# on randomness – pt 1

## on randomness: ecology

The kind ecology that I do and am interested in involves dealing with a lot of randomness*. There’s all the random noise in the observational data I work with and random methods I use to find interesting patterns in it.

Recently I’ve been thinking a lot about the fundamental basis of this randomness. Whether it’s just stochasticity arising from a deterministic system or if there’s actually some source of *true* randomness. There’s quite a lot I’d like to cover, so I’ll string this out over a number of posts.

As I see it ecology is top of the pile of natural sciences when it comes to the amount of randomness we have to deal with. You could order the sciences up like in this XKCD:

except that instead of purity I’d rank them by the amount of randomness – or the noise to signal ratio – inherent in the systems studied in each discipline. I think of all the randomness in ecology as bubbling up from mathematics and physics through layers of increasing chemical and biological complexity into the big old mess that is ecological data.

Our aim as ecologists is to pick out the rules that drive these complex systems. We need simple rules so that we can understand them and apply them to other situations. So we spend most of our time sifting through noisy data looking for patterns.

The usual approach is to come up with a model that explains as much as possible of what we observe, whilst making the least assumptions. We can work out what we would expect from the model and anything that is unexplained (so long as there’s no clear pattern in it) we call noise.

This noise that we shove to one side bothers me. Is it just stuff that we haven’t got round to explaining yet? Or is some of it inexplicable, real randomness that we won’t ever, can’t ever, pin down?

The next post in this series will be on determinism, then I’ll do stuff on quantum uncertainty and potential sources of true randomness in mathematics. I’ll try and tie it all up with a post full of philosophical ramblings about whether any of this is important.

*I’m using the term randomness to mean noise or observed apparent randomness, I’ll go with stochasticity and ‘true randomness’ for the other interpretations

It is a good question, whether you could rank disciplines according to their noise to signal ratio. It seems quite intuitive, however, it reminds me of a study I have heard about on a youtube lecture, which concluded, that this ratio might really not be so different.

I am looking foward to read your next parts 🙂

forgot, since it is a long lecture, the interesting part begins at 1h20min

well, i obv did not want to say he rest would not be ineresing 🙂 – just there is the part about the study.

Thanks Thomas, that looks like a really interesting lecture, will try and watch the whole thing at some point!

I had a think about this recently and I don’t know how you could test this gradient of randomness idea empirically.

The method they use in this study is to compare the size of error bars in papers published in different fields. Surely the people working in those fields are all subject to a trade-off between expending resources on the study and gaining enough data (or working in a simpler sub region of their field) to get sufficient statistical power to test hypotheses. Therefore the size of error bars they find to be common across fields probably just reflects a statistical error rate deemed to be acceptable for publication in that field (i.e. significance = p<0.05 for most!).

That said, a fractal pattern of randomness is, I guess, equally as plausible as the linear pattern I suggest above!

The upshot of either theory is that the ultimate source of randomness stems ultimately from physics or mathematics (or can be described physically or mathematically, depending on your semantic preference). Neither answer whether this is true randomness or really complex determinism which is what really interests me.

Thanks for reading, I'll try and get the next post up soon!