that they should agree with collected data and not contradict those which have already been selected by the S-Method within their limits of applicability.
Many certified scientists will only concentrate on model engineering and leave the task of selecting models to other specialists. There is nothing wrong with that in terms of profession as long as they remember that the fact that a model has been engineered using valid methods still does not mean that the model is the correct rule to describe some natural phenomenon.
Science without the S-Method
What happens if we keep model engineering and discard the S-Method?
Many people today are lured into believing that, as long as a model involves mathematics, it is a good model, but model engineering can be completely detached from the S-Method. As a consequence, using mathematics does not per se provide any extra credibility to a model.
Religion and mysticism contain many examples of models which can even be based on mathematics, but nevertheless would either not be vindicated by the S-Method or even not falling under the scope of its application. Model engineering, without the S-Method, falls into the same category.
Questioning is not Enough
Rebellion against rigid impositions is a good practice. It is by questioning traditional rules that reasoning flaws can be found. However, rebellion for the sake of rebellion is as useless as conformism. One must question things with a reason, otherwise the questioning becomes senseless.
Critics attack the S-Method, or the falsifiability principle, as being too rigid, but ignore what is the original objective that led to it.
If one wants to change the meaning of science once again from a method to find correct models to describe nature’s patterns to a list of professional obligations, there is very little to do to prevent this. What cannot be tolerated is that this new meaning of science still demands to be recognised as something that achieves this.