The surprising properties of Hard Science and some thoughts on Liquid and Volatile Sciences

So far, I’ve discussed what I can recognise as absolutely true (very little), how all the rest of my knowledge has to be assumed to be imperfect and why it is still possible evaluate the (relative) reliability of a given piece of knowledge, if and when direct or indirect experience can be used  to put such knowledge to the empirical test. This led me to science, and inspired a definition of the Scientific Attitude: an intellectual stance that I see as the foundation of what is known as the Scientific Method.

It’s all good and well, but I haven’t even defined Science. This post is my attempt to finish-up the whole epistemological premise, so to define the method of inquiry that inspires the whole blog.

There is a good reason why I haven’t provided a full definition of Science: it’s because one can’t define a unique, monolithic Scientific Method, as it ultimately depends on the subject that is being studied. My definition of Scientific Attitude is supposed to help, but it actually injects some complexity: the attitude itself can be applied to any subject and may or may not produce reliable knowledge. To try and put some order, I subdivide Science in three broad categories arranged over a continuum that starts from Hard Science, progresses to Liquid Science and then fades on the blurry edges of Volatile Science.

The hard side is the one where the most reliable knowledge is produced , the more one moves towards the volatile, the more uncertain one has to be of the results. The Scientific Attitude inspires all efforts, from “hard” to “volatile” and allows to use the “science” substantive in the three definitions. To try to convince you of the usefulness of this classification, I’ll need to spend some more words defining the three types and then highlight their relationships.

Hard Science:

Some people may think this is the only science. It is the kind of inquiry that is legitimately trying to produce models that can be used to describe reality with the highest possible level of confidence, so much so, that one can use the word “facts” to describe its findings [Jerry Coyne has just published a commentary that includes some brilliant definitions of the outcomes of Hard Science: hard facts, defined in a truly scientific way]. When I try to enter my house, I can assume that trying to pass through the door will be painfully useless even if Quantum Mechanics informs me that there is a calculable probability that the whole door may jump into some other part of the universe. This probability is so inconceivably small that I can safely ignore it and believe that the need to open the door is a fact. My Newtonian model of reality is safe in this context, and I am confident that I will not need to question it. Similarly, we now have other more complex models of reality that are equally reliable and can be used to make endless predictions, with known accuracy. For example, the GPS calculation need to account for relativistic effects in order to accurately calculate the receiver position. This is true each and every time, even if the model used is counter-intuitive and rather complex. The key reason of this high level of certainty is that all the variables needed to produce meaningful predictions are known and their role is understood. Whenever this happens, the knowledge produced is approximately certain (hard) because we can demonstrably make reliable predictions.  Examples of hard sciences are of course physics, but also big chunks of chemistry, molecular biology and biophysics.

Liquid Science:

The typical example is Cellular Biology (chosen also because it deals with molecular reactions that almost exclusively happen in a watery medium). Here the degrees of freedom start to get out of control: a single cell is such a complex machinery that no one can assume that all variables are known, accounted for and under control. Hence, Liquid Science usually produces probabilistic outcomes, accompanied with numerical estimation of confidence levels or similar heuristics. However, it is still possible to try and reduce complexity, concentrate on some limited process and find out plenty of “facts” (for example, Krebs cycle and countless other metabolic pathways that we have understood and described rather accurately). In this sense, proper Liquid Science is always trying to become Hard: it strives to identify and describe all variables involved in a process so to eliminate all sources of uncertainty. The interesting fact is that this is not always possible, not even theoretically. For example, many diseases will have different effects on an individual, depending on the current state of the whole system. This in turns depends on genetic, developmental and environmental factors and on how they interact, making it guaranteed that medicine will never become a completely solid science: we know for certain that some significant variables will always elude us (at least for the conceivable future), so that medicine is condemned to remain somewhat liquid. Clearly, good faith medicine will always strive to solidify as much as possible, and indeed any Liquid Science endeavour should do the same. I’ve already mentioned some typically liquid disciplines: cellular biology, most medicine, but one could count also some very limited aspects of Social Sciences as well as mechanical vibrations and fluid dynamics.

Volatile Science:

This is the kind of inquiry where most of the significant factors are known to be unknown and/or un-knowable. The typical example is meteorology: we know enough of it to be sure that chance is always significant. We can, with great effort, produce probabilistic predictions with the aid of freakishly complicated mathematical models, and we can strive to increase the accuracy of such predictions, but we know from the start that we will never fully understand exactly what happens and why. Meteorology therefore is currently placed somewhat close to the Liquid interface, but is still volatile, especially if one considers different time scales.  There are more extreme examples, and have to do with the genesis of all scientific theory. Whenever one tries to apply the Scientific Attitude to a new domain for the first time it can be assumed that most variables are unknown: in this situation one will try to identify some significant elements, isolate them, quantify their significance, and in such way start the long journey to solidity. This is also the level where new revolutionary theories are conceived. Any major “a-ha” moment instantaneously sends you into volatile mode, where little is certain and most needs to be solidified once more. Think of how many possibilities opened up to Darwin when he first contemplated the idea of natural selection, or how was proceeding Einstein when he was using pen and paper to formulate his theories of relativity. Both examples eventually went neatly down the solidification route, and are luminous examples of what a Scientific Attitude can produce.
It is worth noting that some fields of inquiry are inherently assumed to be firmly volatile. Some times this happens for semantic reasons: theoretical physics is inherently volatile, of course, when it starts to solidify it isn’t theoretical any more. Other fields however assume (rightly or wrongly) their volatility, think of Literary Studies and the Humanities in general, but also of big strands of Social Sciences.

Conclusion: the common principles

Remember, all three levels have some things in common: they all use evidence to produce models of reality that are expected to describe what can be observed and, in most cases, make predictions within their own domain. Another common feature is what I’ve defined the Scientific Attitude: this is the assumption that all knowledge is imperfect or provisional, that one can always improve it, and that in order to do so it may be necessary to reorganise the current understandings, even in drastic ways. To use the hardness metaphor, one needs to accept that in order to proceed towards solidity, sometimes it will be necessary to be thrown back into the volatile level, no matter how solid the current models appeared to be just a moment ago. Furthermore, one has to strive towards solidity, the moment one declares a particular field to be “as solid as it gets” it condemns her/his subsequent efforts to become unscientific, or, if you excuse my French, to become “Flatulent Science” at best.

In the next post, I will try to use the classification above in a number of different ways: first and foremost, I’ll show how it can be used to discriminate proper science from make-believe and/or other nonsense, something that is useful when one is dealing with subjects that are still on the Volatile side. I will also show how this perspective offers a neat way to understand the seemingly irresolvable (and currently revived) debate (war?) between the “two cultures”. Finally, I will look at how these categories are useful to explain my past experiences as a scientist and how they shed some light in my current motivations.

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Posted in Philosophy, Premises, Science
2 comments on “The surprising properties of Hard Science and some thoughts on Liquid and Volatile Sciences
  1. […] the previous post I laid down a rather lengthy definition of how scientific efforts are organised and what they […]

  2. […] that knowledge itself  is subjective). The result is another series of posts, that introduced my metaphor of solidity. Real science is what tries to become more and more solid, that is: tries to produce more […]

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