The Misbehaving Universe

[CraigS]

Originally Posted by avandonk

It is simply by asking better questions that progress is made in our finite knowledge.

Elsewhere on the net, I see so often, questions being suppressed about complexity models … mostly, I see no proper explanations, even where the questions are allowed.

I can only conclude complexity models are yet to gather the evidence and even when this happens, 'predictions' are still questionable … unlike your example of a plane at altitude, which was tested many times over in variants of controlled conditions before I boarded it. It is only this which helps me to minimise my instinctive concerns.

Do we impose our need for determinism on top of that which may not be determinable in the first place ?

Originally Posted by avandonk

Godel showed we can never know all without paradoxes that cannot be disproved or proved. It is this indeterminism that makes our Universe so complex. Determinism leads inexorably to simplicity. Ask any fundie.

I prefer to live in a very complex Universe even if I do not fully understand it.

I'm with you in principle on that one … with the exception that my preferences don't really matter … the evidence is all around us .. if only we'd look at it ! ??

Cheers

[avandonk]

As someone that has worked in science for forty years prepare to be shocked!
Just watch this video and if you understand all the implications you understand.

http://www.youtube.com/watch?v=XVBEw...layer_embedded


Bert

[renormalised (Carl)]

Originally Posted by avandonk

As someone that has worked in science for forty years prepare to be shocked!
Just watch this video and if you understand all the implications you understand.

http://www.youtube.com/watch?v=XVBEw...layer_embedded


Bert

That was extremely interesting.

What amazes me is that much of this work had already been done decades ago and yet we've missed the boat (until now) to really come to grips with the implications of this. This will revolutionise everything from meteorology to bioengineering. The understanding of how lightning is generated to how cell membranes regulate the diffusion of substances across into cells, to power generation via photosynthesis, to new battery technologies etc etc etc. It's even conceivable that other liquids which have charge polarity of their molecules may also behave in a similar fashion and it would be wise to study these as well. This even has implications for the origin of life, as was mentioned. Given this liquid crystalline behaviour, this may also be applicable to the creation of LCD's, even information processing (liquid crystalline water CPU's???, water based "fibre optics"???).

It could even be said that the water on this planet acts like a gigantic, living cell. It has an interior that is surrounded by a "cell membrane".

This research is going to lead down paths we haven't even considered yet. It will also rewrite quite a few textbooks. In many ways, this is as revolutionary (to even more fields) as Relativity. As the adman once said "watch this space".

[multiweb (Marc)]

Loved the presentation in layman terms and how he eases into it to the point everything falls back into place by the end very simply and elegantly, almost logically. Very fascinating stuff indeed.

[CraigS]

Ok … so from the perspective of this thread, complex structures can form from very simple behaviours .. in this case, charge separation in water, in the presence of hydrophyllic and hydrophobic surfaces ... all courtesy of energy delivered by incident radiation. Perhaps yet another non-linear 'system' which has influenced the evolution of the geological and atmospheric environments on this planet, for eons. I wonder how all this factors into our state-of-the-art models in these domains ?

There is clearly a very close linkage between water based life emerging on this planet, and the presence of vast quantities of water … no big news there.

As far as the commercial applicability, I'm yet to be convinced … as with all scaling-up exercises ... the devil is in the detail. The issue being what happens to the energy efficiency as the application scales ? (This being said, I also share some optimism, in that the research avenue looks interesting).

Thanks for the Youtube, Bert … very thought encouraging !

Cheers

[renormalised (Carl)]

Precisely, Craig.....simplicity drives complexity, and most likely, vice versa.

I get the feeling that many of our "state of the art" models for these systems, based on a reductionist paradigm as they are, will be in need of some very heavy revision, if not total replacement in some cases.

[CraigS]

I don't think I'm so concerned by reductionism when addressing questions applicable to behaviours at different physical scales. To date, it has worked remarkably well in many areas of understanding.

I think the issue I have, is more about the imposition of predictability over systems which may be inherently … not predictable. This is at the very core of the scientific process and yet, predictability seems to be a condition imposed on the process, seemingly purely, by a human obsession to predict the future. A theory which has no predictive capability, will have no legs, by definition !

There's no question that prediction is possible where little/no randomness (or non-linearity) dominates. But in complex systems, certain macro features emerge, whose behaviours may be predictable, (due to an inherent lack of randomness), or completely unpredictable. The next scale level up in the same structure, doesn't necessarily exhibit the same degree of order (or predictability) as the lower scale .. and yet we seem to expect that it does ! (Like this guy's energy production 'cell' … will it work the same way at larger scales ???)

Perhaps this is a by-product of (reverse) reductionism ?

Short-term weather forecasting, as distinct from long-term weather forecasting, are two areas exhibiting completely different degrees of predictability … its not too hard to predict tomorrow's weather in a given area, but what of the weather 3 months out .. or even a year out .. or many years beyond ?
.. And yet .. these are behaviours of the same system .. ie: the Earth's atmosphere.

Other things also happen with galaxies, which even result in different structures emerging eg: spiral as distinct from open, or elliptical. Is the emergence of one of these structures over another, predictable ? … Even if they are all driven by the same fundamental forces of nature ?

I think the study of computer models, is teaching us a lot about our own self-imposed expectations about how the universe must behave .. by showing us where they actually don't behave.

But getting this word out to the masses is nigh on impossible, at present.

Cheers

[renormalised (Carl)]

Originally Posted by CraigS

I don't think I'm so concerned by reductionism when addressing questions applicable to behaviours at different physical scales. To date, it has worked remarkably well in many areas of understanding.

The problem with reductionism is that you can only reduce something to a certain extent before you start to lose sight of the connections between paths within that process. Much of the time, the behaviours of system are more holistic and synergistic in nature than nice, neat compartmentalised boxes. What's the use of knowing how each and every gear and cog in an old watch works, without having a clue about how they all work together to produce the movements of that watch. Reductionism is great for defining all the parts within a system, but it's no use to figuring out how that system works...except in a very tedious and linear way.

Originally Posted by CraigS

I think the issue I have, is more about the imposition of predictability over systems which may be inherently … not predictable. This is at the very core of the scientific process and yet, predictability seems to be a condition imposed on the process, seemingly purely, by a human obsession to predict the future. A theory which has no predictive capability, will have no legs, by definition !

Another reason to be careful with reductionism. Complexity almost begs for a holistic approach to science, where both complexity and simplicity are seen as different aspects of the one overriding principle that influences the entire system. Reductionism rides very well with predictability as they both depend on a linear train of thought...A + B = C. Breaking things down into their components and then adding them altogether to get the final product. It can help with problem solving, by taking complexity and making it simpler, but doesn't necessarily show you the complete picture.

Originally Posted by CraigS

There's no question that prediction is possible where little/no randomness (or non-linearity) dominates. But in complex systems, certain macro features emerge, whose behaviours may be predictable, (due to an inherent lack of randomness), or completely unpredictable. The next scale level up in the same structure, doesn't necessarily exhibit the same degree of order (or predictability) as the lower scale .. and yet we seem to expect that it does ! (Like this guy's energy production 'cell' … will it work the same way at larger scales ???)

Perhaps this is a by-product of (reverse) reductionism ?

I agree, for the most part. But predicting something at one scale being applicable at another can be useful. It gives a direction in which to head with further research. It can also actually show that despite complexity and the nature of predictability/unpredictability within systems, more often than not a generalised trend in the way systems behave can be elucidated. A case of A + B ~ C.

Whether the prof's energy production cell can be scaled up is a matter of empirical experimentation, not theory. The theory will work at any scale. It's only the practicalities of scaling and whether there are restrictions to scaling up the process that exist. The only way to find out these things is to perform the experiments. The theory is only a set of guidelines with which to work with. Quite often, theory has to be revised or rewritten because of what they find through experiment.

Originally Posted by CraigS

Short-term weather forecasting, as distinct from long-term weather forecasting, are two areas exhibiting completely different degrees of predictability … its not too hard to predict tomorrow's weather in a given area, but what of the weather 3 months out .. or even a year out .. or many years beyond ?
.. And yet .. these are behaviours of the same system .. ie: the Earth's atmosphere.

I don't know about that. I've seen the meteorologists stuff up even the daily weather forecasts Quite regularly!!!!

The degree of predictability seems to depend on the degree of complexity of the system being studied and the behavioural links between that system's component parts. As I said earlier, generalised trends in predictability can be mapped out, however the more you look at the nitty gritty, the harder it becomes to figure out what's happening.

Originally Posted by CraigS

Other things also happen with galaxies, which even result in different structures emerging eg: spiral as distinct from open, or elliptical. Is the emergence of one of these structures over another, predictable ? … Even if they are all driven by the same fundamental forces of nature ?

Like any other complex system, galaxy formation is not something you can just sit down and say "X and Y will become this and that galaxy". Everything has to be taken as a whole. Trying to reduce it down into a nice series of steps is all well and good, but you have to know what the causal links between the steps are, and how these interact to produce the final product. Those fundamental forces of nature do not act in isolation. Not seeing the forest for the trees means you will most certainly miss important interactions which drive the formation of the different galactic morphologies. Many of the processes within each of the different galactic morphologies are basically the same, but it's the way they interact and the timing between the processes which generates the different morphologies.

Originally Posted by CraigS

I think the study of computer models, is teaching us a lot about our own self-imposed expectations about how the universe must behave .. by showing us where they actually don't behave.

But getting this word out to the masses is nigh on impossible, at present.

Cheers

Computer models are only as good as the data they use, the level of programming being used and the complexity of algorithms generating the models. It's the old adage...GIGO. Many of our self imposed expectations are present in these models. What we see with these models where they deviate from that norm is where the algorithms produce unexpected results due to unforeseen interactions produced by those models. This is usually a function of time and increasing complexity as the models evolve. It also depends on the number of variables that the algorithms use to create the models.

I don't think that getting the word out to the masses is necessarily a good thing, considering that 98% of the masses wouldn't understand where the word was coming from. Most people have trouble just getting through an average day, let alone having to deal with chaos and non linear systems theory in their daily lives. So long as they are given a general idea of what is trying to be conveyed to them, most are quite content to leave it at that. Even there, most usually don't get what's being told to them and the reason why you get all the nonsense you see with science reporting and such, these days.

All the complicated business they leave up to those that actually can understand and use it.

[CraigS]

Originally Posted by renormalised

All the complicated business they leave up to those that actually can understand and use it.

… and from what I've seen lately, even the experts don't necessarily agree about the 'expected' effects of things like feedback … and what mechanisms actually constitute feedback in the real complex systems.

Sensitivity to change is also hotly debated, as are the 'initial' conditions.

Its interesting to note that without the added pressure for predictions, neither would need to be 'hotly' debated at all !

Cheers

[Karls48 (Karl)]

From my experience, I would not trust any computer-generated models. Although the outcome of such computer generated model can match the expectations, the process how the outcome has been arrived to can not be understand and given sufficient number of iterations same outcome could be arrived to by using different internal process. Long time ago, I wrote program to predict OZ Lotto draws. My reasoning was that in infinite possibilities of random numbers generated by Lotto draws there should be random number generator that is resonant to Lotto numbers generating machine. The program looked on all past draws and by using kind of genetic algorithm try to made random numbers generator that is in synch with actual outcome. Well, by playing System 8 for two years my loss was around $140. That is way above probabilities outcome.
Why I am telling you this. Well, by changing one line in my program the process would be completely different, but outcome could be same.

Craig – question to you. You are believer in chaos theory. The butterfly flaps its wings – and so on. What will happen when tens of thousands wind generators generating GigaWatts of power will disturb airflow across the globe? What will happen when thousands of hectares of the soil are shaded by solar panels? What effect that will have on global weather? Can computer generated model answer this?

I don't think that getting the word out to the masses is necessarily a good thing, considering that 98% of the masses wouldn't understand where the word was coming from. Most people have trouble just getting through an average day, let alone having to deal with chaos and non linear systems theory in their daily lives. So long as they are given a general idea of what is trying to be conveyed to them, most are quite content to leave it at that. Even there, most usually don't get what's being told to them and the reason why you get all the nonsense you see with science reporting and such, these days.
All the complicated business they leave up to those that actually can understand and use it.

Carl – although I really admire and appreciate your knowledge and your holistic approach to the science – I think that you are bit too harsh in this comment. Not everyone is a complete idiot even if they never went to University and do not have PHD. Most of the people with some sort of technical education will ready accept applied science/technology. It is when it comes to speculative fields of science (Cosmology – Big Bang, Dark Mater, Dark Energy, Expanding Universe and number of other theories I can not thing of right now) when people start question the science. Although, those theories are best what we can come up with at this time, holistically thinking – they are most likely wrong.

Avandok – Thanks for the link. For long time I have believed that, there may be an electrolyte that can store and be recharged by solar energy. I tried and nothing seems to work. It did not come to my mind that the plain water could be the answer.

[renormalised (Carl)]

Problem is, Karl, that much of the general public has a hard time understanding what science is about and especially what scientists sometimes talk about. It's not that they're complete idiots (some are, but that's not the point here), it's that most people have not had an interest in science to the point that they want to know about it. What makes things worse is that journalists try to interpret what they're told and invariably get the bull by the tail or just make things up in order to "sell" a story. The science is either completely watered down and misinterpreted or it's utterly false and bears no resemblance to what they were informed about. Another thing, because someone may have a technical background doesn't necessarily mean they will understand science. They may have a handle of the applied side of things but that's far from actually understanding the theory and the practice behind the theory. Most of those areas of science you have mentioned are not for the fainthearted and even to have a good grasp of them in descriptive terms takes having a good background in the subjects. It's hard enough for those scientist doing work in these fields to come to grips with the theories as they present themselves. Can you imagine how much more difficult it would be for someone, even with a technical bent, to come to terms with them. For most of the general public, it goes right over the tops of their heads. No matter what way you think of them, reductionist or holistically, saying that they're wrong requires that you know enough to have a good enough reason to say that they are. It's very much put your money where your mouth is with science...you either know or you don't. Most don't know, so anything they say is nothing more than opinion and in most cases opinion taken from ignorance or very little background knowledge at best.

And you know what makes things worse??...even the scientists don't always agree with one another. So, you get conflicting views. Leaves poor "Joe Public" in a bit of a mess, doesn't it

[renormalised (Carl)]

Originally Posted by CraigS

… and from what I've seen lately, even the experts don't necessarily agree about the 'expected' effects of things like feedback … and what mechanisms actually constitute feedback in the real complex systems.

Sensitivity to change is also hotly debated, as are the 'initial' conditions.

Its interesting to note that without the added pressure for predictions, neither would need to be 'hotly' debated at all !

Cheers

Have you ever known two experts to actually really agree about anything??

The problem with feedback mechanisms is that like anything in a complex system, they're highly sensitive to the initial conditions which support the feedback and any change which occurs will affect the outcome of the feedback. With regard to mechanisms of feedback in complex systems, this is where a reductionist approach can get you into trouble. You can break the mechanisms down into their constituent parts and follow the paths back into the system. However, taking the linear approach of following the feedback loop won't tell you how each part interacts with the whole mechanism. The actual feedback might not necessarily be linear or even predictive. Complexity within complexity, so to speak. Sometimes the direct reductionist approach works, but not all the time. That's why experimentation is crucial...the theory is only a model of what you believe to be happening and/or an explanation of what comes from experiment. It's never the answer entirely.

Throw in prediction, which is a cornerstone of theory, and that only makes things more complicated, unfortunately. If you had no need for making predictions and just accepted outcomes as they were, it would make things easier. But less satisfying and inherently more uncertain so far as understanding was concerned.

[CraigS]

Originally Posted by Karls48

Craig – question to you. You are believer in chaos theory. The butterfly flaps its wings – and so on. What will happen when tens of thousands wind generators generating GigaWatts of power will disturb airflow across the globe? What will happen when thousands of hectares of the soil are shaded by solar panels? What effect that will have on global weather? Can computer generated model answer this?

Hi Karl;
I need to clarify that I'm no 'believer' in Chaos Theory (see my signature).
However, what I stand for is to acknowledge the fact that the majority of systems in nature are for the most part, non-linear. That is, the whole is not the sum of the parts, their behaviour ranges from purely chaotic to completely ordered within the same system, as do the structures formed by the processes within them. As such, most systems in nature are not predictable and yet that's exactly what we try to (inappropriately) infer from them, when we model them in computer models.

The Butterfly effect is one of the most widely mis-understood concepts I think I've ever seen. In short, it serves as a cautionary reminder about the limitations of determinism and reductionism in science ... not that butterflies play a role in determining weather. Even if we had perfect data which we could trace back from an effect to cause and discover the linear cause of events, and find that some minor event, (a butterfly flapping), originally led to a major one (a cyclone), this in no way means that if that same minor event recurs, it will go on causing the same major one again.

So, in answer to your question, the effect of solar panels and windmills on the chaotic weather system is unpredictable. So, I wouldn't have the foggiest idea what the outcome might be … and neither does anyone else !
Modelling it in a computer, will provide us with more knowledge of typical behaviours of the overall system .. but as far as being able to consistently predict the value of something detailed … like its impact on the global temperature to a fine degree of tolerance … over long periods of time??

… No way !

Cheers

[CraigS]

Originally Posted by renormalised

The problem with feedback mechanisms ....

The problem with feedback mechanisms in say, Climate Models, is that no-one has been able to figure out a way to actually measure these from existing observational data. The issue appears to be a lack of accurate long-term measurements of global cloud cover changes.

From thereon, the 'debate' becomes 'my opinion' vs 'your opinion'.

With no empirical data, the relationships between incident radiation and temperature, (and vice-versa), is unverifiable.

Mind you, there's plenty of temperature data … and yet there are still big arguments about whether or not these show an increasing or decreasing trend.

Even if we did have accurate cloud cover data, there'd probably still be disagreement .. as it seems to me, that this is a blatantly obvious chaotic phenomenon where prediction is not possible … all as you point out .. even the feedback mechanisms are likely to be chaotic.

Cheers

[avandonk]

Originally Posted by renormalised

That was extremely interesting.

What amazes me is that much of this work had already been done decades ago and yet we've missed the boat (until now) to really come to grips with the implications of this. This will revolutionise everything from meteorology to bioengineering. The understanding of how lightning is generated to how cell membranes regulate the diffusion of substances across into cells, to power generation via photosynthesis, to new battery technologies etc etc etc. It's even conceivable that other liquids which have charge polarity of their molecules may also behave in a similar fashion and it would be wise to study these as well. This even has implications for the origin of life, as was mentioned. Given this liquid crystalline behaviour, this may also be applicable to the creation of LCD's, even information processing (liquid crystalline water CPU's???, water based "fibre optics"???).



It could even be said that the water on this planet acts like a gigantic, living cell. It has an interior that is surrounded by a "cell membrane".

This research is going to lead down paths we haven't even considered yet. It will also rewrite quite a few textbooks. In many ways, this is as revolutionary (to even more fields) as Relativity. As the adman once said "watch this space".

It has been said before that all life is water dancing to the molecules.

I have seen ordered water in high resolution xray crystallographic electron density maps around protein molecules. The paradigm was that it only extended for a few layers. It was wrong.

My real urge is to get back into the lab to explore this. Unfortunately all I can do is pass on the information and my version of the implications.

All of science is models of reality. It is only by experiment we refine these models. Eventually the model is found wanting to really describe what is going on. The tools we have are only limited by our ingenuity and insight.

I am sure my view of the Universe will be considered 'quaint' in the future. Until this happens it is the best we can do. It is up to our young to further explore and find what we have been overlooking.

Bert

[bojan]

Originally Posted by avandonk

As someone that has worked in science for forty years prepare to be shocked!
Just watch this video and if you understand all the implications you understand.

http://www.youtube.com/watch?v=XVBEw...layer_embedded


Bert

This is extremely interesting!

Now , a bit OT question:
Anybody knows how to save this (and other) video stream to hard disk, for viewing later, without internet connection?
I investigated a bit, it seems the video (in flv format) essentially comes form various sources, and, while it does exist in computer memory, it is not in cache, (or at least doesn't appear to be there) and therefore can't be saved?

[CraigS]

Originally Posted by renormalised

Whether the prof's energy production cell can be scaled up is a matter of empirical experimentation, not theory. The theory will work at any scale. It's only the practicalities of scaling and whether there are restrictions to scaling up the process that exist. The only way to find out these things is to perform the experiments. The theory is only a set of guidelines with which to work with. Quite often, theory has to be revised or rewritten because of what they find through experiment.

Yep … its interesting that Complexity Models created to replicate natural phenomena, cannot be (easily) directly tested. Parts of them may be able to be tested, but it is unlikely that the whole, can.
This therefore also applies for models where spiral galaxies are created .. and climate forecast models … all for the same reasons. As everyone says, these models will only ever be approximations for the real thing.
However, in complex systems, small errors in the models, originating from untested assumptions, can lead to dramatically different predictions.
More complexity sub-systems added to the models, is also likely to drive the models further away from reality .. the same as too few.
As astrophotographers are aware, in the presence of 'noise', more data improves visibility of 'trends', but 'noise' is fundamentally different from chaotic behaviour .. so more data also doesn't 'smooth out chaotic behaviour. Extrapolating a chaotic model, only results in more chaos.

In this case, predictions will always be in error and the further out you go in time, the bigger the errors will be (away from reality).

Cheers

[renormalised (Carl)]

You know, with all this talking we've yet to come up with a suitable punishment for this misbehaving universe

I reckon 20 lashes with the feather duster and then transportation for life to a suitable penal colony, say Cairns or Canberra, will be sufficient

[avandonk]

Originally Posted by renormalised

You know, with all this talking we've yet to come up with a suitable punishment for this misbehaving universe

I reckon 20 lashes with the feather duster and then transportation for life to a suitable penal colony, say Cairns or Canberra, will be sufficient

It is the first sign of maturity when the young first question and then defy their parents. Punishment is the last phase! Choosing their retirement home comes to mind!

Bert

[multiweb (Marc)]

Originally Posted by bojan

This is extremely interesting!

Now , a bit OT question:
Anybody knows how to save this (and other) video stream to hard disk, for viewing later, without internet connection?
I investigated a bit, it seems the video (in flv format) essentially comes form various sources, and, while it does exist in computer memory, it is not in cache, (or at least doesn't appear to be there) and therefore can't be saved?

Unless it is streamed it is always in the cache and Youtube always caches its output. It's not always in FLV format but you can rename the file extension to FLV and play it in a standalone player if you wish. Usually do a search for videoplayback[n], where n auto increments to 1,2 etc... and sort by date/filesize. The file shouldn't have any extension. Just copy it somewhere else and rename it <myvideo>.flv and play it back at your own leisure.