Kate Clancy blogs on SciAm about a paper in PLoS Computational Biology that supposes that menopause evolved because dudes like their chicks young: "I can’t believe my feminism blinded me to such a raw, important truth. I now realize that, throughout the hominin lineage, the women were just sitting silently to the side, quivering, reproducing… READ THE REST
About time! I saw the original article and downloaded it for fun. You should see the citations, or rather, the lack of citations for biologists. Sarah Hrdy looked at all this over a decade ago. There are evolutionary reasons for menopause, and they don’t involve the need for hawt gals. Hint - menopause also exists in elephants, another species where offspring have long development and rely on post-menopausal elephants to help with successful calf rearing.
At least, if memory serves it was elephants, and one other species that escapes me. Hey, it’s been a decade, but I’m still ahead of other researchers. Less Freud, more Darwin.
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I think this is a huge problem with a lot of the work being done in mathematical biology. Models are cooked up and simulated and frequently the goal is to see a biological phenomenon emerge in some cartoonish way. I’m really not sure who is supposed to benefit from some of these computational studies. Often the model is too simplistic to expect that it will give any quantitative predictions; and it is frequently unclear if there is validity to any conceptual understanding that can be gained (as demonstrated here).
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As its name suggests, the “mate choice” model assumes that males can only fertilise a single female, so if a male is having sex with a younger female then he is no longer available to fertilise older ones.
Even in a pair-bonding situation, if all the younger females are paired up and only older ones are left, a male will prefer to remain abstinent and deny them his essence.
This is where the theory breaks down for me.
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As the recent post on junk science pointed out, it’s amazing how insistently wrong folks with an engineering mindset can be when they try to apply it outside their own field.
It seems to me that “computational biology” is, by definition, a case of trying to apply engineering techniques outside their intended field.
(I’m a computer geek myself – I make my living biting headers off live checksums – but I know the limits of my biological knowledge.)
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Orcas
It’s a problem in all sciences. You can get a computer model to tell you anything. But they can certainly be useful if employed properly.
So in response, scientists treat computer models with inherent distrust and hold studies that employ them to a much higher standard than anything else. In geology, my field, there’s a feud of sorts between computer modelers and everyone else. The result tends to be the best science possible with the tools and data we have available, and ridiculous stuff is ridiculed (if it somehow manages to get published).
Er, no. Biology isn’t just about looking at cute pandas frolicking about, but is as computationally demanding as any other science these days. I’m not a big fan of “evolutionary psychology” like this paper, but as a genomicist, I know that no meaningful discovery can be made in my field without serious application of algorithms and statistics. Computational biology and the related field of biostatistics are two of the fastest growing fields for this reason.
Computational biology and the related field of biostatistics are two
of the fastest growing fields for this reason.
Point fully granted, actually. Cynicism got the better of me.
Would it not make a much stronger hypothesis to suppose that the male preference for younger women evolved because of menopause?
Even if one believed that their model applied to reality, they simply trade the question of “why menopause?” for question of “why do men have a preference for younger women?”
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I’m not sure I can go along with this. The paper provides an additional perspective on menopause in the context of a number of alternative, established hypotheses. The first table in the paper provides a listing of such alternatives, which is more exhaustive than the one provided in the takedown. The authors give the impression of being aware of the context of their work. Maybe they have missed one or two possibilities, but papers are rarely perfect (and sometimes cannot be due to space constraints). At no point do the authors claim that these alternatives are wrong.
I seriously doubt that the researchers were primarily motivated by their desire to put men in the center of all things. Is it possible that this played a role, maybe on the subconscious level? Yes. Is it useful to always assume the worst possible intention behind a new piece of research? No.
It’s a paper. It suggests an explanation for something the authors deem to be previously underexplained. Maybe they’re wrong, and maybe their models are simplistic, in which case everyone is welcome to write up a detailed critique and submit it to a peer-reviewed journal. Just science in the process of happening, nothing to see here, move along.
For all anyone here knows they might end up being right. Hopefully, more research will tell us. Being outraged will bring noone closer to the truth.
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Science doesn’t exist in a vacuum.
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I still think we should focus on the technical content (which may well be insufficient, I’m not an expert), rather than on the assumed intentions of the authors. The linked “takedown” doesn’t do this. It is outraged about the hypothesis itself rather than about the way the authors go about evaluating it.
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Your repeated use of the word outraged is sending me into hysterics.
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Call me hypersensitive, but I don’t like your implying that I’m sexist (if that’s what you are doing). I find everyone is served best in these emotionally charged waters by acting under the assumption of wiki-style good faith.
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It exists in a frictionless sphere.
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“emotionally charged waters”
Each time you comment, my eyes roll back further in my head.
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I’m in applied math myself.
I agree that this is a problem with all sciences; but I feel that in most fields (certainly in anything physics based) one starts with equations of motion or whatever for some system and the question is how do I implement this computationally. That might be really hard like in the case of geology or climate modeling, but at least you have a fairly good idea of what the system is you’re trying to simulate (granted modulo some issues like cloud formation in climate science etc.)
In mathematical biology a lot of the time the process is the inverse of this. Some phenomenon is observed (in this case menopause), and then people try to cook up some model that reproduces it. The problem is that this inversion procedure is not well posed. There are many different models you can formulate that will give you the phenomenon in question.
It might be the case that the above procedure is the only way to try to study phenomena in complex biological systems. But in this case I think one has to be really aware of the fact that reproducing the phenomenon does not mean one captured some real truth in one’s model.
And my other problem with a lot of the work in this field is what I stated before. Presumably the models in geology you’re referring to make some quantitative predictions? They may be hard do verify experimentally but still. The authors of agent based models in biology like this often don’t even claim any quantitative predictions can be made with the model; the game is simply lets see if these rules for the agents make them do something interesting. Again, if a tight connection to the real world dynamics is not made, and one can’t actually get quantitative predictions from the model than what’s the point? If they analyzed the model analytically maybe at least there would be interesting math, but it seems like the model was just simulated. I really dont understand who benefits from stuff like this.
That was really long. Sorry.
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