Study predicts imminent irreversible planetary collapse
sfu.ca
sfu.ca
Imminent Death Of The Net Predicted!: prov.
[Usenet] Since Usenet first got off the ground in 1980--81, it
has grown exponentially, approximately doubling in size every
year. On the other hand, most people feel the signal-to-noise
ratio of Usenet has dropped steadily. These trends led, as far
back as mid-1983, to predictions of the imminent collapse (or
death) of the net. Ten years and numerous doublings later,
enough of these gloomy prognostications have been confounded
that the phrase “Imminent Death Of The Net Predicted!” has
become a running joke, hauled out any time someone grumbles
about the S/N ratio or the huge and steadily increasing
volume, or the possible loss of a key node or link, or the
potential for lawsuits when ignoramuses post copyrighted
material, etc., etc., etc
PS: Obviously I don't think we should just nuke everything because we're /eventually/ going to die, but I find the general fear and surprise at the fact that we as a species could cease to exist interesting. Like we're entitled to live forever because the Universe is ours or something.
I mean... why the hell not?
Edit: Here's the actual paper: http://www.nature.com/nature/journal/v486/n7401/full/nature1...
I'll reserve my full judgement until I've read it, but I don't have high hopes. It seems quite odd to invent an entirely new scientific discipline in one fell swoop along with making such a bold prediction.
PPS: After reading the paper my opinion is that this is far more pseudoscience than not, by a wide margin. There are some points worthy of further study (biodiversity studies, effects of the impact of human activity comprising a large and growing part of the world's energy budget, changes in ecosystems due to fragmentation due to human activity, etc.) but this paper steams by those topics at a thousand miles an hour. There is no theory here, only the vaguest outlines of one. Even the core terms (state, state shift, forcings) are vauguely and generically defined, if at all. There is no model, no predictions, and no evidence.
Flatly put the ideas put forth here are non-falsifiable. At its simplest "state shift theory" can be boiled down to "change causes change, sometimes permanently". Figure 2 is a graph which plots "global ecological state" against "global forcing". There are no numbers and neither term is defined either quantitatively or qualitatively. As a side note the essay references the now debunked theory of Mt Kilimanjaro's glaciers melting due to global warming, despite the lack of evidence of local temperature increases there (the glaciers have been sublimating away and snowfall changes have failed to replace the lost material.)
Nature should be ashamed to have printed this, even in an editorial context. It is so lacking in hard scientific rigor and so generic and amenable to any interpretation (there is no evidence presented whether "state change" could be beneficial or detrimental to humans or to Earth's species) that it doesn't belong within a thousand meters of a journal as prestigious as Nature. There is even a chance, though admittedly slim, that this could be a hoax, the work of someone who wishes to discredit the scientific establishment of today and its seemingly infinite ability to bend its standards and tarnish its history to print anything so long as the conclusions are politically correct (human population reduction, switching to "green" energy and "sustainable" agriculture, etc.)
These topics are too important for us to abandon scientific rigor in our attempt to understand our world and how it works and will change. We cannot afford to slip into an era of superstition and ignorance where conclusions are assumed and evidence is irrelevant.
The thing is, if we are approaching a critical transition then it is very hard to say what the precise outcome would be as the whole point of these events is that they are highly chaotic. Is a bit like asking where a particular molecule of water is going to be after boiling a pan half dry.
What would the actual negative consequences of such a transition be? Would they outweigh the positive consequences? Would they outweigh the cost of avoidance measures, taking into account the probability that those avoidance measures won't work? The avoidance measures here are extreme even by extreme standards - we're talking a comparable cost to a global thermonuclear war (large decrease in population, large semi-permanent decrease in global GDP). We need numbers here, guys. Numbers.
They're worried that ecosystems (those networks of interacting species) will collapse (meaning the ecosystem dynamics change and the vast majority of the species who rely on and compose the ecosystem go extinct).
BOX 1: Past planetary-scale critical transitions and state shifts
Last glacial–interglacial transition18,24. The critical transition was a
rapid warm–cold–warm fluctuation in climate between 14,300 and
11,000 yr ago, and the most pronounced biotic changes occurred
between 12,900 and 11,300 yr ago24,27,30,54.
The major biotic changes were the extinction of about half of the
species of large-bodied mammals, several species of large birds and
reptiles, and a few species of small animals30; a significant decrease in
local and regional biodiversity as geographic ranges shifted
individualistically, which also resulted in novel species
assemblages37,49,53,54; and a global increase in human biomass and
spread of humans to all continents27.
The pre-transition global state was a glacial stage that lasted about
100,000 yr and the post-transition global state is an interglacial that
Earth has been in for approximately 11,000 yr. The global forcings
were orbitally induced, cyclic variations in solar insolation that caused
rapid global warming. Direct and indirect of effects of humans
probably contributed to extinctions of megafauna and subsequent
ecological restructuring.
It's like arguing that a CS paper doesn't define what "compiler" means; there is a basic level of literacy expected when reading scientific papers.
I'm guessing this really means the land surface of the Earth, rather than the Earth's surface as a whole, as ~70% of the Earth's surface is water. Without access to the paper itself, I can't say whether or not this is an issue with the "media release" or the research itself, of course.
" It is still unknown, however, what percentage of Earth’s ecosystems actually have to be transformed to new states by the direct action of humans for rapid state changes to be triggered in remaining ‘natural’ systems. That percentage may be knowable only in retrospect, but, judging from landscape-scale observations and simulations66, 67, 68, 69, 70, it can reasonably be expected to be as low as 50% (ref. 68), or even lower if the interaction effects of many local ecosystem transformations cause sufficiently large global-scale forcings to emerge."
or in other words, "fuck, I dunno, half?"
This sort of study is not science and only as good as the quality of their assumptions. I'm glad the authors and all participants got their names in a prestigious journal and I hope it helps their careers.
But the usual cure people propose, a massive expansion of government, is worse than the disease. It will ultimately lead to more wars and an end to dealing with threats to our existence.
But doing nothing is not viable either. We need to get control of the planet. We simply cannot just let the next ice age or global warming occur.
This sort of thing is the ultimate "tragedy of the commons". And some of those most familiar with that concept seem the least likely to apply it the effects of technology.
People seem to think nothing could ever go wrong with such a concept. But history tells another story about government. We've had some very bad totalitarian governments. I'd rather not trust any single organization that will never face any competition.
I'm not saying its a bad idea to coordinate with other countries on specific issues. That's what treaties are for. Even there, some pretty bad ideas can be agreed upon. At least countries can leave a treaty that proves to be a disaster.
Neither would I. That would be silly.
Can you tell me what government is? In my experience, people who are afraid of governments becoming totalitarian cannot seem to grasp what the government itself is. Would you be an exception to this trend?
It's the end of the world as we know it. And I feel fine.
Link to actual paper (may or may not work outside the ivory tower I currently occupy, someone please check and let me know): http://www.nature.com/nature/journal/v486/n7401/full/nature1...
edit: Apparently it doesn't, but try this one (pdf) http://www.stanford.edu/group/hadlylab/pdfs/Barnoskyetal2012...
Current models of the entire planetary ecosystem are admittedly quite primitive, but they often have proven to be predictive. That which is predictive and reproduce-able is science, by my reckoning. And, this paper is drawing on expertise across an extremely broad spectrum of real research, not merely models or educated guesstimation.
What have they predicted?
I mean, I have a model for predicting the result of coin flips which is often predictive. It's called "heads".
But if you try to make a model based only on your observations, without also being able to experiment, then that's not science, that's tautology.
Your model will do whatever you want, and you have no way of knowing if it has anything to do with the real world because you can not run multiple experiments. In some cases if you wait long enough you may randomly get enough variation in the real world to be confident in your model (for example stellar evolution).
But using a model to predict something you have never seen in the real world? That's not science, that's speculation.
In terms of the article, the theory of climate is pretty well understood at this stage, and the models are sound at the macro scale. The hard parts are now in factoring in relatively small changes to make the numbers line up even better, and working through the implications of their predictions... which is what the article seems to be doing.
Correct - they can't be used for prediction, and therefor they have no value at all (for prediction - they are useful for validation).
> Of course they sometimes don't line up with the real world
Then you improve them, but you don't try to predict from them. By improving them you increase your knowledge, but you don't predict from them because the only thing that comes out of a model is what you put into it. You learn nothing about a real world from a model. You learn about your understanding of the real world from the model. (They better you know the real world the better you model - the model teach you about your own knowledge. It has zero value in teaching you about the world since nothing can come out of it that you didn't put into it.)
> why do you think there are a metric shit-ton of scientists in Antarctica?
To do research about the real world? Is this supposed to be a big revelation? Not sure waht you were trying to imply here.
> The hard parts are now in factoring in relatively small changes to make the numbers line up even better
Well obviously. The details are always the hard part - the details is also where all the interesting stuff happens. You can make the best model in the world, and then miss the one tiny detail that only happens outside the domain of your model, and all the predictions are worthless.
Those formulas are not models! They are exact mathematical representations of a phenomena!
A model by it's nature can not include everything, they include everything possible of course, but the world is too complex for them to include everything, so they must estimate. If you have a feedback loop with the real world you can tune your model to make it useful, but you can never get out of it anything you did not put in, since it's impossible to include everything.
If a model did include everything then of course it would work perfectly. But it's not possible to do that in the real world.
By your argument, V=IR must then be a "model" and is useless, with no predictive power, etc, etc. In practice, the only difference between those formulae and a more complex model is one of degree.
You think V=IR describes a resistor, it does not. It describes electricity. So it's not surprising that when applied to a resistor it doesn't work. You are trying to model a resistor, but you are not including everything in your model, so it fails.
Which is exactly what I'm warning about.
> By your argument, V=IR must then be a "model" and is useless, with no predictive power
V=IR is not a model. Using it to describe a physical resistor is a model.
Suppose I never touched a real resistor in my life, and the only information I had was an understanding based on some other law that V=IR. Now I want to use that information to predict what a resistor will do. I will fail - among other things it doesn't take into account inductance or capacitance.
Which is why it is very very important to know what your equation describes. You thought that V=IR completely describes a physical resistor, when it does not.
Now you know why it's incredibly dangerous to try to learn anything from a model. If even something as simple as Ohm's law can be confusing, imagine how many error a model with hundreds of equations has.
V=IR is a mathematical model of a resistor, useful under most circumstances except pathological ones. Can the model be improved? In most cases yes, but then it loses some value due to complexity.
This is why there are scientists in Antarctica: they're testing the model, gathering new data to see how well the models hold up. But for some reason you're acting as if those poor scientists haven't realised this yet.
If you want to continue to argue the point feel free, but you're disagreeing with every scientist and engineer on the planet. Try looking at http://en.wikipedia.org/wiki/Mathematical_model - what do you see? Ooh - equations.
"essentially, all models are wrong, but some are useful" -- George E. P. BoxIt's the basis for a model of a resistor, but it's an exact equation of resistance. There is a difference.
> they're testing the model, gathering new data to see how well the models hold up.
No, they are not testing the model, they are trying to improve the model by recording what it did in the past.
To test the model you make a prediction of what it will do in the future, then wait and see if it happens the way your model shows. It can't be done for climate though since it would take decades. They do try to test it by giving it old data and seeing if it predicts current conditions - which is great, but does not help in predicting the future since the input will be different this time.
> you're acting as if those poor scientists haven't realised this yet.
They most definitely do realize how poor the results are (just see how many weasel words they put in the paper linked to this topic), they just have no better options.
> try looking at http://en.wikipedia.org/wiki/Mathematical_model - what do you see? Ooh - equations.
Do you really not understand the difference between a model and a fundamental equation? A model does not have perfect input, and a model may not include all things that can affect the output.
An physical law (an equation usually), is exact. It might not apply to real physical objects (like the ideal gas law), but that doesn't make the equation a model - the equation is correct for where it's used, it just doesn't cover everything.
Excellent quote BTW, but don't take from that that everything is a model. The real world is not a model - if I test something and it does something in the real world, I have real information. In a model I only have the information I put in it, and it's simply impossible for me to put in everything.
I found a great example for you: http://en.wikipedia.org/wiki/Castle_Bravo#Cause_of_high_yiel...
They made a model - but they missed something. Good thing they were able to test it, and see what they missed. But if you don't have the ability to test? (Like climate.) Then your model is useless.
A model is useful only if you can test it. The model will help you know what to test, the model will help you see if you understand the topic. But a model you can not test is useless.
(Just to repeat myself: I'm not arguing about models, I'm arguing about models that can't be tested.)
Also look at this image further down the page: http://www.skepticalscience.com/pics/sea_ice_prediction.jpg
The glacier model is wrong, but wrong in the wrong direction (Actually I think that's combined results from multiple models, but anyway...). How do they know? Because there are climatologists out on the ice, or in satellites taking measurements-1. Testing the model - you know, all that stuff that is supposedly impossible to do.
1- there are even high resolution GPS modules in Greenland which can estimate ice loss by how far up the crust lifts (as well as detecting plate tectonics).
Science often models things it has never seen in the real world. Sometimes, when it becomes possible to make a measurement, the model is found to be accurate. Like in the classic xkcd "Science. It Works, Bitches." cartoon about the microwave background radiation. - http://xkcd.com/54/
Models do not just do whatever you want. Many unexpected behaviours turn up in models and some of them can be almost impossible to know the future behaviour of in advance of running them, even when you know all the input states.
In science, models are often what you use when the science isn't known, as you can use them as a guide to pick up on interesting things to go and look at. Engineering is usually where you use models when the science is known. [edit] And economics is where you use models when the science isn't known, and then you worship them and hope that money falls out.
That's not true. It's a fallacy that the first step in the scientific method is formulating a hypothesis. It's completely unnecessary. The first step is "let's see what happens". You do not need any speculation or hypothesis first. That comes later - after you have collected your data then you try to understand and predict.
> Sometimes, when it becomes possible to make a measurement, the model is found to be accurate.
And for more often the model is wrong. But you have no idea if it's wrong or right if you can not test the real world.
> microwave background radiation
That's not a model, and the fact the you think it is makes we wonder. That's an exact mathematical representation of the phenomena. A model is imprecise, it includes as many parameters as possible, but by necessity can not include everything since the world is too complex.
> Models do not just do whatever you want. Many unexpected behaviours turn up in models and some of them can be almost impossible to know the future behaviour of in advance of running them, even when you know all the input states.
That's called Chaos. And the interesting thing about Chaos is that tiny changes in the input (for example what decimal precision you use) cause large changes in the output. If your model is chaotic then it's utterly useless for any conclusions whatsoever because it's completely impossible for you to enter the input with the same level of precision as the real world. Chaos is fun to look at, but pretty useless for prediction.
There's a second thing possible called emergent behavior. But that too can not be modeled without understanding the real world first. What you do is keep changing the model till it matches the real world, then pull out the seemingly simple rules that cause complex behavior. But the model will fail as soon as you go outside the domain it was built in. Just because something acts the same way every time in the limited circumstances you tried does not mean it will keep doing so forever. That's a common extrapolation fallacy.
So again, useless for prediction since prediction by definition puts you in a circumstance you have not yet seen.
> In science, models are often what you use when the science isn't known, as you can use them as a guide to pick up on interesting things to go and look at.
Operative word: To go and look at. Not to draw conclusions from. Engineering uses models to validate the assumptions, Science uses models to verify understanding. In no field are (should) models be used to draw conclusions.
I didn't say that you shouldn't test your model, was just pointing out that you can model something based on an incomplete understanding and it can still be considered part of the scientific process before you have gone and checked your results. It can even be science even when there is currently no known way of checking the results, as long as you can point to a reasonable path that might lead you there.
Chaotic models are used in prediction. Regularly. Computational fluid dynamics wouldn't exist as a discipline without this.
And I never claimed that you should draw your final conclusions from a model, just that they can be a fantastically useful tool for exploring the unknown.
They can also be a trap if you trust the model more than the data, but that isn't the fault of modelling.
"A model...[is] really much more of a metaphor, an attempt to find an analogy between something you want to understand and something to really do understand, either heuristically or by a theory."
from http://www.econtalk.org/archives/2012/03/derman_on_theor.htm... - very good interview.
Also, your coin flip is not a predictive model any more than claiming that using the same numbers every week on the lottery is a predictive model. For one thing, it makes no attempt to model anything, and for another, it has no attempt to be predictive, it just attempts to be right nearly half the time (edge), through understanding of the likely odds, which is a different thing altogether.
Therefore, the plausibility of a future planetary state shift seems high, even though considerable uncertainty remains about whether it is inevitable and, if so, how far in the future it may be.
don't seem much more useful than my horoscope for today:
Today you may be wondering just how much you should get involved in a certain conflict between family members or in your career. There's a lot of pressure on you to find an answer to this problem. But all you have to do is not take sides and everything should work out fine. You may finally realize that it's probably best to just let things happen for once.
I mean, what would it mean in real-world terms for either of those to be wrong?
The gist as far as I can tell is that complex systems behave in specific ways when changing from one state to another (eg. "an increase in variance in ... fluctuations"), and that the climate currently seems to be undergoing similar fluctuations. It doesn't seem to be particularly vague; I noticed references to things like the inverse power law of species body mass, so they're not just pulling numbers out of their ass.
In real world terms? Mass extinctions most likely, just like the last major state shift.
I am not sure the current model has predictive power. Predictions in science have to be specific enough to yield testable result. However, predictions of this sort have been vague enough to be inclusive of any anomalies. And this characteristic is a common hallmark of non-science.
I am not saying that climatology is non-science. However, I do think that there are claims being made that are not entirely scientific.
Frog A goes extinct, frog A just happens to eat beetle B.
Without (as much) predation pressure, beetle B goes completely fucking nuts and eats everything in sight, including most of the forests in the area, crops, etc.
Everything in the area starves, the ecosystem collapses, and you're left with scrub, beetles and widespread erosion until something else can colonise the area and start a new ecosystem.
It says that local ecosystems are known to go through critical transitions, and that what's make these transitions remarkable is that the tipping point is so hard to identify or predict. They are extrapolating this and saying that a global tipping point may exist as well. That's it.
You are right, it's incredibly vague and there isn't any awesome undeniable evidence. That's the whole point of the paper. "Hey guys, we might be screwing up our shit more than we think, we need to start taking more measurements" is basically the entire gist of the paper.
And then therefore concludes that "governments undertake five actions immediately", the first example being "Society globally has to collectively decide that we need to drastically lower our population very quickly" - so mankind must enter into a global depopulation program. We all know exactly what that is. It doesn't look like scientific claims at all, it looks like irrational hysteria with an agenda.
Please save your accusations of irrational, hysterical agendas for them that advocates hysteria and the abandonment of reason.
> “Society globally has to collectively decide that we need to drastically lower our population very quickly. More of us need to move to optimal areas at higher density and let parts of the planet recover. Folks like us have to be forced to be materially poorer, at least in the short term. We also need to invest a lot more in creating technologies to produce and distribute food without eating up more land and wild species. It’s a very tall order.”
Just look at Figure 2 (here: http://i.imgur.com/Zi3uB.png). What is the definition of either of the axes? What is the basis for the relationship between any of the factors portrayed. It is favorable even to call this figure a schematic. If a student tried to slip such a figure into a research paper in high school or college they would certainly draw the ire of their professor, and justifiably so.
There is nothing falsifiable about this "theory", it is not science in any reasonable sense.
This is the age of irrationality.
A few species on a thin smear of biosphere on the outside of the planet might be in trouble - but the planet is perfectly safe until the sun expands.
I mean, you've already dispensed with the anthropocentric definition of "safe" that implies "for human civilization". So why stop at the anthropocentric definition of "planet"?
Why not take comfort in the fact that matter can neither be created nor destroyed, and as such, even being baked by the sun or destroyed in a collision of galaxies or pulled into a star or black hole will leave the energy that comprises "Earth" quite safe and sound?
If we want to imply that man is insignificant, surely the fact that our planet itself is insignificant would only underscore that point.
For such an astute student of language, with an eye toward practicality over formality, I don't know how he could see a difference between his complaint that the colloquial 'save the planet' implied rather than explicitly expressed a human point of reference and his many rants against unnecessarily precise techno-jargon. (see: PTSD, pre-boarding, etc)
The only useful value in highlighting that 'save the planet' implies 'so we can still live here', is to draw attention to how the knee-jerk response to such slogans and policy suggestions [1] is the height of arrogance/ignorance.
But rather than exposing the absurdity of that knee-jerk response, his little rant has served as ammunition for that response, shifting the expectation of evidence of lasting harm to an intellectual game of pointless abstraction. [2]
As such it started, and continues, to bother me.
So, again, if you find any legitimate comfort in the idea of the planet continuing on without us (As Carlin may well have) there's no good reason to stop at that level of abstraction.
And if you just want to complain about colloquial language, generally speaking, that pursuit winds up on the receiving end of the bulk of Carlin's analysis of language.
[1] In short: dismissing any potential for a legitimate concern about defending life as we know it by casting conservationist policies as the work of extremists or dismissing any potential for damage by humans by suggesting that we're powerless to truly harm nature.
[2] E.g. "We may turn America's breadbasket into a dust bowl - but after we all starve to death, things will grow again!" As if that matters to the conservationist or their opponent, being as they would both have starved to death.