Home Science Page Data Stream Momentum Directionals Root Beings The Experiment
Every Data Stream has certain characteristics independent of what it refers to and has a momentum independent of reference. Because of this predictions can also be made independent of reference. The Data Stream assumes an existence independent of its source. (The Random Data Stream has no source, of course.) This section explores the interaction between Data Streams. We will look at their interacting measures.
Before moving on we need to stress that the Data of a Stream is ordered. This has been implied but not explicitly stated. In order to compare Data Streams, effectively, one must compare measures, which are based upon the same Duration and same time period.
A few points need to be made before proceeding to the interaction of Data Streams. First, because a Data Stream is a flow of data, an individual impact is not enough to really change much. We've defined Force as a function of Data Density and Acceleration. So far Acceleration has only been defined as an individual event. Hence Force is also individual event. This is perfectly valid in a physical setting. One impact will change the momentum of a physical object, forever. This is not true of a Data Stream, however. It takes a collective impact to change the momentum of a Data Stream.
Below is the mini-derivation of a collective measure, the Current, the average Force, or the Data Stream Density times the collective acceleration. An individual acceleration has already been derived. Its equation is below.
The Current is based upon the average acceleration. The average acceleration is the sum of all the individual accelerations divided by N, the number of Data Stream elements.
Remember that a contextual definition of an average was derived above and repeated below. This mean average is based upon the New Data and the prior average, rather than all of the N Data Bytes. All the prior, N-1, Data Bytes are contained in the prior average. Hence their individual values need not be retained to compute the New Average.
In a similar way the average acceleration or Current of our Data Stream can also be computed in a contextual manner. This is shown below. {This will become a very important equation in the Notebook Data Stream Derivatives.}
We've defined Force and Acceleration on an individual and on a collective level. This is nothing revolutionary. "We only averaged 50 miles per hour on our way to Phoenix. For some stretches we averaged 65 miles per hour and then through Los Angeles because of traffic jams, we almost came to a dead stop. On some empty stretches in Arizona, we even hit 80 MPH. We stopped once for lunch and only had a few rest stops." This common statement expresses a long-term average in terms of some short-term averages with some irregular peaks and valleys. If the trip were continued across the country, miles per day could be computed as an average velocity. On a trip across the country, the individual peaks and valleys fade into insignificance before the miles per day velocity. Many people would be more interested in how many days it took to get across the country than in how fast the driver went for individual stretches. This indicates how many times the collective measures have more density, more meaning, than do individual peaks.
Physically, a change in momentum over time is called Force. We will call the same measure, Force, in Data Stream terminology. When the Data Stream Momentum changes then this is Force. Beware of brain puns, however. Physical Force and Data Stream Force, while based upon similar concepts, have some major differences. Because of this we will call the Force of a Data Stream its Current, to avoid ambiguity.
Force is defined as mass times acceleration. Current is defined as Data Stream Density times Data Stream Acceleration. Newtonian mass is fixed, while acceleration can change. Data Stream mass and acceleration are both variable. As a matter of fact, many times, the greater the DS acceleration, the less is the DS Mass. This is because if the acceleration becomes greater then the Realm increases and the DSDensity, the mass, decreases.
Another major difference is that the measures that determine physical Force are immediate and direct, while the measures that determine Data Stream Current are composite, i.e. they are built upon what went before. Because of this, the Impact of an individual Data Byte upon a Data Stream will not cause a permanent change in direction and momentum. It is only the Impact of a collection of Data Bytes that will cause a change in Data Stream Momentum or Flow. In the physical world, however, one individual force will permanently change momentum unless another force acts upon it. This physical analogy of Force is perhaps why some elements of society believe in instant conversion. {See Spiral Time Notebook.}
As pointed out before, the DS Force, the change in Data Flow or Momentum, can come from at least three sources or a mixture. The analysis of the Data Stream only identifies that a force exists but doesn't tell you what the source of that force is. It never can. Additionally identifying a change in physical momentum can be quite precise and immediate, while identifying a momentum change in Data Streams is quite difficult. It takes a series of Impacts to change a Data Stream. Even then it could revert right back.
We've spoken about Data Stream Quakes in terms of a sequence of large impacts. We've looked at some collective measures, which can detect these Data Stream changes. The size of the Current of a Data Stream determines the size of the Forces working upon the Data Stream from outside, inside, or in-between. (Remember that only Live Data Streams interact with the Source.) The outside forces come from the Environment; the in-between forces come from the Experimenter, i.e. Data Collection changes. We will ignore the outside and in-between forces because they are outside the power of our subject to control.
We will instead focus upon the Will bubble in the Diagram above, breaking it into parts. These are the internal forces. They are of two kinds, intentional and reactive. The intentional internal forces are examples of conscious Will. There are two types of intentional force. One internal force goes with the Flow of the Data Stream, augmenting the movement with intentionality. The organism consciously catches a Data Stream wave. The other internal force moves independently of any Data Stream Derivative. This is Pure Will, acting independently of Data Stream Momentum. Many social scientists minimize the importance of this Force, ignore it entirely, or pretend it doesn't exist.
This type of Force needs to be cultivated in order to break the power of negative Data Streams. The Data Flow or Momentum only exerts a virtual Force upon the subject. The Data Flow is like a habit, good or bad. If it is a good habit, catch the wave. If it is a bad habit, stop the flow. The Data Stream urges to overeat, over drink, or indulge in any excessive behavior are only virtual. Although no one is forcing the subject to continue his life patterns, this virtual force seems to exert a tangible influence. Recognizing the illusion of this supposed force is the first step to diverting or controlling its influence upon one's behavior, which is one's life.
This brings us to the reactive internal force. This also comes in two forms. One form of reactive force is the subconscious response to Data Stream Derivatives and their patterns. Sometimes this is a nice Force if it is a positive Data Stream, however if it is a negative Data Stream, this internal reactive force can be quite destructive. This force makes the organism a victim. If this is a major force, then the individual just repeats negative behavior patterns repeatedly. He is victim of his lack of Pure Will and his lack of consciousness. The first step to breaking the power of these Data Flows is by becoming conscious of them. Then the organism can choose to ride the Wave or break its Flow through an exertion of Pure Will.
The other internal reactive force is due to the principle of conservation of time. If one Data flow increases or decreases for any reason at all, then the rest of the Data Flows, in combination or individually, must shrink or grow accordingly. This force is automatic and inflexible. It should be taken into account when looking at the whole picture. It is important when exerting intentionality, either catching or breaking the Wave, to realize that the entire web of Data Streams will be affected in some way. No change in Data Flow acts independently of the rest.
The Will Bubble above is expanded below. It is very hard to get inside the bubble to break down which influence is which. Increasing consciousness through meditation helps shift reaction to intentionality.
The actual force is mostly a combination of internal, external and in-between forces.
This brings us to the topic of this section, interactive Data Streams. We've talked extensively about individual Data Streams and the forces affecting their Flow or Momentum. Now we will speak about the interactive forces between Data Streams. The last type of internal force, reactive and equal/opposite, reflects the interaction between Streams. We are speaking about a closed system. One Stream's measures go up, another's go down. Which Stream or Streams are affected by the change in another Data Stream? The measures for potential impact cannot be used in comparing Data Streams because the potential impact of the elements is all the same. The measures for real impact cannot be used in comparing Data Streams because the values are only relative to a specific Stream. The main way to compare Data Streams is through proportional impact or Z scores. The Z scores of each New Data Byte are measures of how out of the ordinary the New Data Byte is. The Z scores of different Data Streams can easily be compared because the Z score is a relative figure not an absolute figure. It relates to the degree of change.
Traditional Correlation between Data Sets is defined as the average of the products of the Z-scores of corresponding Data Bytes from the Data Sets. Remember the definition of the Impact or Z-score. ItŐs the ratio between difference between the New Data Byte and Prior Average and the Prior Deviation.
The following is the traditional definition of the correlation between Set X and Set Y, i.e. the average of the product of their Z scores.
(Equation 12.4, p. 208, Introduction to Statistics for the Behavioral Sciences, Hardyck & Petrinovich, 1969).
The following equation for correlation between 2 Data Streams is the same as before except that the Correlation has a subscript. This acknowledges that we have a running correlation based upon a continuous flow of Data rather than a fixed set of Data that is complete.
Below is a notational simplification, for the product of the Nth Z scores from Data Streams X & Y.
Remember the contextual equation of an average that we have used before.
Applying the contextual equation to the averaged Z-score products, we come up with the following equation for the Nth correlation of two Data Streams, X and Y.
Below is another notational simplification. In our Data Stream system, the bar across the top indicates an Average. Without a bar indicates an independent reading.
This is the correlation equation written with the new notation.
By looking at this figure one can determine relations between Data Streams. One can get a negative, positive, zero, or no correlation. {See Zero Correlation Notebook for the difference between zero and no correlation.}
In traditional Newtonian physics for every action there is an equal and opposite reaction. This equality is at the root of many physics vector diagrams. Cause and Effect are mixed up even here. In traditional commonsense thinking, I throw the ball. I am the Cause. The motion of the ball is the Effect. In the Data Stream system, we are working with a closed system hence when there is any change in any action there is an equal and opposite change in another action or group of actions. Example if the subject sleeps three hours less then he is awake three hours more.
The Cause and Effect are quite identifiable in common sense physical phenomenon. It is easy to say that because the Sun went down below the horizon that it gets dark, because the Sun is the Cause of the Effect of Light. This is not at all true in Data Stream phenomenon. Just because one Data Stream goes down and another goes up, doesn't mean that the one caused the other. In a bound system, when one of the complete set of Averages rises, then another must fall, because the sum of the complete set of Averages equals the Duration, a constant, by definition. Thus even if we have a high correlation between two Data Streams, we do not know which was the cause and which was the effect. Nor do we need to know.
Hence Newton's first two laws have an analog in the study of Data Streams. The change in momentum identifies a force in both systems. Also for every change in DS Average there is an equal and opposite change is the rest of the complete set of DS Averages. So equal and opposite also applies but in a little different way.
Because the world we have created is the world of Probability, we have left the world of cause-effect and have entered the world of quantum logic. We can see and describe what happens in Data Streams. We can even make accurate predictions. But we canŐt get inside the clock to see how it works. We must be content with observation, prediction, diagnosis, and even prognosis. But we cannot say, nor do we need to say, what caused the effect. Was it a Hormone, a malfunction of the liver, or perhaps the neural net misfired? These questions have no significance in the world of Data Streams. They are orthogonal planes, which go right thru' each other and yet still have an effect on the organism. See Diagram below.
Although the Cause-Effect Plane and Data Stream Plane are in a dimensional relation to each other, their common effect is experienced as a flat blend, a mixture of the two. See the Diagram Below.
We cannot say that the new Data caused an effect. But we can say that the new Data reflects a Force acting upon our Data Stream if the new Data Bit falls consistently outside the realm of probability. Although we can identify that a Force must exist which has changed the momentum of our Data Stream, we cannot identify the force. You are pinched. You turn around to see who has pinched you. You can find out who pinched you. You can easily find out the Cause of the pinch. In our probabilistic case, however, the New Data pinches you. You turn around and there are always at least three different Forces, which could have caused the Effect. As pointed out before, the Force could come from an external source, an internal source, or an experimental source. Someone may have pinched you, you may have pinched yourself, or the ghost of the experimenter may have pinched you, causing an effect from outside the experimental environment.
Any time the physical momentum of a physical body is changed then a force outside itself has caused this effect. No ball or projectile changes its course through internal means. Although we suppose it is physically possible for all the molecules of a ball to change its course, working together, it is highly improbable, practically speaking, statistically impossible. This is not true behaviorally however. The cause-effect interplay disappears inside the bound system.