Glossary: A>F

Glossary A>F: definitions, descriptions and links of the diverse variety of terms employed throughout the multiple articles devoted to Information Dynamics.

The Active Pulse is the name for the *Pulse* when it occurs in the *Triple Pulse*. It is first and last pulse of this alternation of pulses. The graph below is a visualization of the Triple Pulse, which contains both the Active Pulse and the *Rest Pulse*.

The Triple Pulse is a result when the *Living Algorithm * digests a specific type of data stream. The standard sequence consists of an uninterrupted string of 120 ones, followed by an uninterrupted string of 120 zeros, followed by another string of 120 ones. The number string of 1s is associated with the Active Pulses, while the number string of zeros is associated with the Rest Pulse. However, the Active Pulse only occurs at the beginning of the data stream or if it follows a Rest Pulse.

The Living Algorithm process reveals the rates of change (derivatives) of a data stream. The Triple Pulse is a visualization of the Living Algorithm's 2nd derivative – the *Directional*. Experimentation combined with analysis has associated the Active Pulse with a creative session, consciousness, and the momentum of attention. In regards to *Information Dynamics* theory, it is also called both the *Creative Pulse * or the Pulse of Attention.

"In mathematics and computer science, an algorithm is an effective method expressed as a finite list for calculating a function. … In simple words, an algorithm is a step-by-step procedure for calculations. … Giving a formal definition of algorithm, corresponding to the intuitive notion, remains a challenging problem." (Wikipedia)

Some common examples of simple algorithms are the '*step-by-step procedures*' that we learn in elementary school to add, subtract, multiply, and divide large numbers. The *Living Algorithm *of *Information Dynamics* only employs these simple arithmetic processes. The name 'Living Algorithm' is based upon the '*intuitive notion*' of algorithm being a '*step-by-step procedure*' for determining an answer.

Attention is a *theoretical primitive* of *Information Dynamics*. As such, it is an essentially undefinable notion. Every logical system contains these theoretical primitives. 'Set' of mathematical set theory is another theoretical primitive, i.e. undefinable. As such, we must appeal to intuition and suggestion to determine the meaning of attention.

Generally speaking, most of us sense that attention is the ability of more complex life forms to focus awareness upon
the world external to themselves. Attention corresponds with this idea in Info Dynamics theory. Attention turns 'on'
when we are conscious, i.e. awake, and turns 'off' when we are unconscious, i.e. asleep. According to Info Dynamics theory, when Attention is 'on', we take in and digest new information via the *Living Algorithm*. Conversely, when Attention is 'off', we cease taking in information in order to finish the digestion process.

Due to its association with the Living Algorithm's digestion process, Attention has some special features. For instance, *Sustained Attention* over a distinct duration is required to have an *Experience*. This property is due to Attention's linkage to the *Pulse*, one of the innate mathematical forms of the Living Algorithm. According to the theory, an Experience is an essential requirement for the assimilation and integration of information. Living systems employ this knowledge to fulfill potentials including survival.

For more, check out the treatise: *Attention, Life & the Living Algorithm*.

The Formula for Attention, at right, encapsulates a basic premise of *Information Dynamics* theory in a single equation. In brief, the Attention Formula is a mathematical process that exhibits the requirements for how inert Information is transformed into useable Knowledge in living systems. The equation makes many testable predictions. Common experience and experimentation have validated many of these predictions. (For a partial list, check out the glossary entry – *patterns of correspondence*.)

Let us verbalize the formula. When Attention (circle A) is sustained upon Information (I) for a sufficient duration, it generates an Experience (circle E), whereby the Information is transformed into Knowledge (K), i.e. a change in the neural networks. The Directional's information digestion algorithm (§) drives this entire process.

Each element in the equation has a tightly defined mathematical equivalent. The symbol '§' represents the computational process. '§' is the Directional operator. The *Directional* is an equation that generates a data stream's directed acceleration at each moment. In the case of the Attention Formula, the Directional only operates upon data streams consisting of identical elements and with a specified length, i.e. Pulse Strings. The Directional Process is repeated enough times to generate a Pulse of Acceleration from these Pulse Strings.

'I' represents the content, i.e. the Information contained in the Pulse Strings. 'Circle A' represents a Pulse of Sustained Attention, i.e. a Pulse String of 1s. 'K' represents the Information once it has been transferred to the neural networks via the Directional's information digestion process. 'Circle E' represents a Pulse of Acceleration.

In the Attention Formula, the Directional operates on a one or more Pulse Strings and generates an equivalent number of Pulse Types. In other words, the answers to the Attention Formula and its derivatives are processes. A data stream of numbers represent these processes. In contrast, the summation operator '∑' adds up a specified string of numbers to yield a single numerical result.

For a more complete discussion of the elements, the context, and the testable predictions of the Attention Formula, check out the article *Mathematics of Attention*.

The Pulse

The ideal Pulse is a result when the *Living Algorithm * digests a data stream consisting of a sufficient number of consecutive 1s (120 in the graph at the right). In the *Creative Pulse Notebook,* the Author develops the notion that the intriguing curve to the right represents the momentum at each *moment* in the data stream. In this sense, it could be said that the Pulse represents the momentum of a data stream that contains 120 consecutive '1's. As the momentum of the uninterrupted data stream of 1s grows, the pulse rises to its maximum peak. As the data stream's momentum falls, the curve gradually diminishes, and then ultimately zeros out. Because of the simplicity of the data stream, just 1s, the Author refers to the Pulse as the Living Algorithm's primary mathematical form, i.e. energy pattern.

This model was originally chosen to represent an uninterrupted creative session. Due to the many patterns of correspondence between the Pulse and a productive session, the original name of this flowing curve was the Creative Pulse. It was much later that he realized that this name was both misleading and limiting. Instead of only applying to creative sessions, this mathematical model seems to apply to a broad and significant range of human experience. And instead of being related only to creativity, the mathematical pulse seemed to be related to a natural attention span. It was further supposed that this natural attention span, when uninterrupted, lay the foundation for a productive and creative session. In other words, the mathematical curve shown above seemed to be more related to attention and less related to creativity. With this realization, the name was changed from the Creative Pulse to the Pulse of Attention.

For a more detailed exploration of this significant curve, check out *The Pulse of Attention, an Introduction*, *The Pulse of Attention, the Graph*, and *The Pulse of Attention Interrupted*.

The original name for *Information Dynamics*. See this glossary entry for a deeper understanding of the meaning of the phrase. Following is a brief explication of the motivation behind the name change.

Information Dynamics was originally characterized as a *proto science* devoted to the study of living behavior from a mathematical perspective. The author instituted the name change after realizing that the theory is based solely upon the mathematics of the *Living Algorithm*, a system of digesting information. In other words, behavior is subservient to the dynamics of information flows. These information based theories intersect with the *orthogonal* planes of physics, chemistry, biology,
and psychology.

The Living Algorithm has been renamed the *Living Algorithm*. Check out this glossary item for the meaning of the phrase. We changed the name because the process has nothing to do with biological cells. Further, it is a general algorithm, rather than a specific equation.

The *Living Algorithm*, a.k.a. the Cell Function, digests any data stream. The digestive process produces the rates of change (the derivatives) that describe the data stream's ongoing *moments*. To facilitate understanding we characterize these data stream derivatives as a family of ongoing averages (central measures). The traditional mean average is the ancestor (the *Living Algorithm * without decay). The data stream's 1st derivative, i.e. the Living Average, a.k.a. the Living Average, is the mother. In this role, she gives birth to 2 children, a son and a daughter. These are the data stream's 2nd derivatives. The brother in the family is the Deviation, a sheer magnitude without direction. The sister is the Directional, a directed magnitude. The Directional, in turn, gives birth to the Liminals, the grandchildren of the family. These are the data stream's higher derivatives. Their relationships are shown at right. Check out the article *Living Algorithm Family* for more information.

The mother (the Living Average) indicates the probable location of the next point in the data stream. The son (the Deviation) indicates the range of variation of this prediction, while the daughter (the Directional) reveals the recent tendencies of the data stream. As a group, they are called the *Predictive Cloud*. Because of their descriptive and predictive capabilities, the Predictive Cloud is very useful to living systems.

Interpreting graphs of this nature requires a special footnote. The values in this particular set of graphs represent a different way of thinking about what are traditionally known as the x and y-axis. The values on the horizontal x-axis represent the number of iterations (repetitions) in the digestive process of the *Living Algorithm*. As such, the x-axis consists of whole numbers only. Dividing iterations into fractions would be nonsensical. With each iteration of the Living Algorithm process, a new reading is taken and plotted on the graph. The dimensions of the vertical y-axis reflect the values contained in the data stream.

This set of graphs are also unique due to the Living Algorithm's innate nature. The Living Algorithm is not a regular equation, but is instead a recursive function, i.e. iterative and self-referential. There are 3 elements of the Living Algorithm process that characterize the non-traditional nature of these graphs. Initially we note that the substance of the digestive process is unique in that it consists of an ongoing data stream. The process of the Living Algorithm is also unique in that it is both iterative and self-referential. It is iterative in that it is a repetitive process – with each iteration, a sample of the ongoing data stream is taken. This process becomes self-referential because it relates the present sampling to previously digested data.

A mathematical system based in the *Living Algorithm's* method of digesting data streams.

The Living Algorithm is a recursive function – an interative equation that refers to itself. In other words, the Living Algorithm's digestive process is self-referencing and is repeated over and over again. Each repetition (iteration) of the process produces the rates of change, i.e. the derivatives, of the current instant/data byte. This is a *moment*. If the information contained in these moments is consistent over a long enough duration, it generates an *experience*. Mathematically, this is a pulse of momentum. We have deemed this the *Pulse of Attention* because the mental energy of sustained Attention is required to transform moments into a life-changing experience. These are all features of the Living Algorithm System.

The *Predictive Cloud* is a particularly signficant feature of this mathematical system. These are the cloud of derivatives (rates of change) that surround each data point - the *moment*. This feature is of particular significance because the Living Algorithm's Predictive Cloud provides data stream descriptors that provide future estimates. This feature is extremely useful to living systems on a moment-to-moment level. As such it provides an evolutionary advantage. For a more in depth coverage of these topics, check out the *Living Algorithm System* and *Evolutionary Potentials*.

These articles are part of a larger Notebook, *Mathematics of Living Systems*. The primary purpose of this article stream is to establish that living systems could employ the Living Algorithm to digest sensory data streams. In the crucial role as Life's operating system, the Living Algorithm's innate structures also provide the necessary boundaries that determine what forms that biological systems could take – a grammar of Life. In this role, the Living Algorithm didn't just provide a requisite talent, but actually provides the limits that are essential for the growth of complexity in living systems.

The Living Algorithm's digestive process generates a system of dynamics - data stream dynamics. This dynamical system provides some of these innate structures of the Living Algorithm System. Data stream dynamics shares a nearly identical underlying structure with physical dynamics. Just as physical dynamics governs the material world, the data stream dynamics governs the world of living information. These ideas are developed in the Notebook – *Data Stream Dynamics*.

The Living Algorithm System is pure mathematics. The *Information Digestion Model* is a conceptual model that was developed to make sense of the synergy between the Living Algorithm System and empirical data/*scientific 'facts'*.

These words have simple meanings in the context of *Information Dynamics*. When an organism is* conscious*, it takes in information. When an organism is *unconscious,* it does not take in information. The organism's *subconscious * processes digest and organize information. This process can happen or not during both the state of consciousness and unconsciousness. Unconscious cognition is the word that cognitive science employs instead of subconscious processes.

The many correspondences between the mathematics of *Information Dynamics* and the empirical reality of living systems 'forced' us to make some bold assumptions. These assumptions are radically different from the underlying assumptions of behavioral reality that are currently in vogue. A prevalent scientific mindset is that the mental world is almost totally subservient to the physical world. Indeed some, if not most scientists, might say that Mind is a subset of Body with no real autonomony whatsoever. Accompanying this mindset is the notion that choice is but an illusion. It is exciting to present a powerful explanatory theory that is based upon the alternate assumption that Mind and Body are equal players and that Mind has the ability to choose a course of action.

Following are several prime assumptions (core postulates) underlying Info Dynamics theory.

1. Living systems consist of both a material and information-based component (Body & Mind?).

2. These components are autonomous yet interacting systems. They suggest the notion of intersecting *orthogonal* planes.

3. Living systems digest information via the *Living Algorithm*.

4. By revealing the dynamics of the moment, the information digestion process relates transforms data streams, environmental and otherwise, into meaningful knowledge that enables the living organism to fulfill potentials.

5. The mathematical method of information digestion also shapes and limits human behavior.

(Check out the article *Core Postulates* for a more detailed analysis of why we were forced to make these assumptions.)

The Creative Pulse, the original name of the *Pulse of Attention*, is a result when the *Living Algorithm* digests a data stream consisting solely of 1s. In the *Creative Pulse Notebook* the Author develops the notion that the intriguing curve to the right represents the momentum at each *moment* in the data stream. In this sense, it could be said that the Creative Pulse represents the momentum of a data stream that contains 120 consecutive '1's. As the momentum of the uninterrupted data stream of 1s grows, the pulse rises to its maximum peak. As the data stream's momentum falls, the curve gradually diminishes, and then ultimately zeros out.

The Author performed a computer experiment on this ideal Creative Pulse. He sensed, from his own experience, that interruptions to his creative sessions seemed to have a disproportionate negative impact. He deems this the * Interuption Phenomenon. *As it was based upon a data stream consisting of an uninterrupted series of 120 1’s, the Creative Pulse model was chosen to represent an uninterrupted creative session - hence its name. To simulate interruptions to a creative session, the Author altered the ideal data stream by introducing interruptions in the form of 0s at a variety of locations along the stream. He called this series of manipulations the

Other articles concerning the Creative Pulse include: * Applications and Elements*.

An Excel spreadsheet that employs the *Living Algorithm *to compute the derivatives of any data stream. This program also generates the all-important graphs for *Information Dynamics*.

A Data Set consists of a static number of clearly defined data points. Data Sets are the substance of the mathematics of Probability. Probability utilizes fixed and invariable central measures, such as mean average and standard deviation, to characterize data sets.

In contrast, a Data Stream is a growing set of ordered Data points. This distinguishes it from a Data Set, with its static set of Data points. In order to process a data stream, Probability must treat it as a fixed Data Set. The *Living Algorithm * treats the data stream, not
as a fixed set, but as a living set. It does so by computing the ongoing derivatives of any information flow.

For a more thorough discussion
check out the *Data Stream Momentum Notebook*.

Data stream momentum is a mathematical method for determining the momentum of a flow of information and the attendant inertia to changes in that data stream. In other words, the method examines the pattern of energy in a series of * moments* as a way of analyzing the strength of a habit. As such, it is a way of quantifying the energy or strength of habits.

*Data Stream Momentum* is also the name of an entire notebook devoted to the mathematical development of the concept. Because of the presumed importance of this notion, a *collection* of notebooks from the Author's creative obsession of 1994 was given this same title.

The Decay Factor is a highly significant feature of the *Living Algorithm*. The most basic form of the algorithm, the *Living Average*, is shown at right. As the name indicates, D, the Decay Factor, determines the rate of decay in the equation. They are inversely related. The higher the Decay Factor, the slower the rate of decay; and vice versa.

Although D can be any positive value, we can divide the Decay Factor’s continuum into 5 pragmatic sectors:

instantaneous(D=1)

volatile(D=2 through 9)

stable(D=10 through 20)

sedentary(D=20 through 50)

eternal(D greater than 50).

The *Decay Factor* article illustrates the effects of different ranges of D upon a single data stream. In brief, the Living Algorithm’s Decay Factor is inversely related to the shapeliness of the contours of the Data Stream Derivatives. Decay Factor up; Contours down. And vice versa. (Note: When the Decay Factor is set at 1, the *instantaneous* range, there are no rates of change, hence no data stream derivatives.)

If living systems employ the Living Algorithm to digest information, they could also have evolved to take advantage of the Decay Factor's talents. If so, then living systems could regulate the Decay Factor to shift between specific and general attention, the two phases of the well-established *Posner Attention Model*. We further suggest that general attention is the default mode and that it requires mental energy to move the Decay Factor down into the specific attention range. The default mode could be a way of conserving mental energy for other endeavors.

In the article on the *Sleepiness Phenomenon*, we argue that it even takes mental energy to maintain the high Decay Factor that is required for general attention. As this mental energy is depleted, the Decay Factor begins rising into the *eternal* range. The environmental contours become flat and we eventually go to sleep. Sleepiness is a sign that our mental energy is insufficient to regulate the Decay Factor any longer.

The Living Average is the 1st derivative of a *data stream*, as computed by the *Living Algorithm*. It is similar to a 'running average'. As such, it is not a single point, but a data stream.

The equation is shown at right. It utilizes the Xs with subscripts (X_{N}) and bars () to identify the Living Average. Subscripts indicate the Living Average's place in the data stream and the bar indicates that it is an average rather than an individual data point.

The *algorithm* for computing the Living Average is a simple procedure.

- 1) Determine the difference between the most recent data point and the existing Living Average.
- 2) Scale this difference proportionately.
- 3) Add or subtract the scaled difference to the existing Living Average. (Add if the data point is larger than the existing Living Average. Subtract if the data point is smaller than the Living Average.)
- 4) The result of this process is the new Living Average.

This simple procedure turns the existing Living Average into the previous Living Average and, in so doing, produces the next Living Average in the data stream. This process continues with the input of each new data point, relegating each Living Average into a proportional part of the past Living Average. Check out the *Living Average Notebook* for more details, including basic applications and fundamental rationale.

A few more points of note:

When the Living Algorithm is applied to the Living Average data stream, it produces the second derivatives of the original data stream. One is a sheer magnitude, a scalar, while the other is a directed magnitude, a vector. Like the Living Average, these are also data streams.

*Deviation* is the name of the scalar derivative, as it indicates the ongoing range of the original data stream. *Directional* is the name of the vector derivative, as it indicates the ongoing tendencies of the original data stream. As a group, these three most basic data stream derivatives, including the Living Average, are called the *Predictive Cloud*.

The reason for the name is straightforward. The trio of measures provide immediate descriptors of any data stream that can be employed to predict the probable location of the next point in the stream. We argue elsewhere that if living systems were to employ these ongoing predictive measures, it would provide a crucial evolutionary advantage.

The Living Average and the traditional Mean Average, while sharing some similarities, have some distinct differences. Both are averages and even have a similar equation. A single Mean Average characterizes a *data set*, no matter how large it is. This number describes the entire data set. In contrast, it takes a data stream of Living Averages to characterize a data stream. This averaging of the ongoing flow of information weights the most recent data points more heavily, while proportionately diminishing the weight of past data points. As such, it best describes individual moments, not in the entire data stream. As such, the Living Average is applicable to the data stream's context, while the Mean Average is applicable to the data set's context.

On the most fundamental level, a derivative is a rate of change. It is called a derivative because it derives from some kind of change that occurs over time. This time can be real or mathematical, in the sense that it is an abstraction. Derivatives also change over time. As such, it is possible to also compute a higher-level derivative to characterize this rate of change. For example, a car's acceleration, the 2nd derivative, is the rate of change of the car's velocity, the 1st derivative. Because this process can be extended indefinitely, there are an infinite number of derivatives.

The *Living Algorithm’s* sole function is to produce the rates of change/derivatives of a data stream. However, these derivatives are not like the traditional derivatives of calculus. A basic difference is that data stream derivatives are discretized, i.e. broken into discrete parts, while the derivatives of calculus are continuous. As such, data stream derivatives do not require the mathe-magic of calculus, but instead only require common sense arithmetic. Second, data stream derivatives are not meant to model the changes/dynamics of material systems, but are instead meant to model the changes/dynamics of information digestion in living systems. In brief, the derivatives produced by calculus specialize in Matter, while the Living Algorithm’s derivatives specialize in living matter, i.e. Life.

Traditionally, 'derivative' is the mathematical term for an instantaneous rate of change. Calculus is a complicated mathematical process designed to compute derivatives, these instantaneous rates of change. By taking an infinite sum of these infinitely small derivatives, mathematicians regularly come up with something. This mathe-magical process is at the heart of calculus and could be why the word *'derivative'* is intimidating to those with a non-mathematical mind.

For a more in depth discussion of this topic, check out the article – *Data Stream Derivatives*.

A measure of the undirected acceleration of a data stream. Because it is undirected, the Deviation is a scalar.

The *Living Algorithm's * information digestion process produces the ongoing rates of change/*derivatives* for each *moment* in a data stream. The Deviation, along with the *Living Average *and the *Directional* are the most significant of these derivatives. As a group, they are called the *Predictive Cloud*, for the following reason.

According to *Information Dynamics* theory, living systems could employ these measures to make rough predictions regarding probable location, the range of variation and recent tendencies. It seems that knowledge of these predictive descriptors would maximize the effectiveness of response to environmental stimuli. A more effective response would in turn enhance the possibility of survival, thereby conferring an evolutionary advantage.

If this line of reasoning is correct, living organisms employ the Deviation to determine the ongoing range of variation of a data stream. According to the *Boredom Principle*, when the Deviation/range of variation shrinks past a threshold, humans begin to experience mental agitation - i.e. boredom. This inner agitation motivates humans to change behavior, perhaps try something new. As such, human boredom is at the root of experimentation, exploration, fashion and art, at least according to Info-Dynamics theory.

A measure of the directed acceleration of a data stream. Because it is directed the Directional could be considered a vector.

The *Living Algorithm's* information digestion process produces the ongoing rates of change/*derivatives* for each *moment* in a data stream. The *Living Average* is the data stream's 1st derivative (the velocity). The Basic Directional is the 2nd derivative (hence acceleration) of the data stream.

The *Creative Pulse*, a.k.a the Pulse of Attention, is a graphic representation of the Directional of a data stream consisting of 120 ones. The *Triple Pulse* is a picture of the Directional of another binary data stream consisting of 120 ones, 120 zeros, and 120 ones. There is an abundance of evidence, both experiential and experimental, linking these mathematical forms with living behavior.

For more on the mathematics of the Directionals, check out the series of Notebooks entitled *Directionals: Content, Context & Beauty*. For more on the patterns of correspondence between the Directional and human behavior check out the Notebook entitled the *Triple Pulse of Attention*.

In the context of this paper the word 'discovery' means something new to oneself, as in the 'discovery' of a new restaurant, a new hiking spot, or ‘discovering’ something new about oneself.

What does it mean for an equation to be disobedient? To understand disobedience, we must first understand what it is to be an obedient, well-behaved, well-founded equation.

A significant branch of Science, some might even claim the main branch, has to do with the application of mathematical symbols to empirical reality. Included in this branch are all the hard sciences, computer science, and most of the soft sciences. This aspect of the scientific endeavor has been based, almost exclusively, upon obedient equations. Only in the last forty years, has the scientific community begun to even consider disobedient equations as a way of describing reality. This shift of focus was due, at least in part, to Mandelbrot’s development of fractal geometry combined with the computer revolution of the late 1970s. Up until that time, any scientific endeavor that had mathematics associated with it was devoted to obedient equations. Even now, obedient equations still have the center stage.

What does it mean for an equation to be obedient?

An obedient equation belongs to the set of **well-founded functions** in the mathematical lingo. For our purposes, a **well-founded function** obeys the **axiom of regularity**. Briefly, this means that the function doesn’t contain itself. In other words, if a function contains itself, it violates the axiom of regularity and is booted from the club of well-behaved functions.

What is it about self-containment that is so repellent to the scientific community that they have ignored this class of equations? Equations that contain themselves don't conform to the laws that govern standard equations. In other words, traditional set theory doesn't apply to them. While there is only type of obedient equation, there are countless type of disobedient equations. Each type of disobedient equation has its own rules. This individuality makes it difficult, if not impossible, to generalize.

More importantly, disobedient equations are not as predictable as obedient equations. For instance, the sophisticated mathematical techniques that scientists have developed to make approximations don't apply to disobedient equations. While generally unpredictable, disobedient equations do yield precise results to specific conditions.

The *Living Algorithm* of *Information Dynamics* is a disobedient equation. For a deeper discussion of this topic, check out the article *The Either/Or Logic of Obedient Equations*.

We're applying this apropos nickname to Dr. Mihaly Csikszentmihalyi. This esteemed man spent his entire career investigating the human phenomenon that he deemed Flow - even writing a national bestseller of the same name. Flow is an important process that humans must participate in to have those 'optimal experiences' that make life worth living. Other researchers have shown that these optimal experiences are part of a complex process called 'intrinsic motivation'. According to the theory, this is a basic need that must be fulfilled for a human to be happy, productive, and motivated. The Flow process is related to, if not the same as, the *Pulse*. Although we know the doctor's name is pronounced 'chick-sent-me-high', this is impossible to discern from the letters, which are even more difficult to spell. Hence the accessible and descriptive nickname.

In a scientific experiment students were first asked to evaluate their writing skills. They then graded each others papers. In the final stage of the experiment, they evaluated their skills again. In general, grading the papers helped correct everyone's self perception. For instance: although the smart kids initially undervalued their skills, they raised their self evaluation after grading the papers of others. However a certain subset of the dumb kids continued to over value their skills despite being shown papers of obviously superior quality.

This is the Dumb Kid Syndrome: to overestimate one's skills despite being shown evidence to the contrary. This is a particularly difficult illusion to surmount as the individual or group isn't perceptive or smart enough to evaluate the contrary information properly. This syndrome applies to anyone who continues to maintain a theory, belief or self perception despite being shown or experiencing copious amounts of evidence that contradicts their stance.

Elegant simplicity is a feature of good science. If explanations and/or equations are too messy or complicated, scientists continue to search for simplifications. Because of frequent validations over the centuries, it is believed that elegant simplicity is a feature of natural systems. Copernicus rejected Ptolemy's geocentric model of planetary motion because it lacked simplicity.
For more on this topic check out *Copernicus and Elegant Simplicity*.

Nearly everyone agrees that evolution occurs regularly on the micro-level. However there are big problems on the macro-level. This is the area most often attacked by those who don't believe in gradual evolution. How did the elements of the earth gradually evolve from matter to life, from no senses to the sense of smell, from one sex to two sexes, etcetra?

Some scientists have suggested that two complimentary processes are occuring instead of one. In addition to evolution, living systems also experience emergence. Emergence is when something has properties that are not contained in its component parts. Molecules generally have emergent properties that are not predictable from the atoms that make them up. This is why chemists are forever experimenting to discover new and useful features of unusual molecular compoounds. Two hydrogen and one oxygen atom do not predict the features of water.

According to this theory, the increasing complexity of matter yields emergent forms. Emergence has an unpredictable, almost divine, component that many scientists resist. To sidestep these thorny issues, we will refer to macro and micro evolution together, as evo-emergence. It doesn't really matter whether pure evolution, mathematical emergence due to increasing complexity, or divine intervention leads to living evolution. The results are the same.

Experience has a precise technical meaning in the context of *Information Dynamics* theory. This definition coincides with, and yet is not identical with, the usual meaning of the word.

When the mental energy of conscious *Attention* focuses upon a data stream, the *Living Algorithm * begins to digest the incoming information. The Living Algorithm's digestion process transforms meaningless 1D instants into 2D *moments*. This transformation process is a step towards meaning.

When Attention is sustained upon the data stream for a sufficient length of time, the momentum of the 2D moments accumulates, until it produces a 3D Experience. According to the theory, this Experience produces memory traces via neural firings or some such process. In other words, an Experience is required to transfer knowledge to the long term memory from the short term memory.

This notion of Experience is at the foundation of the transfer of information energy into physical energy. When these Experiences accumulate in a similar fashion via sustained Attention, they eventually produce higher level Experiences that are at the heart of multi-dimensional mastery.

Fungible is a legal term that allows for an acceptable range of imprecision in the application of law. For instance, a baker is not found criminally liable if his loaf of bread is not exactly the same weight as advertised. The term recognizes that perfect accuracy of prediction is impractical, if not impossible. In this same sense, we suggest that fungible is a term that aptly applies to all *living* systems.

Living systems require a certain tolerance for imprecision (ambiguity) in both interpreting and responding to the constant flow of information. In order to recognize patterns, organisms must frequently overlook precise details in order to make rough (read practical) comparisons. ‘One can’t see the forest for the trees’ is a common metaphorical expression that recognizes how precise details can obscure patterns. It seems that living systems must have some mechanism whereby they can overlook extraneous details of sensory input to focus upon pattern. This mechanism gives precise details a fungible (approximate) meaning.

Consciousness requires the raw data of sensory input. However, this data is only useful if it has meaning. Some mechanism must digest the raw data for it to make sense to the organism. The digesting of information is an ongoing way of making sense of sensory experience.

The *Living Algorithm's * method of digesting information transforms the precise details of sensory data into ongoing rates of change/*derivatives*. These derivatives provide some rough (fungible) approximations as to the location, range and tendency of any flow of information. Even though these averages are just rough approximations, they provide the basis for the tentative working hypotheses that organisms require for survival.

Because we’re moving through time, these approximations are moving through time as well. Accordingly the Living Algorithm's derivatives create an ongoing relationship between data points. In creating this ongoing relationship, patterns are revealed. These patterns provide essential meaning to the organism. Due to the inherent imprecision of the process these patterns have a fungible characteristic that living systems require. (For clarification of these points check out the article: *Living Algorithm's Fungible Meaning vs. Hard Science's Precision*.)

A Jewish word for someone who is impelled to assist others
by pointing out good monetary deals on goods and services due to extensive
information gathering. In his book **The Tipping Point**, Malcolm Gladwell uses the word
to indicate a personality type that is essential to the spreading of social trends. They are
characterized as those who want to help others out by disseminating useful
information that they have accumulated with no reward in mind.

A mathematical term referring to perpendicular dimensions. These dimensions only intersect and interact along a single line - an infinitesimal amount of their entire surface area. Similarly physics and psychology interact to influence behavior within a limited zone. Otherwise they are independent of each other - with their own laws and principles. The notion of orthogonal planes is why it is unlikely that the science of physics will be able to precisely determine the behavior of living organism.

According to the theory, *Information Dynamics* is orthogonal to other disciplines. It is independent of the physical and psychological realms,
while simultaneously interacting with them.

A logical system of thought which has no rigorous
empirical evidence to back it up. It is waiting for testing to validate
the theories. This is different from a pseudo science, which has been tested
repeatedly with no conclusive results. As *Information Dynamics* has not been
tested rigorously by real scientists it is still a proto science. After testing
it becomes either a pseudo or a real science.

A logical system of thought which has no rigorous empirical evidence to back it up. For instance astrology is based upon an elaborate system of correspondences between time of birth, personality, planetary interactions, and earthly events. Although an uncountable number of books have been written over millenia regarding these connections, it is not considered a science, as no experiments executed with repeatable scientific rigor have verified the validity of any of these correspondences. The lack of evidence does not disprove astrology, but relegates it to the realm of a pseudo science.

Similary until Newton's laws were
verified through observation his was but a pseudo science. This holds true for *Information Dynamics* as well. Until repeatable experiments can be performed which
validate specific theories, the logical structure of correspondences remains a proto or
pseudo science.

The ability of complex forms of life to focus consciousness upon
a certain topic of interest for an extended length of time. A predator focusing
consciousness upon prey to obtain food – the male focusing upon the female of the
species for reproductive purposes – the artist focusing upon a painting to produce
beauty - or even a mob focusing upon a perceived injustice to wreak revenge
– are all examples of this type.
*Information Dynamics* claims that the
*Pulse* reveals the
mechanisms of Sustained Attention.

"the logical form of an argument consisting of three statements, two premises and a conclusion. The conclusion of a valid syllogism follows logically from the premises and is true if the premises are true." (Desk Encyclopedia)

In the classic writings of Aristotle and his followers, the syllogism was more specifically defined as being of 3 types: categorical, hypothetical, and disjunctive. However Aristotle gave this meta-definition of a syllogism. "A syllogism is a discourse in which, certain things being posited, something else follows from them by necessity." This is the essence of deductive reasoning. Although we will attempt to stick to the two premise form, sometimes we will use the word syllogism to refer to a deductive argument of any length.