If academic knowledge were simpler to understand and use, more people would understand more, misleading misunderstandings should be less prevalent, the education industry would be cheaper and more efficient, and humanity would make faster and better progress. I am convinced this is an idea with enormous potential, but it does not seem to be on anyone's agenda, and there are very strong vested interests opposing it.
Human civilization depends on knowledge. Lots of it, ranging from how to use Pythagoras's theorem to produce a right angle to the science behind mobile phones and GPS, from the idea that germs cause disease to the science behind modern medicine, from stories and ideas about to produce them to the theories behind voice to text software. There are lots of different types of knowledge, and the boundaries of what counts as knowledge are a bit fuzzy. I'm talking here about knowledge in people's heads, not the knowledge in databases and AI algorithms.
Some knowledge is easy and we pick it up naturally as we grow up. But some of it is complicated - it's difficult to learn and use: this is the rather fuzzily defined "academic" knowledge that I'm concerned with in this article. Two massive, interlinked, industries have evolved to cope with these difficulties: education which disseminates the knowledge, and what, in the absence of a suitable word, I'll call the knowledge production industry or KPI. (In universities knowledge production is called research, but from my point of view this term is too restrictive because it seems to imply the search for the "truth" by modern academics, whereas I need a term which also covers the work of Pythagoras and people trying to devise ways of making driverless cars.)
The KPI - scientists, researchers, and innovators both now and throughout history - make discoveries or invent theories and better ways of dealing with the world, and the results of their labours are then passed on to a wider audience by the education industry - schools, colleges, universities, textbooks, etc. The education system gets a lot of analysis and criticism: better ways of teaching and learning are proposed and tested, and inequalities of access and the ineffectiveness of a lot of the education system are bemoaned ad nauseam, and so on. But the knowledge itself is seen as given, fixed, handed down from the experts, and the job of education is to pass it on to students and the wider public in the most efficient possible way.
My suggestion here is that there are often good reasons to change the knowledge itself to make it simpler or more appropriate for the audience. An important KPI (key performance indicator) for the KPI (knowledge production industry) is the simplicity of the product.
This idea stems from a number of sources, some of which I'll come on to in a minute, but first a little thought experiment. Imagine that a bit of knowledge could be made simpler by a factor of 50%, so that, for example, the time needed to learn it, or to use it, was halved, or that it led to about 50% fewer errors and misconceptions in implementation. Imagine this applies to all knowledge taught at universities and similar institutions. Students would learn about 50% more, or they would understand about 50% better, or have about 50% of their time free to do something else. Leading edge researchers would arrive at the leading edge in the half the time they take at the moment, giving them more time to advance their subject. If such simplifications could be made across the whole spectrum of knowledge, this would represent an enormous step forward for humanity.
You might think that the innovators and researchers of the KPI would have honed their wares carefully to make them as simple as possible, so a 50% improvement is simply impossible. But you'd be wrong. Very wrong. Except at the leading edge there is absolutely no tradition in the academic world of trying to make things simpler. Simplicity is for simple people, not academics who are clever people. I've had a paper rejected by an academic journal because it was too simple: it needed to be more complicated to appear more profound. Teaching and learning methods are tweaked to make them easier for learners, but the knowledge itself is considered sacrosanct: the experts have decreed how it is, and that's it.
There are exceptions: areas where simplicity is a prized quality of knowledge. One interesting example is the leading edge of one of the most complicated areas of human knowledge: the physics of things like quantum mechanics and cosmology. I've just been watching an interview of the physicist Roger Penrose who was recounting his difficulties with lectures at Cambridge University: they were too complicated to understand so he had to invent simpler ways of looking at the issues. Einstein is supposed to have said that everything should be made as simple as possible, but not simpler. I also came across similar sentiments by two Nobel prize winners, Paul Dirac and Murray Gell-Mann, and another Nobel winner, Richard Feynman invented a type of diagram (subsequently called Feynman Diagrams) which gives "a simple visualization of what would otherwise be an arcane and abstract formula" (Wikipedia). Where things are really difficult, simplicity is essential. But behind the pioneers of the discipline, the normal practice is to accept what the gurus have produced.
The history of science and the growth of knowledge in general are punctuated by occasional revolutions that often lead to far simpler ways of looking at things. The invention of the alphabet made record keeping far easier and more flexible and all sorts of stories could have a wider audience, and the replacement of Roman numerals by the current system (2019 instead of MMIX) did a similar job for arithmetic. The ideas introduced by Galileo and Newton provided a way of understanding and predicting how things move which can be summarised in a few simple equations and covers everything, both on earth and in the heavens. This would probably not have been considered simple by contemporaries of Galileo and Newton, or many present day students, but the equations are staggeringly simple when you consider what they achieved. Similarly, Charles Darwin's theory of evolution by natural selection provides a ridiculously simple explanation of the evolution of life on earth.
But what about the detailed, mundane stuff that students spend their time learning? Quadratic equations and statistics, chemistry and the methodology of qualitative research, medicine and epistemology? Are there opportunities for simplification here?
My contention here is that there are, and the fact that are almost never taken is a massive lost opportunity. There are two important differences between the situations of the leaders and followers in a discipline. The first is that the leaders will have a really good understanding of all the stuff leading up to their innovation - the mathematics, other results in the field, the meaning of the jargon, and so on. The followers are, inevitably, not going to have such a thorough understanding of the background (they've got better things to do with their time). The second is that the motivations are likely to be different. The followers will want to fit new ideas into the mosaic of other things they know and the current concerns of their lives with as little effort as possible; the leaders, on the other hand, are likely to have a burning desire to progress their discipline in the direction they want to take it. These two factors mean that the best perspective for the followers may not be the same as for the leaders.
But is this possible? Are there alternative, simpler, or more appropriate, perspectives in many branches of knowledge? Well, yes, there are: difficult ideas have often spawned popular versions, or, as cynics would say, they have been dumbed down for the masses. But pop science is not serious science: if you want to use the ideas for real, or make breakthroughs yourself, the dumbed down, popular version will not do: you need the original ideas produced by the leaders, the experts themselves.
This is not what I am talking about here. What I am suggesting the is possibility of producing a simpler more appropriate version for the followers, but one that is as useful and powerful as the original expertise produced by the experts. Or, possibly, better.
I used to be a teacher in a university, several colleges and on short courses for business. As a teacher you try to explain your material as clearly as possible. But often, perhaps usually, I found myself thinking of alternative ideas which I thought were more appropriate. And I've been doing this for 40 years, publishing the occasional article on what I came up with (the first such article was published in 1978: there is a list of a few more here).
The area I thought about in most detail was statistics. There are three key innovations I would like to see promoted here. The first is computer simulation methods: instead of working out some complicated maths for lots of specific situations, you just do some simulation experiments on a computer so that you can, literally, see the answer and how it is derived (e.g. Bootstrap resampling ...). The second is jargon, which needs changing where it is misleading. The worst offender is the word "significant". This has a statistical meaning, and a meaning in everyday language which is completely different. This leads to massive, and entirely predictable and avoidable, problems. The third is to focus on ideas that are helpful as opposed to ideas which fit statistical orthodoxy - see for example Simple methods for estimating confidence levels ... .
Other areas I pondered include research methods as taught in universities (a lot of the jargon is best ignored: Brief notes on research methods and How to make research useful and trustworthy), decision analysis (The Pros and Cons of Using Pros and Cons for Multi-Criteria Evaluation and Decision Making), statistical quality control, mathematical notation in general, Bayes' theorem in statistics (see pages 18-22 of this article), and the maths of constant rates of growth or decline (traditionally dealt with by exponential functions, calculus and logarithms but this is quite unnecessary).
Did I act on these ideas and teach the simpler versions that I felt were more appropriate? Sometimes I did, but usually I didn't. I was paid to teach the standard story, and didn't feel I could go out on a limb and teach my own version - which was usually untested and might not work. That's what the students and the organisations I was working for expected. And, besides, the system has an inertia that makes it difficult to change just one bit. The term "significant", for example, might be, in my view and the view of many others, awful jargon describing an awful concept which promotes confusion and discourages useful analysis, but it is very widely used in the research literature and people do need to know what it means.
There were exceptions where I did follow my better judgment. Computer simulation methods in statistics are widely used so, on some courses, I did use these. And sometimes, as with research methods, the problem was that a lot of the standard material was just a waste of time and was best ignored so that we could focus on things that mattered. But even here, by not explaining the t-test, or emphasising the distinction between qualitative and quantitative methods, I was failing to meet the expectations of many colleagues and students.
But surely, you're probably still thinking, if there really is such an enormous untapped potential, people would be tapping into it already? Part of the reason why they aren't, or are to only a very limited extent, is that the forces which act against change are very powerful and go very deep. I was so deeply enmeshed in the assumptions of academic statistics that an obvious alternative to the concept of significance in statistics (Simple methods for estimating confidence levels ...) did not occur to me for 30 years after publishing an article critical of the concept, and the statistics journals I submitted my idea to rejected it often with a comment along the lines of "if this was a good idea the gurus of statistics would have thought of it".
As well as the inevitable conservatism of any cognitive framework there are three factors which are peculiar to the knowledge production industry: the peer review system, the lack of a market or responsive feedback system for evaluating ideas and theories, and the desire of the education system to preserve "standards" by keeping knowledge hard. I'll explain the problems with each of these in the next three paragraphs.
The peer review system is the way new academic knowledge is vetted and certified as credible. Articles are submitted to a journal in the appropriate field; the editor then sends it out to two or three peer (usually anonymous) reviewers - often people who have published in the same journal - who make suggestions for improving the article and advise the editor on whether it should be published. The fact that an article has been published in a peer reviewed journal is then taken as evidence of its credibility. This system has come in for a lot of criticism recently (e.g. in Nature): mistakes and inconsistencies are common, but one key issue is that the reviewers are peers: they are in the same discipline and likely to be subject to the same biases and preconceptions. Peer reviewers would seem unlikely to be sympathetic to the idea of fundamentally simplifying a discipline. I think some non-peer review would be a good idea as advocated in this article.
Mobile phones and word processors are relatively easy to use. You don't need a degree or a lot of training to use them. This is because if people couldn't use them, they wouldn't buy them, so manufacturers make sure their products are user-friendly. There are lots of efficient mechanisms (purchase decisions, reviews on the web, etc) for providing manufacturers with feedback to make sure their products are easy to use. The academic knowledge ecosystem lacks most of these feedback mechanisms. If some knowledge is a difficult to master, you need to enrol on a course, or try harder, or give up and accept you're too lazy or not clever enough. What does not happen is the knowledge producer getting a message along lines: "this is too complicated, please simplify or think again."
This is reinforced by the education system which has a strong vested interest in keeping things hard. There is an argument that the purpose of the education system isn't so much to learn things that are useful (everyone knows that a lot of what is learned is never used), but to "signal" to potential employers that you are an intelligent and hard-working person (an idea popularised by the book reviewed here). From this perspective difficult knowledge is likely to be better for differentiating the worthy from the less worthy students. And of course difficult knowledge enhances the status of teachers and means that they are more obviously necessary than they would be if knowledge were easier. Universities would lose most of their business if knowledge were easy to master: teaching and assessment would be much less necessary.
So ... I would like to propose that simplifying knowledge, and making it more appropriate for its purpose, is an idea that should be taken seriously. Otherwise knowledge will evolve by narrowly focused experts adding bits on and making it more and more complex until nobody really understands what it all means, and progress will eventually grind to a halt in an endless sea of technicalities.
This requires a fundamentally new mindset. First we need some serious creative effort devising new ways of looking at things, and then empirical research on what people find useful, but also simple and appealing. Perhaps knowledge should be viewed as art with aesthetic criteria taken seriously? Whatever we are trying to do - discover a theory of everything, cure diseases, prevent suffering or make people happier - simplicity is an important criterion for evaluating the knowledge that will best assist us.
Then we should make faster progress, more people will get to understand better, and we should make fewer silly mistakes.
This article is just a summary. There is more on this theme in the articles linked to this page.