One of the student phrases I found irritating was: 'Well, it's true for me'. It represented an extreme form of relativism. Mind you, there is a long history of that sort of attitude which goes back to the early Greek philosophers, and versions of it are still promoted by philosophers such as Foucault. There is an early statement of the view by the early Greek philosopher Protagoras. Protagoras writes: 'Man is the measure of all things; of what is, that it is, and of what is not, that it is not'. Socrates anticipates the question which will be in every reader's mind: What on earth does Protagoras mean?
Protagoras was mainly concerned with perceptual knowledge and variations in it. He thinks that these variations lead to the conclusion that whatever any individual believed as a result of their individual perceptual knowledge was true for him or her. Socrates easily disposes of this position by pointing out that people make perceptual mistakes and can easily be brought to recognise these and recognise that what they thought was true was in fact a perceptual error.
But there are more sophisticated versions of the argument. They draw attention to the fact that reality is interpreted via conceptual systems and that conceptual systems vary. For example, it might be argued that we interpret the world via the conceptual system of modern science, whereas anthropologists have drawn attention to and investigated other conceptual systems, based perhaps on the examination of animal entrails. I agreed with my students that what is known as 'science' was developed over centuries by mainly (but by no means exclusively) white, male scientists. But then I would pose the question: 'Would you fly in an aeroplane constructed as a result of technology based on animal entrails?'
Of course, scientists sometimes get things wrong. But when that happens, it is better science which can show what was wrong and how to put it right. If the aeroplane crashes, scientists look at the black box rather than the entrails of some unfortunate animal.
The 'true for me' brigade – the relativists and sceptics – will not on the whole fight a battle over the exact sciences. Mind you, there is a lot of room for scepticism about cosmology – the Big Bang, dark matter and so on.
The internet is full of scepticism about medical science – vaccines were a recent target. Moreover, the technology which science has created – the internet – has provided the means ('platform', I believe is the word) for the anti-science movement.
The sceptics I used to encounter were the hard men from Glasgow University Beer Bar, but nowadays the trolls from the internet are in their millions and are a much nastier and more influential bunch. Radical scepticism has morphed into the post-truth movement.
Is there anything to be said in favour of scepticism about science? Do they ever have a point? I don't think you will find much by way of argument from the internet trolls, but seeds of doubt can be sown by the constantly repeated slogan 'Follow the science', and when things go wrong there is the politicians' excuse: 'We followed the science'. What
were they following?
The usual criteria for acceptable science are that what is being studied has been acquired by observation or experiment; that the scientist has reduced the data to the factors being researched, or that the science is reductionist; that the conclusions can be tested by experiment. Medical science may be required to satisfy further tests in the form of clinical trials, trials that might consider factors such as side effects. The results of most sciences are quantifiable, generalisable and frequently offer causal explanations. They are therefore objective in the sense of being independent of personal bias. And, importantly, they provide understanding of the normal workings of nature.
Recently, however, a method which does not satisfy all these criteria has appeared in political discourse. It is known as mathematical modelling and goes along with its first cousin the algorithm. We all tend to be impressed with complex mathematics. Mathematics offers a means of generalising and predicting what may happen in the future. But its reliability depends both on the reliability of the original data and on the assumption that the future will resemble the past.
In some areas, these assumptions may be fair enough. For example, in weather forecasting, mathematical modelling makes an important contribution because the meteorologists have reliable data obtained from weather satellites. Even here, however, meteorologists, having got it wrong in the past, sprinkle words of caution into their forecasts. But there can be doubt about the use of and reliance on mathematics to model the spread of a pandemic, for the reason that the original data is uncertain. The data is the behaviour of the virus and the behaviour of people.
As we now know, a virus can mutate several times. Moreover, the behaviour of people is highly uncertain – how they socialise, how they travel, what they eat and other unknowns. Perhaps those who can understand the mathematics can give more accurate predictions than we can get from entrails, but the results in the UK leave room for the sceptic. We shouldn't be over-impressed by advanced maths – it is no more reliable than the data on which it is based.
Economists also nowadays make plentiful use of mathematics and mathematical modelling. Some even wish us to see economics as a science. But economists don't seem to be more accurate now than they were in the days of Adam Smith. A letter to The Times
signed by 30 leading economists will be answered the next day by another 30 saying the opposite. Once again, it is important to examine the data and the arguments rather than the maths.
Economics is an interesting and worthwhile subject but it does not have the value neutrality of science. It is better seen as 'political economy', and placed in a wider context of values.
The term 'algorithm' has become fashionable. But it is just a method of getting from A to B, and it is hardly a new idea. Descartes writes that his philosophy is just a matter of breaking a problem down into the right steps. But, again, algorithms are no substitute for judgement. Readers may remember the mess that educational authorities got into the other year when it was not possible for schools to have exams. Kids were given quite improbable marks because the algorithm started from their postcode. No doubt environment has an effect on exam success in some cases, but it is a wildly inaccurate method of predicting an exam result for a specific candidate.
Once again, the fiasco came about as a result of a fascination with modelling, and technical words of unclear meaning. Lord Kelvin has a lot to answer for. He has helped to skew research in many subjects. His bad influence stems from the following statement:
I often say that when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science, whatever the matter may be.
This has led to bizarre uses of numbers. Medicine, for example, uses many bogus measurement scales. Quality of life scales abound, and palliative medicine even has a spirituality scale (1-10).
Readers may like to reflect on their reactions if they saw their hospital case notes and learned that their quality of life was 7. My reaction would be similar to my reaction on learning from The Hitchhiker's Guide to the Galaxy
that the meaning of life is 42. Of course, a sceptic might say: 'That's true for me'.
Robin Downie is Emeritus Professor of Moral Philosophy at the University of Glasgow