The Theory of Non-Causality, Apparent Causality & Natural Variation

In this article I disprove the whole of modern science (at least the version taught in academia). I disprove much of the belief system present in modern academia and I show that global warming is natural variation. But accepting what I say, really amounts to “common sense”. It’s not that science is wrong – it’s that when applied with “common sense” it is right – it’s just that recently academia has given up on common sense.
In academia, there is a strong belief in the theory of causality, that is to say if we have a stimulus A then it will cause an effect B such that

A→B.

This implies that if A is present, then B is present whereas if A is absent then B is absent.

However, the theory of causality (at least for the purposes of this argument) is that if we have an effect B, there must exist a cause A. Or to put it into simple terms: if we see a warming in the late 20th century, then there must be a “cause”.
The theory of non-causality is so simple that it is known to everyone intuitively and so academia doesn’t have to teach it, instead they have to try to get people to adopt the theory of causality. And in so doing, they constantly portray the theory of non-causality as “bad”, “not-thinking-properly”, etc. etc.
And it is best to use a few examples:
1. My oil light went on in the car one day when I started it. I’m not sure why, the oil seemed fine (above minimum), but I filled it up to the brim and it’s not come on again. It may be it was parked on a slope. It may be there was an air bubble at the sensor. I can’t know whether it will reappear unless & until it comes on again. Till then I will keep an eye on it.
2. My wife comes home from work. She’s in a foul mood. According to the theory of causality, I should find the cause and remove it. According to the laws of non-causality, I should ignore the cause and just let her do something she enjoys.
3. In a factory, a machine breaks down. We find there’s been a lack of maintenance. According to the theory of causality, we identify the cause and then we schedule the maintenance for that cause. According to the theory of non-causality, we focus on ensuring all maintenance takes place – because sure as eggs are eggs, if one thing has been neglected then many things will be neglected. (Note this is not a causal law).
4. A butterfly flaps its wings in the Amazon (or where ever it has to be) and a storm appears over Washington (or where ever). According to the theory of non-causality we say “there is a storm”. According to the theory of causality they say “The butterfly’s flap caused the storm, and therefore if there is a storm there must have been a butterfly” (which is obviously non-science as any disturbance from a leaf falling to a small meteorite could have been the “cause” indeed, there were likely innumerable actual “causes” all as small and smaller than the butterfly. Indeed, we are talking about random motion of individual atoms “causing” the storm if we go back far enough.
So to put this formally we would have … well I’m not sure. Perhaps “There is B and it might be caused be A1,A2,A3, …A∞ in other words, there are an unaccountable number of possible causes and we can only hope to know the most important. So formally:

A1,A2,A3, …A∞ →B

The theory of non-causality doesn’t say that things are not caused by something. Instead it say that we can never know for sure what they were caused by. And this has been formally adopted into physics in the form of some pseudo-causal rules: statistical models, Quantum waves, the “Wave-particle” duality. These are all “non-causal” models that have been adopted into a profession that overwhelmingly believes in “causality”.

The Problem with Causality as a belief

Anyone who has discussed the global temperature (and won the argument that temperatures have not risen as predicted) will have heard the phrase “well it must have been caused by something” which then is quickly followed by “so what did cause it”. The aim here is to try to have a beauty contest of possible causes. They say it was CO2, and they are hoping we will fall for their stupid argument technique, offer an alternative – which they will then attack (usually without any good evidence) – and by “proving” that our proposed cause is “false”, they believe this proves their cause “must have done it”.
This is of cause a total nonsense. Let us use the example of the oil light above. Let us suppose the academic is convinced it was “the wiring that done it”. I then say “I believe it was Elvis Presley’s ghost” – they remind me that ghosts do not exist, does that increase or decrease the likelihood that the oil light came on because of a wiring fault? The answer is that the fact Elvis Presley’s ghost did not cause my oil light to come on says nothing about whether it was a wiring fault.
However, this is how the theory of causality is used in academia to “prove” things. They assert that a relationship exists between A1 and B. They then demand to be shown what else could have caused it. If no one can show A2, A3, A4,A5, A6, A7, A8, A9,A10, A11, … A∞ as “causes”, they then claim (falsely) that A1→B or indeed, in many cases (notably climate), they will assert C→B and unless anyone can prove A1,A2,A3, …A∞, as potential “causes” they will claim “it must be C”.

All systems are non-causal

Let’s take a simple example. We are all taught in physics that the orbits of planets is defined by gravity. And therefore that we can work out the movement of planets.
Fine, so, we take our laws of gravity and apply them to the planets and work out where the planet that hit the earth when the moon was created was orbiting … except we can’t. Having been told “the orbit of the planets can be worked out” – we immediately find that as soon as one body hits another, that the orbits cannot be predicted before that event. But hang on a minute – each and every day the earth is hit by many many bits of debris. Thus the “law” that we can work out the precise orbit of the earth is wrong even from day to day. And as we go back in time, with more and more perturbation from this space debris and more time for it to have had an effect (like the butterfly’s wings) – the exact orbit of the planets becomes impossible to calculate. And there are other effects, such as the small perturbation due to the movements of nearby stars in our Galaxy. And as the “orbit” of electrons around a nucleus of an atoms obeys pretty much the same rules (but a lot faster) they are likewise unknowable for all reasonable purposes. And as everything is the result of the behaviour of atoms and the behaviour of atoms cannot be predicted, then it follows that nothing is really “causal” in the sense it is used in academia.
So rather than laws of the form :

F=ma

Every law should be written:

F=ma + ε()

Where ε() represents all the unknown influences from cosmic rays, to earth tremors, to random atomic fluctuations. In many circumstances ε() is so negligible as to be ignored, or so obvious (an earthquake) that we know the experiment is invalid. So F=ma is a very good approximation and in a controlled environment for all reasonable purposes the error can be smaller than the accuracy of the instrumentation. But that doesn’t mean F=ma is true in any practical situation. So, it may be “true” – in a theoretical universe inhabited by theoretical academics in theoretical ivory towers. But that doesn’t mean it applies to the real world which is what science is modelling.

The appearance of causality only exists in well defined and controlled systems

Causality as a practical thing we can measure, does not exist anywhere in the universe, but instead we have the illusion of causality if we can control a system in such a way that the growth of non-causality is controlled within the lifetime of the system.
For planetary motion, this “lifetime” of causality is millions of years long. For an atom, it may be nano-seconds long (just a guess). For a laboratory experiment of  motion on a ramp – we may not see any significant earthquakes for thousands of years. Thus if we monitor/control the experiment, we will find that most of the time the experiment proceeds according to what appears to be a causal relationship. That does not mean:

A1,A2,A3, …A∞ ↛ B

Instead it means, if A2,A3,A4 … …A∞ do not change significantly (weasel word!!) then:

A1 →B

In other words, in a well controlled environment (a University lab) in which everything is constrained from affecting the result except the thing we desire to show causes the output, we can often show that the thing we desire to cause the output … does indeed cause the output we wanted to show. From which many academics (sitting in their ivory towers) will then believe that the world is causal: that for every thing we see, there is a cause – and only one cause that “caused” it.

The reality, is far from the world being causal, the truth is that the appearance of Casuality only exists in certain circumstances: well controlled environments, limited time-scales and limited precision of measurements. But in truth Non-causality exists in all circumstances, because in no circumstances can we know a system completely. That’s because the system is the total Universe which all interacts. So we can not possibly assert that if we had measuring equipment which measured without any error, totally precisely that we could work out what was happening. Because without knowing what was happening in the rest of the Universe (or at least what particles were going to collide from outer space at any moment) we cannot work out what will happen only what is likely to happen (and then discard any “outliers” due to “unknown faults”).

Causality is a false belief

As I said above:

The theory of non-causality is so simple that it is known to everyone intuitively and so academia doesn’t have to teach it, instead they have to try to get people to adopt the theory of causality. And in so doing, they constantly portray the theory of non-causality as “bad”, “not-thinking-properly”, etc. etc.

From a practical viewpoint, trying to teach people that in many instances the (non-causal) world can be described for all reasonable purposes in a causal way is a highly laudable thing to do. And so it is perfectly right that academics continue to emphasise the importance of causal relationships such as those we find in physics. And when used sensibly in many circumstances, those causal relationships are invaluable ways to help solve problems. But that is not always true.
The problem is that the world we live in is not really causal. We live in a non-causal world, where only in special circumstances can we apply the rules of causality and get the right answer. And unless we understand what makes these “special circumstances”, we end up with the kind of nonsense we’ve seen in climate for the last few decades. Where academics have taken a system which is obviously hugely complex (it is more complex than anything on earth – because it is the complexity is the sum of everything on the earth). And then they have tried to use “laws of causality” to prove: that if there is an effect – there must be a cause.
As many have said, this is nothing short of religious clap trap. It is no more sensible that saying “everything is caused by god – so the 20th century warming proves god exists” because god must be the cause of everything – anything being caused shows he exists. Likewise, because CO2 must be the cause of warming – any warming “proves” CO2 is the cause. Claptrap, bananas – but that is how many academics apparently think. Far from the rational creatures they imagine themselves to be, many are very irrational in their reasoning strenuously asserting their belief in a causal universe on to clearly non-causal systems.
And so it is quite understandable that a huge number of people reject “science” (meaning academics as a body) with its prescriptive assertion that everything is “causal” in what is clearly an overwhelmingly non-causal universe (filled with people who are anything but causal). Unfortunately, they are not really rejecting science – but the belief of academia that “everything can be explained by science” or indeed “everything should be explained by scientists” by which they really mean “society should see us ‘fresh-off-the-teat’ academics as the ‘wise old men’ whose judgement on everything should be law”

Natural Variation

Going back to:

A1,A2,A3, …A∞ → B

If we know a limited number of things such as A1, A2,A3, we could** write this as:

A1,A2,A3, v(A4,A5… A∞)** →  B

(where v, is a function)And if A1, A2, A3 have a very much larger effect than the total effect of A4,A5… A∞, then we can write this as:

f(A1,A2,A3, v(A4,A5… A∞)) →  B

In a well controlled environment, for a linear system (with no steps) where v() is small and where the effect of changes in v() can be modelled for practical purposes by a random function V() we can approximate this as:

f(A1,A2,A3) ≈  B + V(A4,A5… A∞)

And V() is what is called “Natural Variation”. It is not “Gaussian” noise, instead it is a variation that naturally exists which is (usually) close enough in behaviour to some kind of noise function, that for all reasonable purposes, in many circumstances, it’s behaviour mimics a noise function.

And it is because there is a theory of “natural variation”, that academics are able to reconcile their belief in a causal 100% predictable universe, to the reality of a non-causal world where nothing ever works quite as it “ought”.

Natural Variation in the Climate

In many systems, the source of “noise” (Natural variation) cannot be reasonably known. So, e.g. it’s well known that all electronic components create “noise” (as we hear on an AM/FM radio when we detune from any station and turn up volume).  The component has a very simple function (like a resister V=IR) and the noise can for all reasonably purposes be modelled as a small perturbation on top of this (V=IR + ε() where ε() is noise function).
So, noise and function are clearly distinguishable in electronics. But however, when we look at the climate, noise in this system can be much larger than us humans, it can be larger than countries, it can in fact be as large as an ocean or a hemisphere.
Now, if we model the climate as

f(A1,A2,A3, v(A4,A5… A∞)) →  B

Many of those “small” components (A4,A5… A∞) are large enough that they can be individually measured. And indeed, there is now an almost arbitrary definition of “natural varation” in the sense that we can decide numerous different ways to cut of between “causes” that are uniquely identified and “causes” that are lumped together as “Natural Variation”.

To use a practical example. El Nino (as measured by ENSO) is a cause of variation in the climate. We know roughly what its effect has been in the recent past as we have measurements. We know it heats the climate during El Nino and cools it during La Nina. Therefore when modelling the recent past we can separate out the effect on the climate:

f(A1,A2,A3, ENSO v(A4,A5… A∞)) →  B

However, if we try to model the climate before our measurements for ENSO begin, we cannot include it as a “known cause” so, we have to lump it in as “natural variation”:

f(A1,A2,A3, v(ENSO, A4,A5… A∞)) →  B

The same is also true for PDO, AMO and any other ocean “oscillation”. These are all both “causes” of climate variation that may at times be capable of modelled and at others have to be treated as “Natural Variation”. The problem is that academics can’t seem to understand, that when it comes to climate, just as we have known things like ENSO which have an effect, so there are a host of others (A4,A5… A∞) which also have an effect. And even if each is smaller than ENSO, if there are an infinite number the potential size of all these small perturbation is infinite. In other words, there is no way to prove that the sum of all unknowns is not larger in effect than what we know.

Instead, all we can practically do is to measure what we can see of natural variation (or the unmodelled variation) to assess its size and then model it as best we can. And below is a section of global temperature – or at least I thought it was. Instead I found this image. And it may be part of CET it may be simulated noise. I remember producing a graphic turning the temperature upside down and reversing it. I’m not sure what it is, but that really is the point because I can’t tell temperature graphs from 1/f noise graphs – unless they have a particular feature that is unique to the temperature graph (such as the 1970s cooling).

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**this is not strictly true in a mathematical sense – instead in a well controlled environment if A4… represent the causes that are being controlled or are too small to matter, they can be thought as being separate.