Serendipper wrote:Everything has a cause. If something appeared by magic, then magic is the cause. If it appeared from randomness, then happenstance was the cause. If by hallucination, then hallucination was the cause.

The word "magic" refers to extraordinary occurences (in relation to what was in the past, which is what is considered ordinary) for which there is no evidence. When evidence that proves such occurences is present, these extraordinary occurences are not treated as magic but as reality.

The word "extraordinary" is relative. It is relative to one's circumstances. For example, to Buddha, death was extraordinary.

I am afraid you are stretching the concept of causality a bit too much and by doing so missing the point. I will give you an example of information within which no causality, and also no (complete) order, can be identified.

Suppose that we are dealing with a number of occurences of certain category of events. Suppose that this category contains only two types of events and that we can represent these using letters A and B. Suppose we decide to dig into our memory in order to find out how many of such events happened in the past. Suppose that our memory knows nothing about the order in which these events occured. Suppose that the only thing our memory knows is how many times each one of these events, A and B, occured in the past. Suppose that A and B occured 2 times each. We can represent this using a set {A, A, B, B}. What this means is that at 4 occurences of events of this type A occurs 2 times and B occurs also 2 times. Now, what we want to know, which is to say to predict, is how many A's and how many B's there are at different numbers of occurences of events of this type. Suppose we want to know how many A's and how many B's there are at 5 occurences of events of this type.

Our job is to choose one choice out of many choices. Many will also say to choose the best choice from a list of choices. The question is, what kind of criteria should we use? In reality, you can use any criteria. But there is only one criteria associated with intelligence.

Intelligence is about making evidence-based -- you can also say informed or educated -- decisions. What this means is that intelligence is about choosing what aligns with evidence (i.e. personal experience) the best.

If what we know -- our evidence, observations, experience, etc -- is represented using a data set then what we need to do is to find a data set that has the highest degree of similarity to it.

It's pretty easy to see that every data set has only one other data set that has the highest degree of similarity to it and that this other data set is its clone. For example, {A, B} has only one closest relative and this relative is its clone {A, B}. It's pretty easy to figure out the closest relative for any data set. There is no need for intelligence. This is why it is necessary to say that intelligence is about finding the closest relative of a given data set that is not its clone. In fact, sometimes we might go further than that and look for the closest data set within even narrower category of data sets.

With all of this out of our way, we can proceed to define intelligence in the following manner: intelligence is the search for the closest relative of some data set D within some category of data sets C.

In effect, the task of intelligence is to

approximate.

In our above example, our personal experience is represented using a data set {A, A, B, B} which means "2 occurences of A and 2 occurences of B". What we want to figure out is which one of the data sets within the category of data sets that contains every combination of A and B with length 5 is the closest relative to the data set that represents our personal experience.

The first thing we are going to do is to list all combinations of A and B of length 5. Then we'll compare each to the data set that represents our personal experience and figure out, using either intuition or a set of rules, which one of them is the closest relative.

Here we go:

{A, A, A, A, A}

{A, A, A, A, B}

{A, A, A, B, B}

{A, A, B, B, B}

{A, B, B, B, B}

{B, B, B, B, B}

And we want to compare each one of these to this data set:

{A, A, B, B}

The question is how to compare them. The easiest way is to use intuition. We can easily determine the rank of these based on how similar they are to {A, A, B, B}. A bit more difficult task is to understand how intuition works. For the purpose of this post, I will say that our intuition works by counting how many elements the two compared sets have in common. This is how we quantify the degree of similarity between two sets. Roughly speaking. And that's what we're going to do.

{A, A, A, A, A} has 2 elements in common (A and A.)

{A, A, A, A, B} has 3 elements in common (A, A and B.)

{A, A, A, B, B} has 4 elements in common (A, A, B and B.)

{A, A, B, B, B} has 4 elements in common (A, A, B and B.)

{A, B, B, B, B} has 3 elements in common (A, B and B.)

{B, B, B, B, B} has 2 elements in common (B and B.)

We can see that there are TWO closest relatives to {A, A, B, B} and these are {A, A, B, B, A} and {A, A, B, B, B}. This means that at 5 occurences of events of type either-A-or-B there is either 3 A's and 2 B's or 2 A's or 3 B's.

The situation in which there is MORE THAN ONE closest relative is known as randomness (also equiprobability.)

The more heterogenous (i.e. mixed) the data set, the greater the number of its closest relatives. Figuratively speaking, such data sets are "friends of everyone".

The greater the number of closest relatives, the higher the degree of randomness. Randomness can also be defined as a degree of arbitrariness.

If you apply the same logic to 6 occurences of either-A-or-B you will get a different outcome. At 6 occurences, there is exactly one closest relative. And that is {A, A, A, B, B, B}. This holds true for every even number of occurences of either-A-or-B.

At every number of occurences of either-A-and-B, the closest relative of {A, A, B, B} is:

1: {A} or {B}

2: {A, B}

4: {A, A, B, B}

5: {A, A, B, B, A} or {A, A, B, B, B}

6: {A, A, A, B, B, B}

7: {A, A, A, B, B, B, A}, {A, A, A, B, B, B, B}

8: {A, A, A, A, B, B, B, B}

etc.

From this we can see that there are no functions fA(n) and fB(n), where n is any natural number, that return a number of occurences of A and B respectively for n occurences of either-A-or-B.

However, if n is any

even natural number then these functions exist and they are fA(n) = fB(n) = n / 2. If we change n to be any natural number that represents a number of pairs of elements within the set, then these functions can be rewritten as fA(n) = fB(n) = n and we can say there is a correlation between the number of pairs of elements within the set and the number of A's and B's.

So, there is a correlation, but where is causation?

And this correlation isn't everywhere within the information.

And with certain data sets that are non-random, my intuition says, there is not even correlation but only relation.

You can't say that the number of pairs of elements within the set CAUSES the number of A's within the set. That's very strange.

What people don't understand is that relations (e.g. correlations, causal relations, etc) exist WITHIN information and not OUTSIDE of it.

Information isn't caused by anything. It is in no relation with anything at all. It simply is. It occurs. It is given. For no reason at all. Rather, it is what is WITHIN information that is in relation with stuff that also must be WITHIN information. In other words, relations only exist between the constituents of information.

Relations can only exist between what is EXPERIENCED.

There can be no relation between something that is EXPERIENCED and something that is not.

That is MEANINGLESS.

When you say "everything has a cause" yet for certain events you see no causes then what part of your experience is the cause of that event? None, right? Because you see none. So you must say "it is something beyond" which is meaningless.

I got a philosophy degree, I'm not upset that I can't find work as a philosopher. It was my decision, and I knew that it wasn't a money making degree, so I get money elsewhere.

-- Mr. Reasonable