Scientific rationalities and why they are important today

Without any introduction, the reader is probably thinking: rationalities? Why plural? Is there more than one way to reason with science? More orthodox readers would say this is an anti-scientific post by a “flat Earth guy” or a relativist where everything goes. NO WAY! With this post, I will try to be crystal clear: (a) there are different ways of reasoning scientifically, (b) there are classes of problems that impose different scientific rationalities and (c) a mismatch between the problem and the rationality leads to untrustworthy conclusions, although scientifically grounded by the wrong methodology.

Without being scared of controversies, let’s talk about vaccines (far from being my own expertise so I won’t mess around with technical terms). It is possible to prove the effect of anti-bodies in pathogens in a causal way. Scientists isolate the elements, design experiments in a controlled environment, and using the classical scientific method of hypothesis testing. There is a causal effect that can be proved in ideal conditions. However, from this first step to the final goal, that is to have a vaccine, there is a long way. No details since I don’t know them. What I know is that the ideal lab conditions where causal effects can be tested are very different from real-world interventions with a predetermined objective, in this case immunization of whom is vaccinated. Clinical trials are quite different from lab work of proving, but it also follows a similar classical way (now with statistical grounds) to test the effects of the vaccine.

Now, look this equation: \displaystyle \sum_{n=1}^\infty \frac{1}{n^2} = \frac{\pi^2}{6}.

Can you tell me is if this is true or false? How? (take a deep breath and don’t think how to do it). Yes, it is possible to judge this if I define very well the field I am playing. This means what are the symbolic rules of this statement. But, one thing is certain: It is not via the classical empirical method we evaluate this symbolic domain; It involves a non-classical, symbolic scientific rationality. It is possible to create maps from one domain to the other (e.g., quantum physics or information theory). However, not all symbolic domain must have a “physical” counterpart. But, in the cases they have, then some sort of the classical scientific rationality shall be used.

NOTE: This is very tricky!!!!

Let’s go back to our vaccines that have been proved to work in humans. Consider a situation where the number of vaccines is only 10% of the population. Now, a group of epidemiologists needs to decide to whom the vaccine should be given with highest priority. They created a symbolic model based on a branch of mathematics, called graph theory. They used a very advanced multi-dimensional model and decided that children would be the target group since they are identified by the model as the hubs of the infection. This is a clear intervention in the real-world, based on a symbolic model.

Let’s say that an unexpected outbreak happened even with the planned vaccination. The epidemiologist said: This is not possible, our model guarantees that there would not be an outbreak, this cannot be true. It is: the material reality is what it is, and not otherwise. Maybe the model was the problem. So, does this mean that science is wrong and that a proved vaccine won’t work? No, this means that they were designing interventions in a system that they were part of and then capable of internally affecting its dynamics, creating reactions to their actions! Let’s consider that the population of that region was subject to a heavy campaign against established institutions so that any governmental program was seen with mistrust so significant part of the population decided not to take their kids to vaccination. The model could not internalize that the proposed intervention would create a reaction in the society that the model was about.

But, if this mistrust did not exist, would the model work? The mistrust happened. FULL STOP. It is impossible to hypothesize otherwise (like commentators of football or politics like to do). It is impossible to know what the outcome would be in different conditions since these conditions will never repeat themselves. In any case, there is an intervention and the scientific rationality needs to be different: an interventionist scientific rationality. In this case, we can have good models to propose interventions, but we could never claim that the model is the reality, and that under different condition it would work. When interventions are internal to the system under consideration, it becomes self-developing where internal elements can act within the system based on some sort of structured inner knowledge. Maybe too complicate… The bottom line is: such a class of problems imposes a non-classical, interventionist, rationality with self-referential models with reflexive nature.

All in all, putting the problems in their right places while rationalizing accordingly is necessary to make correct truth claims about reality (= science). Models can be consistent in their own domain, but they do not always reflect the real-world. Engineering sciences and medical sciences know this quite well in practice, but very little in theory. I won’t comment about economists. I always think about how several (technocratic) decision-makers would like to map clinical-trials (a classical problem in medicine) to evidence-based policies (an interventionist problem affecting society) based on rigorous mathematics (a symbolic problem with internal consistence, but very likely supported by unsound assumptions beyond its domain like rationality). Sometimes I wonder why there is a mistrust in science. My view is that many “experts” claim their models are the reality and the issue is the real-world (“people behave irrationally and we need to teach them how to behave rationally”-style of argument). The math is not wrong, the narrow-minded overspecialized training of “experts” might be the problem. Enough now. I think this can provide enough food for thought about COVID-19 models, interventions, vaccines, government, antivax and so on.

If you reach this far, I will give you a bonus: Think about my text and try to reflect on this old fallacy: “my uncle smoked and drank a lot, and died at the age of 85, while a friend of a friend never did and died from a heart attack at 35.”

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