The memetics of science

I've become fascinated recently by the concept of memes and the dynamics of how they evolve (memetics). I'm particularly curious about what memetics might have to tell us about how science works.

Full disclosure here is that I'm very much an interested amateur at this point - there are people who have spent many years thinking deeply about memes and memetics, and I'm not one of them. But the basic concept is kind of beautiful and not very hard to grasp, and it does give us some insights into how science works.

We should start with some definitions. A meme is a unit of information (such as an idea or concept) that's copied from person to person. The idea of memetics is that memes are subject to an evolutionary process because they are copied, a range of variants exist, and they are subject to selection pressures (some memes spread more effectively than others). So what we then have is a way of thinking about the dynamics of how (scientific) ideas evolve and develop.

It strikes me that science in particular is a memetic process where we have one additional concept: we subject our memes to the selection pressure that they must be confirmed empirically. This is an important difference. Memetics per se does not require any given meme to be true; it just has to be good at propagating. This explains why rumours that are false but appealing can spread so readily. By adding the additional constraint of empirical confirmation, we are adapting memetics in order to learn about the universe.

Thinking in this way, we can define a list of general scientific processes in which we can be involved.

• Validating an existing meme or memeplex (empirically or via theoretical proof or computational analysis)
• Improving an existing meme or memeplex (someone had a good idea, then you're able to refine it)
• Making a new memeplex (a new combination of memes)
• Creating a new meme

There may well also be others that this interested amateur hasn't yet thought of :-)

One additional thought is that this also gives us some insight as to how important is it to fill your brain full of relevant memes, so that you've got more to work with.

Rational inference in science

One of the side-effects of working in science is that you end up thinking a lot about how we reason and learn. What rules do we choose in order to do this? Do we have a range of options? This is pretty important, as it underpins everything we do as scientists (or thinkers of any kind, come to that).

The term 'Rational Inference' is a good way to describe how we do this. It covers both logical deduction (for when we have definite facts: if A and B are true, then C must also be true) and induction (for when there is uncertainty: D and E are true probably, which implies that F is also likely to be true). Rational inference is how we reason in science, whether using experimental results, observations of the universe or even (the most obvious example) mathematics, and how we learn more about laws that describe how the world works.

The fascination for me is that we have a mathematical theory that tells us how rational inference must work - probability theory. If you're comfortable with a Bayesian perspective on the subject (and are therefore happy to allow probabilities to represent states of knowledge), then probability theory contains logical deduction as a special case when all probabilities are either zero (false) or one (true). It also extends this to allow for different degrees of plausibility (0 < probability < 1), giving us the capability to represent any state of knowledge from false, through 'maybe', to true.

This also gives us an answer to the question "am I using the right set of rules to make my inferences?". The physicist Richard Cox provided a proof (using only very general starting assumptions) that any consistent mathematical rules for handling degrees of plausibility must be those of probability theory (or equivalent to them). Meaning the answer to our question is "yes!" - and we realise we are using the only set of mathematical rules for rational inference that make any sense.

So we find ourselves with a uniquely correct approach to reasoning in science (and anywhere else), along with a set of mathematical rules to use. There are philosophical considerations along the way (such as whether to adopt a Bayesian or Frequentist viewpoint) but if you're comfortable with the idea that your degrees of plausibility can include states of knowledge, you've got a unique set of mathematical rules that tell you how to make rational inferences about the world.