0. Introduction to Data Stream Dynamics

Recapitulation & a New Question

2: Articles
3. Sections
4. Paragraphs

Are you content with the materialist perspective of traditional Science?

Are you content with being characterized as a sack of atoms that obey the invariable laws of Physics? Are you comfortable with the notion that we humans are just a collection of neural networks or perhaps a sophisticated electrical device with no real motive power of our own? Or have you tired of the materialist perspective of traditional science that turns humans into mindless automatons? Are you instead interested in a new mathematical perspective that addresses the innate nature of human experience? If so, read on.

1st Volume: Are there verifiable Patterns of Correspondence between the Living Algorithm & Life? Yes!

Triple Pulse Studies, the 1st volume in this series, asked the question: Are there verifiable Patterns of Correspondence between the Living Algorithm's mathematical behavior and living behavior? The answer was a resounding 'Yes!' The striking correspondences evoked a question: Why is there a correspondence between mathematical behavior and human behavior? Because traditional perspectives provide no explanation for these cognitive mysteries, we decided to consider the possibility that living systems might employ the Living Algorithm to digest information. Determining the plausibility of this hypothesis was the focus of The Mathematics of Living Systems, the 2nd volume of this series.

2nd Volume: Could Life employ the Living Algorithm to digest Information? Yes!

We speculated that if living systems employ a mathematical system to digest the constant flow of environmental, that this data stream mathematics must provide up-to-date information that is both descriptive and predictive. Further this mathematics must address immediacy, relationship, and the potential for informed choice. The traditional mathematical systems of Probability and Physics could not fulfill these stringent conditions. In contrast, the Living Algorithm’s mathematical system is able to fulfill all the specified requirements of a data stream mathematics of living systems. These results lend credence to the hypothesis that living systems employ the Living Algorithm, or something very much like it, to digest data streams. In other words, it is plausible that the Living Algorithm is the mathematics of living systems.

This Volume: What is the causal mechanism linking the behaviors of Life & the Living Algorithm?

The 1st volume addressed the question: Is there any empirical evidence validating the patterns of correspondence between the Living Algorithm System and living systems? The 2nd volume focused upon the question: Is it plausible that living systems employ the Living Algorithm to digest information? The answer to both questions is ‘Yes’. Yes, there is empirical evidence that validates the patterns of correspondence. And yes, it is plausible that Life employs the Living Algorithm to digest information. Yet, there is another chain of implicit questions that remain unanswered: Why are there patterns of correspondence between the two systems? Even if Life employs the Living Algorithm to digest information, why would living systems ‘choose’ to emulate the Triple Pulse and the Pulse of Attention? These are just a few of the Living Algorithm’s myriad transformation. What makes them special? Why would evolutionary mechanisms select for these transformations above all the rest? In other words, what kind of causal mechanism links human behavior with the mathematical behavior of the Living Algorithm System?

Probability reveals static states, not the dynamics of causality.

In the previous volume, we provided evidence that the dynamical relationships of a system reveal causality, not the static conditions. Probability’s specialty is characterizing the static nature of fixed data sets. As such, Probability can accurately characterize position, but can reveal nothing about dynamics. Because of this inability, probabilistic analysis tells us little, if anything, about causality. Causality, which answers the question of why, requires an explanation based in dynamics.

The dynamics of Physics & the Living Algorithm reveal causality.

Mechanics is a system of dynamics that accurately characterizes the behavior of material systems. The Living Algorithm, we hypothesize, generates a mathematical system of dynamics that characterizes the behavior of living systems. The two dynamic mathematical systems provide plausible causal mechanisms for the systems they describe. In other words, Mechanics reveals why dead matter behaves the way it does. In similar fashion, we speculate that the dynamics of the Living Algorithm reveals the causal mechanisms behind significant features of living behavior.

This volume focuses upon the dynamic symmetry between the 2 mathematical systems.

Theorists have completely delineated the dynamical structure of Physics. What about the Living Algorithm? What kind of dynamical structure does the Living Algorithm have? Interestingly, the internal structure of each system of dynamics is identical. Establishing the dynamical symmetry between the two systems is the focus of this volume. Understanding this symmetry will provide a plausible answer to the question: Why are there patterns of correspondence between Life and the Living Algorithm? In other words, what is the causal mechanism that links these disparate systems?

Preparing the Mental Ground

To prepare the mental ground for the radical notions that follow, let’s first provide a little background information. As we will see, classical Newtonian Physics (Mechanics) and the Living Algorithm share a common dynamical structure. While Physics characterizes the dynamics of matter, the Living Algorithm characterizes the dynamics of data streams. Spawned by their common obsession with change, the two systems are based upon similar principles. Their common structure combines the constructs of Newtonian dynamics: including space, time, matter and energy. Physics applies these constructs to matter; the Living Algorithm applies them to information. Biological systems consist of matter and digest information. Accordingly, it takes two complementary systems to accurately characterize the behavior of living matter. These 2 systems share a common dynamical structure.

Necessary to 'explode' Newtonian Constructs: mass, time, space, and energy

While the 2 systems share a common dynamical structure, the characteristics of the components are unique to their respective systems . For instance, space and time are continuous in Newtonian Physics, while, as we shall see, space and time are incremental in the Living Algorithm System. In Physics, material mass is permanent. In the Living Algorithm’s information system, mass is ever changing. In the closed system of Physics, energy is conserved. In the Living Algorithm’s open system, energy is in a constant state of decay and needs to be continually replenished.

Necessary to put Preconceptions aside to understand the Dynamics of Information

To understand the common dynamical structure of the two systems, it is necessary to focus on the essence of these fundamental principles. We cannot allow ourselves to get stuck in the superficialities of the surface. For instance, if we allow our preconceptions about mass (its permanence) to dominate our thinking, it will be impossible to understand the notion of mass in the dynamics of information. If, however, we put our preconceptions aside, we will see that the essences of space, time, matter, and energy expand to fill an entirely different part of the Universal Flux. (For a background check, see ‘Exploded’ Newtonian constructs bridge Matter & Life.) The following article begins the support of this claim. In the process, we will also lay the foundations for Data Stream Dynamics, which provides the causal mechanism behind the Living Algorithm System.

Dynamical structure of Physics crucial for an understanding of Info Power & Energy

This discussion raises an interesting and obvious question: Why even claim any kind of similarity between the dynamics of the 2 systems, if these constructs are employed in such dissimilar ways and must be ‘exploded’ to have any relevance?

'Exploding’ these traditional Newtonian constructs is essential for an understanding of the concepts underlying Data Stream Power and Energy. The notions of Energy and Power are essential components in providing a causal mechanism. The only way to make sense of these composite constructs from Physics is by similarly 'exploding' the traditional notions of space, time, and matter. Even though the altered notions of these seemingly objective and fixed elements are variable and subjective, the deep insights of theorists into the nature of dynamics provide essential explanatory tools for the correlations between human behavior and the Living Algorithm System. This connection between Info Dynamics & Material Dynamics, as unusual as it seems, some might even claim strained, is crucial to an understanding of this amazing math/behavior linkage that defies current common sense.

Time to get to know the Living Algorithm's mathematical Essences, her Algebra

Due to the complexity of the topics, we must take it step by careful step. To lay an adequate foundation for our discussion, it is first necessary to examine the mathematical essences that drive the Living Algorithm’s digestion process. We’ve seen her in action; it is time to explore the mathematics of the Living Algorithm’s algebra.

Basic Operations of Arithmetic: No proofs or computations

For those of you who are mathematically challenged, this discussion of the Living Algorithm’s algebra is verbal, rather than technical – no proofs or computations. That occurs elsewhere. Further the Living Algorithm only employs the basic operations of arithmetic. Nothing esoteric. No mathe-magic. This discussion lays the foundation for Data Stream Dynamics. To understand the algebraic essences that drive the Living Algorithm, read the next article – Living Algorithm Algebra.

For a lighter touch, check out our allegorical world. See why Life wants a Causal Mechanism.


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