I am convinced that most people become scientists not for the big overarching aims of science, but for personal reasons. Because I love the outdoors, plants, working with data, and a very flexible independent job would be four of mine. Others love working with their hands, a certain form of status, just love their species, etc. But none of these are the overarching goals of science. And even if I don’t think overarching goals are why we get into science, I do think most scientists are bought into the overarching goals of science as well. Certainly I think most scientists see themselves as truth-seekers. Can we be more specific about the overarching goals of science? I am going to argue that there are three major overarching goals of science:
- Understanding – the answers to why questions. How does the world work. Why is the sky blue? Why are there so many species?
- Prediction – what will happen? Neptune must exist because of the weird deviations in the orbit of Saturn from expectations. How many species would I expect to find on an island of size X with climate Y?
- Description – the answers to what questions? What is the nature of the world around us? What is the gravitational constant? How many species are there in the world?
Of course most scientists buy into all three of these goals. But I believe that most scientists are attached to some of these goals more than others. So before reflecting on this further, if forced to pick only one goal, which one do you think most motivates your work as a scientist?
Now that I’ve forced you to commit, here a few thoughts:
- Obviously all three are important – I’m not down with X is the most important goal point of view. I believe science benefits from diverse methods and approaches. So science benefits from having people pursue all three of these goals. I truly believe which goal calls to you is a personal choice.
- Some fields of science (or at least some problems) don’t have to choose. Newton’s three laws blew everybody away because it simultaneously understood and predicted from hot-off-the-press descriptions from Brahe. But I would submit in a multicausal field like ecology, we rarely get to do all three at the same time. When 20 different forces are operating on the same system, understanding in detail even with quantitative models how one of those forces works, is not necessarily going to help you too much in being predictive about the full complex system in the field (where you don’t even know if that force is important or not). So I submit ecologists usually have to pick.
- It is quite common to hear just understanding vs prediction as goals. I feel like description has kind of faded into the background, although the strong movement for re-valuing natural history could be interpreted as an argument for description. I also think description sounds “qualitative” and therefore soft. But description can be highly quantitative which I think is one of the main hallmarks of science. And there are physicists whose life’s work is to put a few more decimal places on the gravitational constant or the charge of an electron. What would ecology be like if we pursued these questions more strongly? Would we still have order of magnitude error bars around the number of species on the planet?
- It is tempting to put these in a sequential order. First you describe. Then you understand. Then you predict. Except I think this is completely wrong. As noted, already, I don’t think understanding necessarily leads to prediction. So often in ecology correlative methods are more predictive than more mechanistic methods. And some forms of description only even make sense after we have clear understanding and models.
- I think it is fairly easy to define success of prediction (e.g. RMSE of predictions on data not used to calibrate the model) and description (how many significant digits can we put on a well defined quantity to measure). Defining success at understanding is much more slippery. I think some people would quickly say it is the same as mechanism (to which I might agree). But that is just passing the problem to another word. There is not even clear agreement among practicing ecologists (nor among philosophers) on what mechanism is (e.g. see Connolly et al 2017 vs McGill and Potochnik 2017 or McGill & Nekola 2010). I realize my views of mechanism are heterodox and not agreed with by most readers (or even co-bloggers). But the traditionalists have yet to give a really precise definition either. And to return to Newton, did he really understand gravity? or just give a really precise formula to describe gravity that is also predictive?
- With co-authors I have argued elsewhere that the best demonstration of understanding is prediction. If you cannot accurately predict, just how well do you understand a system? And that is a standard that for example physicists or meteorologists have successfully risen to meet. Conversely, it is popular among ecologists to claim that correlative methods cannot extrapolate – only mechanistic understanding can successfully extrapolate (Dunham & Beaupre 1998). But as I hinted above, I really think ecology would be best off if the advocates of these two goals recognized that in a multicausal world, these two goals often have little to do with each other and declared a truce.
- Nowhere did I mention models. Models are clearly an important part of science (see the McGill & Potochnik link above). But I would argue they are orthogonal to this discussion. Models can be present or absent in all three of the goals. Arguably all three are better when models are present most of the time. But that is a separate question from this one.
- And for the 1% of you who are wondering what I think. I started my career as a strong understanding first person. By the end of my graduate career I had evolved into what I would have said was a prediction first view. But I would now say that I am a description first, prediction second person and that that is what I’ve actually done most of my career. I have seen so many theories that seem detached from reality. If I want to know what really happens in the real world (and here I am giving primacy to a scientific goal that I recognize is personal and not global), my best bet is to go measure it.
What do you think? Am I missing a major goal? Do I have one too many goals? What big goal motivates you and why?