Ok. A lot of things are very 'reducible' but information is lost. You can't extend back from the reduction to the original domain.
Reduce a computer's behavior to its hardware design, state of RAM, and physical laws. All those voltages make no sense until you come up with the idea of stored instructions, division of the bits into some kind of memory space, etc. You may say, you can predict the future of the RAM. And that's true. But if you can't read the messages the computer prints out, then you're still doing circuits, not software.
Is that reductionist approach providing valuable insight? YES! Is it the whole picture? No.
The largest of the finite simple groups (themselves objects of study as a means of classifying other, finite but non-simple groups, which can always be broken down into simple groups) is the Monster Group -- it has order 808017424794512875886459904961710757005754368000000000, and cannot be reduced to simpler "factors". It has a whole bunch of very interesting properties which thus can only be understood by analyzing the whole object in itself.
Now whether this applies to biology, I doubt, but it's good to know that limits do exist, even if we don't know exactly where they'll show up in practice.
That's not really true, otherwise every paper about it would be that many words long. The monster group can be "reduced" into its definition and its properties which can only be considered a few at a time. A person has a working memory of three to seven items.
I think that chemistry, physics, and mathematics, are engaged in a program of understanding their subject in terms of the sort of first principles that Descartes was after. Reduction of the subject to a set of simpler thoughts that are outside of it.
Biologists stand out because they have already given up on that idea. They may still seek to simplify complex things by refining principles of some kind, but it's a "whatever stories work best" approach. More Feyerabend, less Popper. Instead of axioms they have these patterns that one notices after failing to find axioms for a while.
Several different definitions are being bandied about. If you think of reduction as understanding a material system in terms of its components, biology is now reductionist, having abandoned vitalism.
Evolution is a theory of the origin of species via natural selection of heritable traits; evolution is not a theory of biogenesis, the origin of life itself.
Yeah, I almost wrote ‘nearly have a theory of everything’ for that reason, but decided it wasn’t worth the extra words. We have a few plausible outlines of how life started, and IMO it doesn’t really matter all that much which one(s) actually happened. When we ourselves are doing biogenesis, there’s no requirement that it has to happen the way it happened before. It would be interesting to know, though, so if in your estimation we don’t have a theory of everything because of that, I’m okay with that.
That's a fine counterexample to "theory of everything", and fertile ground for spirited debate. But I think it's a distinction thats relevant to <1% of the work that biologists do, so like... does it matter?
It would imply that when dealing with complex systems, models and conceptual frameworks are, at the very best, useful approximations. It would also imply that it is foolhardy to ignore phenomena simply because they are not comprehensible within your preferred framework. It does not imply biologists should give up.
> Biologists don't try to reason everything from first principles.
What do you mean? The biologists I've had the privilege of working with absolutely do try to. Obviously some work at a higher level of abstraction than others, but I've not met any who apply any magical thinking to the actual biological investigation. In particular (at least in my milieu), I have found that the typical biologist is more likely to consider quantum effects than the typical physicist. On the other hand (again, from my limited experience), biologists do tend to have some magical thinking about how statistics (and particularly hypothesis testing) works, but no one is perfect.
"Reductionist" is usually used as an insult. Many people engaged in intellectual pursuits believe that reductionism is not a useful approach to studying various topics. You may argue otherwise, but then you are on a slippery slope towards politics and culture wars.
I would not be so sure. There are many fields where reductionism was applied in practice and it yielded useful results, thanks to computers.
Examples that come to mind: statistical modelling (reduction to nonparametric models), protein folding (reduction to quantum chemistry), climate/weather prediction (reduction to fluid physics), human language translation (reduction to neural networks).
Reductionism is not that useful as a theory building tool, but reductionist approaches have a lot of practical value.
> protein folding (reduction to quantum chemistry),
I am not sure in what sense folding simulations are reducable to quantum chemistry. There are interesting 'hybrid' approaches where some (limited) quantum calculations are done for a small part of the structure - usually the active site I suppose - and the rest is done using more standard molecular mechanics/molecular dynamics approaches.
Perhaps things have progressed a lot since I worked in protein bioinformatics. As far as I know, even extremely short simulations at the quantum level were not possible for systems with more than a few atoms.
The context here was a claim that reducibility is usually a goal of intellectual pursuits. Which is empirically false, as there are many academic fields with a negative view of reductionism.
'Reductionist' can be an insult. It can also be an uncontroversial observation, a useful approach, or a legitimate objection to that approach.
If you're looking for insults, and declaring the whole conversation a "culture war" as soon as you think you found one, (a) you'll avoid plenty of assholes, but (b) in the end you will read whatever you want to read, not what the thoughtful people are actually writing.
Reduce a computer's behavior to its hardware design, state of RAM, and physical laws. All those voltages make no sense until you come up with the idea of stored instructions, division of the bits into some kind of memory space, etc. You may say, you can predict the future of the RAM. And that's true. But if you can't read the messages the computer prints out, then you're still doing circuits, not software.
Is that reductionist approach providing valuable insight? YES! Is it the whole picture? No.
This warning isn't new, and it's very mainstream. https://www.tkm.kit.edu/downloads/TKM1_2011_more_is_differen...