Natural Systems Theory

by Hugh M. Lewis

http://www.lewismicropublishing.com/

Chapter Twenty-Nine

Alternative Metasystems

Meta-systems are basically two sets of things at the same time:

1. Meta-systems are systems of systems, mostly naturally occurring systems, or alternatively real and human-made systems. They can also include abstract and imaginary systems. Most systems themselves are super-complex--therefore meta-systems science deals with super-complexity in the interrelationships between different systems or their component subsystems. In a sense, we can describe a single hypothetical meta-system that comprehends and encompasses all other systems. It can be said that all of nature, indeed all of reality, which seems to be somehow a greater and more general notion than that of nature, can be unequivocally said to be constituted by systems that occur upon multiple levels of articulation. We understand the functioning, organization, operations and patterns that occur and recur in reality in countless cycles in terms of systems, in terms of the ordered relations that recur between like elements, in terms of rules that are consistently reiterated in the processes of change and occurrence. A systems approach is ultimately how we approach knowledge of reality scientifically once we move beyond simplistic deterministic models that are based upon strict correspondences between events and terms names and the classical sense of causality that is used to explain such event structures. It was Niels Bohr who pointed out the relevance of a view of complementariness in all fields of the sciences, at all levels of the articulation of reality, and this amazing insight, as true for cultures and the designs of frogs as it remains for realistically understanding the structure of subatomic particles in their atomic orbitals, remains at the heart of a meta-systems approach.

2. Meta-systems are knowledge theories and heuristic methodologies relating to knowledge. In this sense, meta-systems are comprehensive and they represent both a form of philosophy and philology and a kind of science about knowledge. Because all knowledge that is known is fundamentally human knowledge, or at least human mediated knowledge, this sets certain basic constraints and conditions on the normal or typical structure that knowledge takes. Therefore, we may say that meta-systems provides a heuristic system for the organization, articulation, application of received knowledge and the generation of new knowledge.

Meta-systems as a perspective and approach grew out of my professional involvement in the Anthropology of Knowledge, and represents an extension and application of this approach to a wide range of issues and areas that are both trivial and important in our world. The anthropology of knowledge has had an eclectic history of development, and is related but not the same as the sociology of knowledge though it comprehends many components of this other area. Anthropology has long been interested in the problem of the psychic unity of humankind and the general problem of "primitive thought." It has had its own tradition and contributions to psychology and the study of human behavior and symbolism in cross-cultural contexts. It has been intimately interested in problems of socialization, enculturation, identification and the linguistic ties of the native speaker to a coherent worldview. The focus of the Anthropology has come to focus upon what has been known as the worldview problem, or how we articulate a coherent view of the world and function in relation to such a world.

Metasystems science was where theoretical and methodological development in the Anthropology of Knowledge had been leading me consistently over the last decade, one step at a time. It took my fieldwork experience in the heart of central China to precipitate this framework out--perhaps it was the totalitarianism of daily life there that demanded of me a sense of totality of worldview that was not violent or destructive but at least appears benign and constructive. But even more importantly, I believe, it was my students and their continuous questioning me about the larger world, making me think about the consequences of a shattered or ill-defined or incomplete worldview, that the consequences of its ideological manipulations that the true power of genuine independent thought and intellectual freedom came to the foreground of my anthropological concerns.

Of course, an entire decade of graduate training and prior fieldwork led up to this stage in my own development. There as a growing dissatisfaction with conventional solutions and pat answers that even an esoteric field like the Anthropology of Knowledge could offer.

Since that time four years ago, I have been devoted in one way or another, and usually in multiple ways at the same time, to the development and fulfillment of a meta-systems approach, not only on paper, but in terms of lived reality as well. I believe the world is more than ripe for such a frame-shift or maze-way reformulation, but it is not yet prepared psychologically or ideologically to receive or participate in such alternation, especially in any collective sense that would be necessary to bring such a vision to fruition. It was I believe Buckminster Fuller who saw the most optimistic and positivistic vision of a world governed not by politicians and their private interests, but by the good intentions and wisdom of scientists and the public benefit that is derived from this. In this sense, he was completely a visionary, a man ahead of his own times. But he had the open and naively idealistic framework of the 60's, set against the evils of Vietnam, to propel him forward in his vision. Since then, socially and ideologically, human knowledge has seen much regression in spite of the quickening tempo new scientific revolutions, discoveries and inventions around every corner. We have revived for administrative attention and public obfuscation issues that were supposed to have been settled with the Scopes Monkey Trial.

There has arisen an unfortunate legacy of this sister area of the sociology of knowledge that it has been construed as somewhat anti-scientific and political in its interpretation and application. In terms of its central tenets and methodologies, nothing could be further from the truth--it has only striven for a more realistic vision of the articulation of scientific knowledge in the world and how this articulation is susceptible to social and ideological influences. Like the general anthropological doctrine of relativism, with which it is closely associated, this doctrine of the social construction of knowledge and knowledge systems has been reinterpreted and revisioned to suit the interests of whomever it is doing the re-visioning and reinterpretation, regardless sometimes of the accuracy of the point of view being promulgated. In such a manner, we see that even the field like the sociology of knowledge is susceptible to the same ideological constraints and influences that it was created to critique and "deconstruct" in the first place, and this makes sense because even knowledge about knowledge becomes susceptible to the same kinds of structural patterns and limitations and distortions that all knowledge is prone to.

Coming from the anthropology of knowledge, these political and ideological issues can be at least partially side-stepped. The problem with the anthropology of knowledge has been that it has been conventionally received as such an esoteric professional interest that even most other anthropologists themselves are mostly unfamiliar with its terrain, much less the average non-academic.

There are five basic sets of questions that mostly deeply concern meta-systems, each of these questions informing and guiding research at different levels of meta-systems stratification:

1. What is physical reality? Or What is real?

2. What is life?

3. What is intelligence?

4. What is possible?

5. What is true?

The answer to these kinds of questions is never straight-forward, and attempting to answer them results in a life-time of research and query. Some might claim that these kinds of questions are unanswerable, though I do not think so, at least from a relative point of view. Unanswerable kinds of questions are those that science should not appropriate ask, and, when we boil it down, there may be only one such unanswerable question:

How and why did it all begin in the very first instance?

A logical extension of this is to ask the opposite but complementary question:

How and why will it call end in the very last instance?

The question that I believe to be ultimately unanswerable is the question of ultimate origins of our reality. This is a question that cannot be answered even if we adopt a purely mechanistic and material point of view. It is therefore a problem not for science but for religion and symbolic ideology to deal with. There are also non-absolute or relative questions that I believe it to be ultimately beyond the purview of science to resolve. These are normative or human evaluative questions like:

What is good?

And what is beautiful?

There are no absolute or absolutely certain answers to this kinds of questions that science can grab hold of in a fully objective manner. That does not mean that explication and especially elucidation of these kinds of questions should not be attempted in the name and spirit of science, to yield what greater objectivity we might from them. Religion and symbolic ideology can also answer these kinds of questions as well in some ultimate sense.

Otherwise, I see the range and possibility for scientific query to be fairly unrestrained and wide open. Science can and ultimately will, if provided enough time, solve all problems relating to the questions of reality and truth listed above, at least in a way that is mostly satisfactory if only approximate. If we consider the fullest logical and natural implications and consequences of these kinds of questions, we realize that they extend beyond the boundaries of the current state of knowledge in critical ways. They open us up to asking questions we might not otherwise think to ask, and to seek answers to problems we previously did not even imagine existed. And this augmentation of reality has been a normal and common function of our sciences.

The development of systems theory and methodology in a complete sense allows us this degree of openness and flexibility, and permits us to approach and formulate new kinds of problems that were previously unapproachable without this consistent framework.

Advanced MetaSystems Science

In general philosophical perspective, it is significant that, as regards analysis and synthesis in other fields of knowledge, we are confronted with situations reminding us of the situation in quantum mechanics. Thus, the integrity of living organisms and the characteristics of conscious individuals and human cultures present features of wholeness, the account of which implies a typical complementary mode of description. (Niels Bohr, Causality and Complementarity, 1958)

If we seek a unity between C. P. Snow's two cultures of the Sciences and the Humanities, we must find this common ground in the so-called social and human sciences. There is good reason  for this intersection, as it is the anthropological relativity of humankind, as the central knowers and doers in reality, that leads to the possibility of the integration of these separate approaches to reality. Niels Bohr gave us the means for achieving this kind of integration when he compared complementary explanation to deterministic causality, and he referred to the complementarity of relativistic explanation with intrinsic naturalistic explanation. If we pursue the humanities far enough, we are liable to run into either narrow ideologies or expansive relativist doctrines. If we stick too strictly to science we end up with a empty model structure of reality that is devoid of the very pattern it seeks to understand.

All human knowledge exists in a fundamental relationship to the unknown, which relationship  can be defined in terms of relative or residual uncertainty that is attached to any particular bit or statement of knowledge. A statement like "Christopher Columbus discovered America in 1492" is one that is fairly unambiguous and most would attach almost no amount of uncertainty to it. This is as true as the common sense that a school-child's song would imply. The trouble with the statement, understood critically and semantically, apart from the actual historical record, is that "America" did not receive its name until well after 1492, and that the dating system is not universally applicable to all people. This may seem like equivocation, but it does emphasize the kind of critical attitude to information that is necessary to understanding its form and function in our lives. In this example we can clearly distinguish between internal and external validity and consistency of the statement.

Research that is rooted in the expansion of knowledge is in a sense rooted in the desire to systematically reduce or eliminate the source of ambiguity in knowledge that arises from uncertainty. We do so by trying to chase out the unknown, or at least chase after it. This process is clearly evident for example, in the more mathematical of sciences like chemistry or physics, where problem sets are usually posed trying to solve for a specific unknown factor in terms of factors that are known. Such systems of knowledge rely on the great applicability of mathematical definitions and procedures for the identification and relation of physical substances, properties and processes. We know that a mole is a specific number of atoms or molecular units, and that a mole of iron will weigh, on earth at least, a certain number of grams. Even here though, determination of unknown values often rests upon empirical measurement which contains some residual degree of uncertainty.

Another approach to the problem of the unknown in other knowledge systems is the use of inductive inference and contextuality to help make determinations regarding unknown variables. Of course such methods are inherently less accurate and less precise than are the preferrred mathematical approaches, but if done well they can bring a degree of synthetic understanding to complex problems that in a sense transcend the analytical aspects and factors associated with the problem.

It does us little good to define an unknown in terms of another unknown, unless the second, substituted unknown may be determined to be somewhat better known, or a partial unknown, that can help to reduce the level of uncertainty associated with the first unknown. The second unknown variable may provide for us  a context or a point of reference by which we can tackle the larger problem it comes to represent.

It is natural and only human to attempt to define unknown variables in terms that are known. In the simplest cases, we merely substitute what is most familiar to us, and rely upon complex rationalizations to absolve ourselves of any contradictions that may follow. The trouble with using our known variables in reference to unknown realities, is that if something is unknown, it tends to be without reference point or hook. We do not even know where to start in unraveling the problem it presents. It opens, in the language of A.I., a tremendous degree of search solution space and informational complexity which presents us with what can be called an informational bottleneck--what we know may be simply inadequate  to defining the problem in the first place, much less in determining its solution.

The unknown therefore always surrounds and overshadows what we know with a cloud of uncertainty. We can be certain that we are uncertain, but we cannot be uncertain of our uncertainties. The inherent uncertainty of all knowledge presents of with inherent dilemmas about our understanding of reality that are difficult, in the largest sense, probably impossible to overcome. On the other hand, the horizon of our knowledge offers us the promise that the quest for new knowledge will never come to an end until at least we ourselves come to an end.

The known and the unknown exist in a complementary relationship, and uncertainty is a measure of this relationship. It is tied to the idea that disorder connects to all sense of order, and that everything tends in the long run toward increasing disorder. In other words, knowledge has a fundamental anti-entropic information function, that was tied to our own physical survival and adaptation in the world. This function is inherently based upon the self-organization of natural information, and the energy relationship it shares with real systems in the world.

The antinomalities presented in our understanding of physical reality are able to be transcended when we realize that our understanding and descriptive observation interacts with physical reality in basic ways. The cognitive models we hold of a system or an object and its behavior influence directly how we  see and understand the thing and its actions. We may see and approach a thing both analytically or synthetically--paying attention to the properties that are the result of its holistic integration as a system, or paying attention to the elements or components that are the efficient cause of the systems functioning. Either way, we are neither right nor wrong. Thus a pocket watch is both understandable as a machine of many fine interlinked components, and as a single integrated mechanism that keeps time in an accurate manner.

There are fundamental, intrinsic limits to our ability to observe and know the very large and the very small. The speed of light defines an observational parallax for the very large that prevents us from seeing the universe in an instantaneous manner as it may exist at this moment and at the next. We can only infer its existence by reason and by extension of reference from more proximate examples that we can prove--we cannot see the exact disposition of Mars at the moment we are observing it through a light telescope, as its light took a few minutes to reach our objective lense. And yet because we can still observe Mars several minutes later, then we can conclude that we are seeing at a later point what Mars actually looked like several minutes earlier. And there is sound reason not to think that this is the same general situation for very distant points in the universe that can only be observed at very great depths of space and time. Just because we cannot directly observe the instantaneous state of the universe, does not mean that we therefore conclude that it doen't exist. Scientific evidence at this point does not rest on either falsifiability or upon proof--we can neither prove nor falsify what we cannot even indirectly see. We only conclude that it is so by logical deduction and inference from phenomena we can demonstrate. We suppose that the universe in the largest sense exhibits a minimal amount of consistency in its basic physical properties and components, and we do not  expect it to be radically different.

Similarly, it seems, that there is a scale of the very small that defines a fundamental limit of observability. This scale seems to be related to the size of a photon or quantum of light energy. In logical short hand, anything smaller than a photon would be invisible by means of  photons. While both these limits express limits of observability  based upon our dependency upon  the basic properties of light, it is not necessarily the case that they describe inherent physical limits to the possible size and shape of the very small or the very large.

There exists ample indirect experimental evidence of the very large and the very small that allows us to conclude that at both extremes there occur other kinds of interesting constraints that influence both our observability of these physical phenomena and their own intrinsic behavior. Upon the scale of the very small, it becomes impossible to localize in a determinative manner any particular entity that is independent of the field in which it occurs and may be evenly distributed within a given range of Bose-Einsteinian probabilities. In other words there occurs an inherent indeterminancy or uncertainty in our ability to precisely define both the location and the energy state of such fundamental particles at the same time. Upon the scale of the very large, evidence tends to suggest that what we assume to be Euclidean in dimensions may actually be non-linear and may be in fact larger or smaller than would be expected in a Euclidean sphere.

The concept of complementarity implies an inherent duality of structure and explanation, of event pattern and observation, that informs the structure of physical reality upon every level of its organization. This gives to scientific explanation an inherent dialectical tension between an inferrable holism of pattern on one hand, and an analytical reductionism on the other. This complementarity of physical reality, that can be observed at all levels of stratification and integration, is as much result of the anthropological relativity of our knowledge, to squeeze our understanding through the symbolic screen of our consciousness, as it is anything intrinsic to the structure of reality itself. It is a product of both anthropological and physical relativity of structure and pattern. Dialectically speaking, physical relativity of knowledge about physical reality constrains the anthropological relativity of our  knowledge and worldview in basic ways. At the same time, the anthropological relativity of our knowledge and worldview also constrain our understanding and relation to the physical relativities of our sense of reality.

What is a System and a Metasystem

A system can be described as a complex set of interrelationships that occur in a semi-determined manner, within the framework of a larger set of surrounding relationships that may or may not have an determinative influence upon the system. The relationships in general are so complex in even a simple system that they tend to defy description or prediction of outcomes. A system in a technical sense is a kind of mechanism, and therefore such systems tend to follow mechanical principles as these are understood at some level. The types of mechanics that describes systems varies considerably with the level of the system and its integration. We employ quantum mechanics to describe the behavior of the atomic system of electrons, light energy and other fundamental particles of physical reality. We employ classical and relativistic mechanics to describe on the other hand the motions of bodies in space, and I would make the case for gravitational mechanics to explain what occurs within gravitational fields and gravitational bodies. Upon a biological level, we may employ the term bio-mechanics at several levels as well, referring first to the cellular and biomolecular mechanics of energy storage, molecular production and reproduction. We can refer to the mechanics of cell tissues and the physiological mechanics of complex organs and biological systems of an organism--distinguishing for instance the nervous system and the skeletal system. In this, we can see that the organisms constitute what can be called metasystems, which simplest definition can be said to involve the integration of various component systems and subsystems within a single organiismic framework that is characterized by specialization and differentiation of function, and by the emergence of organiismic properties that shape the behavior of the system as a whole. Upon a human level we can find systems in terms of the organization of worldview, attitude, response pattern, affectation and social adaptation of the individual, and of the small group dynamics and network that individuals maintain in larger social contexts, to the development of full fledged institutional and corporate systems that serve one purpose or another in the world.

All natural systems are metasystems, or else parts of metasystems

A metasystem is a complex organization of processes of change and transformation of states that can be said to have some kind of structure or order (i.e. nonrandom occurrence).

All natural systems are stochastic systems. In other words, they are derived as the result of the chance concatenation of component subsystems into a regular working order.

All natural systems as such perform some form or function of work, which can be described as the antientropic transference or increase of energy in a systematic manner.

In order to do work, natural systems must be organized in a manner that implicitly conveys information.

Scientific knowledge systems are built upon and lead to this kind of natural information implicit to the patterning of phenomenal reality.

All systems may be characterized by certain structural patterns, and these patterns recur throughout the universe and appear to be something profoundly basic to all physical reality. First and foremost, we might state the following:

1. All natural systems tend towards a state of dynamic equilibrium.

2. This equilibrium tends to be complex.

3. Systems perform work, which is the informational organization of energy to maintain equilibrium.

4. If the equilibrium of a system is perturbed, it will tend to restore it self.

5. Systems at all levels tend to be continously perturbed.

6. All systems follow basic principles of universal energy dynamics.

7. All systems follow therefore state-path trajectories that can be characterized in the long run and in the large by non-linear control equations.

8. The universe as a whole is a system within equilibrium, what we can call universal equilibrium.

a. All finite systems occurring within reality are subsets of the universal system.

b. The universe is the universal set that contains all systems as members.

9. Subsystems are stratified and nested upon multiple levels based upon fundamental dimensions of differentiation.

10. Systems tend to be historically unique and relative to the level upon which they occur.

The concept of equilibrium of a system is really what is definitional about a system as something distinctive to the concept of a "system." In other words, a system that does not exhibit some form of equilibrium (upon some level of integration) is one that cannot be said to be a system. Equilibrium is generally represented in the following kind of formula:

K = (X)/(Y)

Where upper case K stands for an equilibrium value associated  with a system, and variable (X) stands for all those variables and associated values that are in the end-state of the system being measured, and (Y) stands for the composite of all those variables and associated values that are a part of  the start-state of the system being measured. Alternatively, unknown (X) can be the composite of all internal values associated with a system, while (Y) can be the composite variable of all unknown external values associated with  a system.

This formula implies an inherent reciprocity of value between X and Y and it is this reciprocity that is the key to the equilibrium that exists between them. We can rewrite each unknown in terms of the equilibrium value and the other unknown. For instance:

(X) = (Y)/K  and (Y) = (X)/K

Integration is the measure of order of a system that is achieved by means of equilibrium. Equilibrium is defined as a state of relative balance to which all things tend in their interrelationships. Once a system has achieved equilibrium, such a system will tend to maintain that equilibrium indefinitely. That equilibrium is important in reality can be demonstrated in many basic principles, for instance, it is known that in the electromagnetic spectrum, wavelength times frequency is equal to the speed of light. We therefore  have an equivalent form of basic equilibrium if we rewrite the formula:

ln = c, l = c/n, n = c/l

where l is the wavelength

n is the frequency

and c is the speed of light

Associated with these properties is the idea that both wavelength and frequency of  light always exist in equilibrium with one another in relation to the constant of the speed of light.

Another related and well known formula is the statement of the equivalence of energy to mass:

E = mc², m = E/c²  and c² = E/m

Where E is the total energy of a system,

 M is the mass of a system,

And c² is the speed of light squared.

In this equation, we can say that measurement of both mass and energy are in equilibrium in relation to the square of the constant of the speed of light, which is itself therefore a constant. In both cases, because the denominator value of the equilibrium equation is a known constant that never varies, we can understand that the equilibrium of the system is variable in only two  sets of dimensions. We refer to these forms of equilibrium as equivalence structures, and equivalence thus defined represents a form of equilibrium that is variable in only one set of dimensions.

Equilibrium implies something more important as well, and that is the operation of a "control" or controlling force that influences the behavior and trajectory of the system. In this case, control is to be considered several things--it is non-linear and it is self-determining.

A second basic statement about systems is that all systems can be said to be self-organizing systems. That is, they arise stochastically as a function of blind chance without necessary causal predeterminations. Such systems tend to run toward increasing degrees of complexity in their functional equilibrium. We tend to see such systems therefore as being inherently underdetermined. The complexity of these systems is a consequence of the degree of integrative equilibrium they achieve, and the fact that this equilibrium arises as the result of complex relationships that are at best only semi-deterministic. They are thus inherently unpredictable as systems--a fully determined system is one that is theoretically uninteresting because we can learn nothing new from it. It has no new information to be gained. In science, we cannot attach attributions of original or inspired causality or determination to natural events--natural events must be explained in terms of a kind of mechanical causality, or at least a reciprocal relationship between things that are part of the system.

A third fundamental statement about natural systems is that all such systems are inherently dynamic--i. e., they tend to change in the long run in certain important ways. The dynamics of systems is really what makes them most interesting to study, as change processes, though not predictable, can be expected to yield new information.

To summarize our key points, we may say the following:

1. natural systems exhibit equilibrium

2. natural systems tend toward complexity

3. natural systems are self organizing

4. natural systems are continuously dynamic

5. natural systems are informationally representable.

The last point is an important consideration to make, to the extent that it ties the status of the inherent organization of such systems to our ability to comprehend such systems in a manner that can be considered objective and true to the nature of the system--i.e., this feature of natural systems makes possible scientific knowledge about such systems.

In the consideration of metasystems in relation to advanced systems analysis, we must realize that the development of a metasystem entails the progressive subordination of systemic function of lower order systems. A metasystem is a complexly integrated entity that is composed of a number of different subsystems that interfunction to create a total system. In a sense, any discrete thing that is inherently differentiated on the basis of specialized functions can be considered to be a metasystem. The earth can be considered a metasystem, as might be the oceans and continents. I believe that a metasystem has the features of wholeness and integrity. They are easily identifiable as separate systems in a larger framework of other systems. The solar system is an example of a metasystem that combines multiple planetary, lunar and the solar system within a single unified structure.

Metasystemic functions are characterized I believe foremost by emergent and synergistic properties. As mentioned above, they are characterized by  a subordination of function to the purposes of differentiated specialization in a context that contains its own internal equilibrium, or even what might be called, internalized ecology of environment that serves to set it apart from the larger surrounding context of its occurrence.

By and large, the kinds of metasystems that I am concerned with in advanced systems science are those possible or potential metasystems that can be developed by  humankind in the context of the earth and beyond in the context of a larger universe. I am also concerned foremost as well with the actual human metasystems that have been produced, and that are developing along their own state-path trajectory regardless of human interferenece or involvement.

Scientific Relativity

Scientific relativity concerns the question of the status of scientific knowledge within the context of the patterned structure of reality that such knowledge seeks to represent. The status of such knowledge is that it is constrained by certain intrinsic and extrinsic limitations at various levels that serve to predetermine how much we can know, and how we can know it, with the implication also that it tends to preclude our capacity for knowing things beyond the compass of our scientific knowledge systems.

In a general sense, we experience scientific relativity in terms of the kind of data and knowledge that science deals with, and the kind of knowledge that science cannot deal with. We distinguish between ideology and scientific methodology as distinguishing hallmarks that constrain scientific knowledge.

We may also distinguish three major forms of scientific relativity--physical relativity of knowledge at the level of physical systems theory; biological relativity of knowledge at the level of biological systems theory; and the anthropological relativity of knowledge at the level of anthropological systems theory.

Relativity concerns the question of the relationship of the known to the unknown, or of the degree of relative uncertainty of our knowledge that is based upon the working constraints and that serve to limit the sense of certainty that can be attributed to our knowledge. Certainty is an important problem in scientific knowledge, as it concerns centrally the issue of the validity and reliability of information, of what we know and how we know it. We cannot escape this central existential dilemma about knowledge, that we can never be absolutely certain of what we know. There is only one area of knowledge that has any sense of absoluteness about its value as knowledge, and this is in the area of mathematics.

Mathematics in a sense comprises the only field of non-relative knowledge that we have. There is a sense of certainty in the statement that  two plus two equals four, that can be found in no other kind of statement we can make, neither "this is a tree" or "the tree's leaves are green," etc.. Mathematics, being the basic language of science, remains the preferred form of expression and mode of operation in scientific research, if this is at all possible. Mathematics refers to nothing in the external world as the basis for its validity, unlike the sciences which, however rationalized, are always fundamentally empirical in reference and orientation.  Mathematics derives its truth value from the internal coherence of its purely logical structures. In this sense, a computer is  nothing more than an intricate mathematical machine, and a computer therefore exists always in a closed world that has no reference to the external or larger world of which it is a part. It may  reflect or represent this world, but this is only a form of mimicry. The lack of external reference in mathematical validation renders it inherently non-relative and absolute in form, and it can be said to be the only non-ideological or symbolic system that achieves this status that we know of.

The relativity of knowledge at all levels can be seen therefore as a function of the dependency of such knowledge to external reference for its validation. With an empirical dependency, the problem of inherent correctness that is characteristic of mathematics, is replaced by the problem of external certainty. Such relative systems also fundamentally face a problem of internal coherence, when the meaning and verification if its system is always pointing to a larger world, because coherence becomes faced with a central dilemma of inherent ambiguity of meaning that is uncertainly placed in a larger world.

At each of the levels, there are various reasons and sources for the scientific relativity of knowledge that will be each addressed in turn below. At whatever level, we may identify both internal relativity of such knowledge that is largely a function of the limits of the language of science to accurately describe and represent the reality it refers to, and the external relativity of knowledge that is based upon the status of ourselves and our physical limitations in being able to know reality in some larger or finer or alternate way.

Physical relativity is well known upon several levels. There is the uncertainty principle that determines that we cannot know the exact point position of an electron in its orbital without sacrificing other kinds of information in the process. There is the general and special relativity of Einstein, that determines that the universe exists within a four dimensional space-time coordinate system. I would impose as well a framework of fundamental relativity that states that there is a limit to our ability to know the very small due to the constraints of our ability to see at a size smaller than that of a photon. The other form of relativity is what I refer to as universal relativity, and this takes several aspects. The first is our inability ever to see beyond the space-time limits of our own sphere of observation, to view simultaneously the exact contemporaneous state of the universe, or even a portion of it. This constraint limits our ability to see the very large in certain interesting and basic ways. We cannot see for instance, exactly what may be going on inside of a blackhole, if no light ever escapes the confines of its gravitational force. We cannot look outside of the historical dimension of the speed of light, so that we cannot even know, for instance, the exact disposition of the whole universe at any point in time, past or present. We may speculate further that if light  bends, or space-time curves in some basic ways, and perhaps leads into alternative universe systems, then it is possible that we  will never be able to directly realize or experience these systems. I would add to the concept of universal relativity the inability ever to observe a completely motionless system or a system that is completely outside of a space-time system that is shaped by a complex gravitational field.

Biological systems they, as I have previously remarked, are some of the best mapped out theoretical constructs that we now have. We are confronted with basic forms of biological relativity in terms of the limitations of our own species from a biological standpoint. For instance, we can never know in a final or complete way how another large brained organism, like a dog or a dolphin, may think, nor will we ever be able to develop a fool proof system for communicating with these creatures at the level that we can converse with one another. Dogs have a tremendous sense of smell, that we cannot approximate, and it appears as if dolphins may be able to see acoustically in three dimensions. Biological relativity  becomes even more pertinent when we consider, for instance, the challenge of the creation of alternative life forms, or the formation of alternative or alien biological systems in the universe. There are certain biological constraints that we, as human beings, must overcome if we are to achieve a larger vision and broader experience of reality. One of these sets of constraints for instance is within our normal light field of vision. We have through technology learned to see at other wavelengths, by the translation of these wavelengths to forms that can lead to visual representations. We use telescopes to extend our range of vision to vast distances, and microscopes to clarify the world of the very small that is beyond the resolving powers of the naked eye. To a great extent, biological limitations of knowledge have been largely overcome, and it will not be until our encounter with truly alien forms of life that we will once again confront very fundamental issues involving our own sense of biological relativity.

Anthropological relativity stems foremost from the fact that we are the knowers of the universe. We sit at the center of all knowledge and understanding, and there are many forms of anthropological constraints that serve to influence, limit and shape our knowledge and experience of the world. These kinds of influences can be cultural, rational, linguistic, historical, social, symbolic, ideological or religious, etc. Anthropological relativity exists to be dealt with at all levels of our knowledge systems. It ties to biological relativity to the extent that our own knowledge systems are based upon the organization and functioning of the human brain within the kind of context that it develops within. Our brains constrain us to see and understand and respond to the world in certain ways, and not in others. The symbolic organization of human cognition and consciousness constrains our behavior and our noetic experience of the world such that it becomes impossible not to see the world in these terms.

The Role of Anthropological Relativity in the Structuring of Human Knowledge

Anthropological relativity is a profoundly important concept. It is especially important in the Anthropology of knowledge, because it serves to identify a problem inherent to human knowledge of all kinds, a set of limitations that are characteristic of the basic structure of this knowledge. Most recognizable forms of relativity, as in linguistic relativity or cultural relativity or social or historical relativity, plus many other kinds, are merely variants of the general problem of anthropological relativity that is applied to some field of study or general range or set of problems in reality. Scientific relativity is also a variant of anthropological relativity, and I would dare say so are most forms of physical relativity as well, which, in whatever form, boil down to the proposition that our ability to know something basic about physical realtiy depends critically upon the point of view of the observer's frame of reference.

Anthropological relativity can be said to exist in the inherent limits of human knowledge systems and in the natural languages that are used to describe reality and to convey understanding. Our knowledge is universally structured in very basic ways, ways that I would simply call symbolic, and this structuring imposes inherent limits of design and articulation in our knowledge systems from the beginning. Scientific knowledge has achieved remarkable results in its application and progress mainly to the extent that it has been able to systematically control and overcome the influence of anthropological relativity in our knowledge. This has been slow and painstaking progress. We are in essence involved today in a scientific revolution, one that has seen an exponential explosion of new knowledge and insight across all the scientific domains of research, and that has driven the electronic information revolution as well. We may well ask when this growth curve will taper off upon its natural plateau, as it has not yet appeared to do so.

It seems, the main problem presented by anthropological relativity is its invisibility in our knowledge systems, for we though we are entrapped within it, we are most often oblivious of its influence upon our thoughts. Its influence normally lies beyond the bounds of our awareness, because it exists in the background of the knowledge system upon which we know and build our knowledge. A way of understanding this is to see that all that we know is always a subset of all that is unknown, and the unknown (including the unknowable) is always a larger and more inclusive set than the known. The trouble is that we cannot directly know the unknown, or unknown the known, unless we are willing to suspend for the time being the ideational frames of knowledge that we bring normally to the experience of reality. We can say that our basic knowledge background prestructures how we see, think about and relate to the world. This prestructuring is symbolic and it is unavoidable. Much of our knowledge remains implicit to the background--that is it is usually taken for granted and assumed to exist as such without being further queried. This is not just a convenience, it is a necessity, as otherwise our knowledge systems become quickly overwhelmed by the need to deal in an explicit sense with too much information. We suffer information overload anyway, regardless of how well defined our background knowledge may be, because when we deal with unknown variables with large uncertainty factors, overloading becomes an eventual consequence of failing to resolve the information bottleneck of our own symbolic sense making mechanisms. We find that we cannot avoid the problem even if we try.

Anthropological  relativity then becomes the basic problem of the limitations of our knowledge, and our ways of knowing, to deal with every  question and problem set that we confront in reality. This is even more problematic when we realize that how we pose questions and define problem sets are themselves constrained in critical ways by the very knowledge systems from which they spring in the first place.

There are several sets of dimensions that are useful in understanding the role and implications of Anthropological relativity of our knowledge:

1. Subjectivity versus Objectivity in knowledge systems

2. Empirical versus Rational knowledge

3. The ideological limitations of language and culture

4. Problems of inference and reference

5. Problems of implication and explication

6. Analysis versus synthesis

We can identify further important issues when we come to a recognition of the limitations of scientific knowledge versus other alternative ways of knowing:

1. Limitations of observation and observability

2. Limitations of perception and cognition

3. Limitations of measurement and abstract application

4. Limitations of description and explanation

5. Limitations of definition of problem sets

To these issues we can add probably a host of other critical kinds of limitations to or knowledge, especially those standing out as being primarily social or methodological in orientation:

1. Limitations of social communication and openness

2. Limitations of social praxis and relations

3. Limitations of research funding and priority of focus

4. Instrumental and methodological limitations of research tools and instruments

5. Limitations of bureaucratic controls and socio-cultural contraints.

To this final set of limitations, we may add one more profoundly important set, and that is the ethical constraints and professional obligations and standards of a discipline of knowledge, that may preclude the possibility of some kinds of research, or restrict access to information or to possible procedures that might otherwise lead to new information. The last consideration is especially acute in the human sciences, where much that has been learned for instance about the human brain and human behavior prior to new technologies was achieved through "forbidden" experiments or by natural but unusual occurrences, such as brain aphasias from battlefields.

Anthropological relativity is not just about limitations of our knowledge--even where and when knowledge appears to be relatively unlimited or unconstrained, when we have an open and free view of something, the claim may nevertheless still be made that our knowledge systems remain fundamentally constrained and restricted in basic, anthropocentric ways from which we cannot by ourselves escape. It  must be understood that the means to greater power and vision in knowledge is not through the abandonment or side-stepping of relativistic considerations, but by the embracing of such issues with the intention of both better understanding how such constraints influence our knowledge in what ways, and how we may work to circumvent or side-step such knowledge systems in a better way of knowing. We turn our weakness into our strength, and we take advantage of what is relative about our knowledge, in order that we may better overcome such limitations in the long run. And so far, we have largely been successful in this regard, and relativism of understanding upon very basic physical levels is no longer seriously questioned, but becomes the basis for the development of a complementary approach to theoretical explanation of fundamental phenomena. Relativity arguments fail to catch on in biological and especially social-psychological circles of research, because in the epigenetic complexity of information pattern, imposing such constraints can seem not only unwieldy, but counterproductive to the task of creating simplifying solutions to reality. But we abandon the warning signs of relativism on the intellectual road we travel only at great risk, because it will invariably lead to the foreshortening of our opportunities to expand the basic horizons of our knowledge beyond our own ideological constructions.

Relativism in the social sciences especially has been poorly received because the dragon of relativity has not been fully considered for its implications, and it is therefore generally misrepresented as a kind of blind solipsistic determinism that undermines or makes impossible the objectivity of our constructions and of our knowledge systems.

There are many examples in different fields of forms of relativism of knowledge, and it is characteristic whenever fields of study deal with unknowns with great degrees of uncertainty. In such cases, uncertainty begets little agreement or consensus on one hand, with a plethora of competing solutions, or else no dissent or disappreement at all, which is even worse. Those who want to frame their fields "scientifically" to squeeze whatever kind of left-over legitimacy they can get from such terms, are apt to remove the entire problem of the relativity of knowledge as a perjorative and a counter-productive source of noise in their theoretical and methodological formulations. The tendency and sometimes vocal call to do so resounds time and again across academic classrooms and down corridors. Most end up attempting to avoid the entire problem of relativity anyway they can, not sure in the end how to deal with it or what it may mean.

Anthropological relativity identifies in the most basic sense the status and position of the human being at the center of the knowledge universe--we can say that knowledge is inherently anthropocentric in this regard, as we cannot remove ourselves, even if we wanted to, from this central position as knowers. We can say something like, "I think, therefore it is." We use science to create a degree of social parallax to our knowledge, to make it not a unicentric approach, but a multi-centric field with many interchangeable points of view. Ideology is also socially based, but this is rooted in conformity and agreement to dogmatic ideas, rather than in the capacity for human beings to contest and cross-test ideas for their validation. But the social distribution and sharing of knowledge does not ultimately overcome what is most basic about anthropological  relativity, and this is the fundamental anthropocentric character of the human knower at the center of the human knowledge universe. Social parallax cannot overcome this event horizon, unless perhaps we can increase somehow our definitions of society to include things like talking Chimpanzees and Gorillas, intelligent Dolphins and even symbolic if silent Canines. We have our huge radio-telescope ears trained carefully to the most distant corners of the galaxy in the hope that we will hear something intelligent amidst all the static and interference. So far though, there has been only silence and the sounds of our own thoughts that fill the night sky.

Human language, cognition and culture become the basic limiting factors to consider in anthropological relativity. They impinge upon scientific knowledge in critical ways, circumscribing what and how we know, and the kinds of conclusions we draw about the world. Even if and when scientific method seems to be consistently applied across cross cultural boundaries, it is still the case that the same methods might lead to different results, or at least different interpretations of our results, due to unseen anthropological factors. I would say that if we could clearly isolate the problem of perception from the problem of cognition, conception and problem definition, then it might be possible to  impose relatively objective etic standards of measurement and description upon the phenomena we are testing. This is what science strives to achieve in the adoption of arbitrary but international standards or units of measurement. It is clear though that the problem  of perception is a thorny one upon the horns of relativity, and though it is not clear that in a mechanical sense we are all seeing the same things in physical perception of reality, it is very clear that the images and patterns we derive from these perceptions may vary considerable due to these background factors. This kind of issue becomes even more acute when the problem of description and natural linguistic codification take over, as for example in establishing taxonomic classes and ordering these in relationships, as there begins to be a noticeable lack of agreeable physical measures that can be imposed upon such data. Indeed, in the typologies of Hominid skulls down through time, no expense of effort has been spared on making numerous tedious and precise measurments of cranial breadth, etc. and the various polytypic combinations of these elements have resulted in fairly accurate and clear descriptions of ranges within which certain types appear to fall. But it is to be expected in the natural history record, if not in the fossil record, that continuous variation would be the rule and discontinuous trait boundaries the exception, especially if it appears that we can assume an overall anthropological history of allopatric speciation. The trouble with the precise measurements, especially of type fossils and standards, is that many specimens fall out of categories as anomalous inbetweenies, such that the distances between these variations is less upon the margins as they are at the center. The fossil record, incomplete and often sketchy at best, fall silent in regard to the probable ranges of variation represented by fossils during any one period or in any one area, and this is even more the case when it can be considered that fossil preservation and survival was a very rare exception and not the norm. In essence we are using very small sample sets, of specimens that at least in one aspect are to be considered quite exceptional, as representatives of entire classes that presumably contained very large populations and normal distributions. This is why each additional fossil found is so vital and important in our reconstruction and interpretive efforts, as it adds a proportionately greater amount of knowledge about the range of variation of the traits we measure.

This may seem like a problem of the data, which it is, and not of the language that we use to describe this data, but it can be clearly demonstrated that when the gaps of knowledge are great in data-sets, then the language problem becomes even more pronounced as well as the cognitive and cultural problems involved. Large areas of unknown create a tendency for greater range of interpretation, definition, conception and ethnocentric appropriation, almost in inverse proportion to the sufficiency of our samples sizes. Increase the sample size, and the room for interpretive and semantic parallax will decrease considerably. Simply put, this defines a fundamental relationship between knowledge as this is linguistically articulated, and the factual reality such language is designed for. This defines the following kind of relativistic paradigm:

1. When the data sizes are small and the uncertainties large, this insufficiency of the empirical record will be reflected in the much greater interpretive variation and parallax. Speculation looms large under such circumstances, and the capacity to empirically test one's  assumptions remain very small.

2. When facts increase and the voids between begin to shrink, there tends to emerge a common ground of mutual agreement about which variation moves to the margins. Any area of knowledge will then exhibit a greater degree of consensus.

3. Increasing size of samples, an empirical theory of very large and representative numbers, will tend to factor variation out in the long run, resulting in what can be called  accurate representation of true natural variation of pattern.  This will foster increasing agreement about the middle range of the sample, and push uncertainty to the margins of the data.

4. However large the sample becomes, residual uncertainty will always remain. No data set will be perfectly representative of reality. At the same time, there can never be complete interpretive agreement across large data sets about which marginal uncertainty remains.

5. New facts may and will tend to arise always upon the margins, or in the interstitial spaces between our data sets, that do not fit our models or interpretations. There will always occur exceptions to any rule we may formulate, and there may always be one more Swan we haven't yet seen that is not white but black.

6. All data sets are by definition finite within a larger encompassing natural context. Even if our agreement as to the representation of a particular data set or kind of data sets is strong and uncertainty small, it is also the case that when these sets of data, whether as individual data points or as entire classes or sets, are framed within a larger set of natural relations, the degree of uncertainty about the unknown will then increase. In this sense, no science, however well worked out, like Chemistry, can be said to be a finished or complete science.

7. From the previous point, we can conclude that in knowledge, especially in scientific fields of inquiry that are concerned with the mysteries of reality, what is known is always encompassed by and forms a finite subset of what is unknown, and what is unknown remains, as far as we can conclude, probably open and infinite.

If we are to scratch the problem of anthropological relativity of knowledge a little deeper in terms of its structural factors and aspects, we might see that the language problem is inherent to the linguistic construction and definition of reality itself. We bring language intimately to the understanding and experience of reality, and we cannot separate our linguistically encoded experiences from the organic perceptions upon which they are based. We experience reality linguistically, and build meaning by the words of our language, and it for human beings it can be no other way. It is a fact of our evolution that we cannot escape--it is both our biological and our cultural imperative to see reality through a linguistic lense. The problem is not acute when there is shared categorical agreement over natural sets and problems mostly of concrete description, but it becomes acute in those areas where the problem shifts from being that of concise description to that of abstract explanation. Even in well received and supported theories, such as the theory of evolution, the problem of explanation remains an acute versus a moderate problem, for there are general aspects of evolutionary process and pattern that we do not yet well understand, and may never come to clearly or concisely comprehend, though many of the mechanical nuts and bolts are well worked out and substantiated through experimental research and naturalistic observation.

Then, in all the fields, anthropological relativity of knowledge shows itself in greater and greater proportions when we step up the empirical ladder of scientific representation from direct description of experience to indirect explanation of causality and structural dynamics. At the higher levels, not even physics that entertains some precisely formulaic and mathematically derivable theories, can escape the dilemmas of anthropological relativity as this applies to theoretical and general interpretation.

It has been one of the main arguments of this work, and previous excursions, that natural systems theory, if nothing else, provides a standard frame of reference for the generalistic and structural description of natural event patterns from a theoretical and comprehensive point of view. Natural systems that occur at any level of observation and phenomenal event pattern, exhibit structural similarities of pattern that are not just analogous nor are they necessarily directly historically homologous. Nevertheless, all such systems do necessarily conform to what can be called a general "template" of systemic patterning that is both complex and elegantly simple to understand.

The superimposition of a natural systems framework is not intended to force an arbitrary or preconceived framework upon all fields of knowledge and inquiry. It is intended only to provide a common and shared semantic-linguistic framework for the interpretation and integration of otherwise disparate areas of knowledge across disciplinary boundaries.

I do not believe that we can overemphasize the critical importance of anthropological relativity as a phenomenon of human reality that is intrinsic to our knowledge, its structure and function in the world. It is not just that such knowledge creates at times an irreconceilable sense of parallax about how we construe the world, but that it fundamentally constrains and limits how we see the world and come to know it upon the most fundamental levels. We have achieved a remakable degree of counter-objective parallax in our scientific knowledge systems, but this knowledge and its realism did not get arrived at overnight--it took a very long time to achieve, after many dead-ends and fits and starts. Furthermore, its progress remains incomplete. Science is always unfinished business--however high the next mountain we surmount, there's bound to be yet a taller one hidden beyond the horizon.

Relativity and Complementarity

We may say that relativity implies complementarity of perspective, and complementarity implies relativity of perspective. We may say furthermore that complementarity is derivative of relativity, and relativity is in turn based upon complementarity. These statements are metalogical in the sense that they imply both a relativity of complementarity and a complementarity of relativity.

Relativity implies that we can have different modes or different points of view about the same thing, and that, in our uncertainty, all alternative modes or points of view may be equally (and partially) uncertain. Relativity implies the notion of complementarity of perspective or point of view. Complementarity of view is a form of equivalence which states that different statements, both inherently contradictory, may be simultaneously be true. Such statements can be said to be mutually exclusive in antecedents, but lead to the same sets of consequents or conclusions in a manner that is inherently logical and empirically infallible. We would say that such statements would present a dilemma or a paradox about knowledge. Complementarity can only  be understood from the standpoint that such statements are part of a larger dialectical metalemma in the sense that they rely upon the inherent uncertainty of the problem for their reconciliation or mutual inclusion.

The Scientific Structure of Reality

Stratification and integration are implied in the concept of the complementarity of physical structure and behavior. Stratification entails integration, and integration at different levels entails a stratification of reality between these levels. Integration is marked primarily by the emergence of definitive properties that characterize a level of patterning in reality. Stratification is a mark of the separation between levels based upon the differences of property  and physical characteristics of systems, particularly their relative spatial-temporal distribution, their relative size scale, their relative density and their relative informational patterning that is associated with the complexity of pattern.

The natural world presents to us a very interesting set of properties. Size stratification determines that at whatever level we wish to define, there will always be a level smaller that composes the one we are at, and larger level of which our own level is probably but one small part. Each level represents in a sense an integration of properties that is only an appearance of structure. If we can jump to a smaller level, then we will find that what seems solid becomes hollow and vacuous, and what was once stationary seems to be a world of motion and turbulence.

In our reality, there appears to be an upper limit to the size stratification of physical entities, though this may in itself be only an appearance of reality if we fail to see a larger pattern of order of which we are but one small part. At each level of integration, different physical  forces appear to hold  sway--electromagnetic forces operate at a molecular level, and strong forces appear to operate upon a nuclear or atomic level. Gravitational forces appear to predominate upon a supermolecular level. The level of size scale upon which each force appears to operate is inversely proportional to the relative intrinsic force of the field. Gravitational energy is the weakest of all known forces, and works over the widest range. Strong forces appear to be at the opposite end of the continuum and is the strongest of known forces, but working over a very small range.  Each of these forces appears to contain a kind of field that defines a force of attraction.

It may be that there are even stronger and weaker forces that operate unknown and unseen on levels and in ways we do not yet comprehend. Gravitation may be in fact a kind of composite field of a set of forces of diminishing strength and broadening range. Forces associated with a predicted Higgs boson may prove to be the strongest forces we have yet encountered.

It is possible that, as given to speculation as Natural Systems science seems to be, we might speculate that the universe in fact organizes itself on scales both smaller and larger than we are aware of. The exponential jumps in scale from one level to another may entail that the larger scale of which we and our molecules are a part is vast indeed. It seems as those Black Holes, that compress matter to such densities that even strong forces are overcome, and matter as we know it is disintegrated, then we can wonder if this is not a kind of formation of structure in the universe upon a new level of stratification that we do not yet fully comprehend. Similarly, though a quark structure for nucleons seems self-consistent, is it not yet possible that these structures are yet composed of even more infinitesimal "entities."

In this enlargement or reduction of size scale we can only conjecturally and hypothetically determine if there is some fundamental lower limit or some universal upper limit to this process of stratification. It is entirely possible that we may never be able to prove this one way or another, regardless of our scientific advances. There is a sense that not only do things become too small or too large to "see," whether with the naked eye or aided by some optical instrument, but at a certain scale, both large and small, light itself becomes no longer effective in revealing the mysteries of the unknown. In other words, we are inherently limited by fundamental properties of light past which limits our ability to observe the universe becomes impossible. It may be possible to develop new means of observation, for instance, indirect observation through the presupposition of predictive cause  and effect, or, alternatively, by an alternative energy form such as gravitational energy.

I propose a theory of a non-zero and open-ended state universe. This theory predicts that reality is always:

1. Constitutive, or constituent or componential, or at whatever level we specify, there is always a smaller and a larger level to take into account.

2. Reality is discretely stratified at each  level, and each level of this stratification is largely self-consistent. In other words, properties pertinent to one level of stratification do not necessarily apply to any other level of stratification, and each  level of stratification is more or less independently integrated.

3. Understanding the apparent contradiction between 1 (constituency) and 2 (self-consistency) we must hypothesize that reality is at every level complementary to the system of reality as a whole. In other words, each level is distinct to itself, but  also is part of a larger system of organization, both smaller and larger, that, as a universal entity, has its own integrity and self-consistency as a system, even though it is infinite.

The interesting aspect of reality appears to me to be the concept of functional differentiation and integration that occurs at each level, such that there are synergistic properties that emerge at that level which are not apparent upon any other level of the larger system. This aspect of the stratification of reality appears to me to be fundamental to the structural description of this reality. The challenge of science is to describe how levels occur and are organized, and how one level can come to be constitutive of another. At each level of integration and stratification of physical reality, there appears to be a unique and holistic set of properties and traits that are distinctive to that level and that level alone, and yet, at the same time, each stratified layer appears to participate in and be part of a yet larger level.

Is it possible, for instance, that the discrete nature of an individual hydrogen atom or even nuclei might be lost to some extent when it becomes part of the solar plasma of the sun, such that a stellar system exhibits certain properties of mass and energy that cannot be clearly measured by the mere summation of its component entities. It is apparent that gravitational energy may be relative to the density of the system that is at its source, and it is apparent that other aspects of energy may also increase exponentially in direct proportion to the size, density and nature of the system. We see these emergent properties of physical reality readily in distinctions and phase transitions between gases, liquids and solids.

In this, we can divide the natural stratification of reality into the physical, the biological and the human (or anthropological). The pattern apparent at each  level is unique to that level, though this stratification is hierarchical. Biological systems are made up of physical  components, but exhibit life-patterns unique to such systems. Similarly, Human systems also are made up of biological components, but these systems exhibit patterns of communication, culture and symbolic cognition that are not apparent in other biological systems. The cultural patterns apparent with human systems are not available to other biological systems--only Chimpanzee groups to date have demonstrated rudimentary patterns of cultural acquisition, differentiation and transmission.

At each of the significant levels, the physical, biological and human, we can further sub-stratify into discrete sublevels. We can identify with physical systems what can be called the sub-atomic, the atomic and the molecular, or inter-atomic. At the subatomic level, we really find possibly a number of lower levels of stratification--only a host of subatomic particles are visible or available for indirect observation. These levels compound to form mass objects of visible size scale, and of a very large and grand scale of distribution. With biological systems, we can distinguish between the microscopic (organismic), the metascopic (organismic systems), and the macroscopic levels (super-organic system), with distinct patterns occurring at each of these levels. With human systems, we can distinguish between the individual, the small group, and the larger social system. At each level, we can also refer to the "total physical metasystem" and the "total biological metasystem"  and the "total human metasystem" as these encompass all subsystems together.

We may outline in a formal manner the main structural stratification of physical reality in terms of the informational patterning distinctive to each sublevel:

We may speculate in the model above that there occur at each primary sublevel of each main level (1, 4, 7) above, what can be referred to as multiple intermediate levels that are defined by hybrid precursors between the next intermediate level and the next higher level. In sublevel one for  instance, we can say that the subatomic level may  be further subdivided into any number of more fundamental levels of patterning that we are unable to observe. In sublevel four, at the micro-organismic level, we must understand that cellular growth and reproduction depends upon the availability in the environment of basic geo-chemical nutrients, both macro and micro nutrients necessary for cellular metabolism, as well as a vast array of complex organic molecules that are either manufactured by the cell or obtained from other cells. We can see at level seven that there are many relatively large to intermediate brained mammals and other creatures that deserve study as far as their mental functioning is concerned--rats and dogs have been observed to dream in ways similar to human beings, etc.

Another way of stating this is to observe the overlap between levels, and to observe how discrete entities can be isolated at each level that are not necessarily a part of a larger level of stratification. Each next level, contains by definition all levels below it, though the set contained within each higher  level  is only  a subset of the total at each lower level. Each next level is marked by increasing multi-level complexity of pattern.

In spite of speculation about possible infinitudes of reality, it is apparent that from an evidentiary standpoint, it is difficult to prove or talk about those things we cannot see or demonstrate through experimentation to exist. Therefore, based upon evidence available to us at this time, these levels described above define the intensive limits of informational stratification of natural systems in our reality. As mentioned above and emphasized below, this framework can very well change overnight with new discoveries and new theories about how reality is integrated.

Metasystems theory provides the conceptual and operational basis for the symbolic unification of human knowledge and information systems at multiple levels. The basis of this comprehensive unification is both scientific and ideological in a technical and formal sense. It provides a common paradigmatic framework for the interdisciplinary unification of different fields of knowledge within a common theoretical framework of understanding.

In general, a system may be understood as any set of interacting components that make up a whole functional entity within a background context that relates this system directly or indirectly to other systems at multiple levels. A system thus has a holistic design and patterning of state path behavior or translational structure that is more than the sum of the individual components that compose such a system. A system is always part of a larger framework or context within which that system normally occurs.

All natural systems are by definition open systems. The representation of a natural system as a closed circle is a misrepresentation, though such representations of functionally synchronic systems are common in the literature. A correct abstract representation of such a natural system is as a non-linear control system, that can spiral outwardly in growth or inwardly in loss, or that can fluctuate in some random or periodic manner. A straight line representation of such a system through time would be also a misrepresentation, as such a system would essentially be a static or non-dynamic system. Even an alleged line of equilibrium for a system would essentially be non-linear  in form, as all natural equilibrium is in essence dynamic equilibrium.

There is no real or naturally occurring system that is totally or perfectly isolatable as a system.

All systems are subsystems of larger systems, and all subsystems are larger systems containing other systems.

Systems are by definition complex composite entities the function of which is more than the operation of the individual components that make up the system. Such systems therefore exhibit what can be referred to as holistic, synergistic or emergent properties that are unique to that system. Thus, to understand any system in nature, we must seek to analytically discover how the component parts of that system interact in ways to induce such holistic patterning, and how the state-path trajectory of the system is influenced by the dynamic interaction of its components.

We seek to describe all systems as maintaining some sense of equilibrium over a period of its life-span. This equilibrium determines its state-path trajectory, this trajectory may take a number of alternative pathways that describe a paradigm of alternative states for such a system. We also seek to understand how this equilibrium is established, maintained and altered by the interactions of the components of the system and by the system as a whole upon the individual  components.

In our descriptions and explanations for the behavior of natural systems, we must realize and deal with a basic metalemma that whatever system we circumscribe, this will be part of a yet larger system that will have an influence upon this system in different ways. All systems are yet part of even larger systems, and are composed of yet smaller  subsystems, and there is no avoiding the complexity that this natural situation presents to us.

The only closed systems are abstract systems that are definable conceptually and mathematically. Even alternative, human-made systems are in their realization inherently open systems in some manner. If we build an automobile engine, with all its component subsystems, we still need to input fuel and air and output exhaust and heat. These inputs and outputs define such a system as fundamentally open to the world. Thus we may say that all alternative systems, as real systems, are open as well, even if they are artificial and non-natural systems.

All open systems are by definition subject mechanically to the laws of thermodynamics. For such a system to maintain its structural integrity as a system, it must therefore perform some complex set of functions that are definable as work. Work can be said to be the utilization of energy to maintain the functional organization and state-path behavior of a system.

All systems achieve functional integration by means of maintaining a functional boundary around itself in relation to its surroundings. Such a boundary defines the system as semi-closed and partially determined as a system. In general, a boundary of a system can be said to be a set of implicit limits of tolerance in relational organization and interaction of components of a system. These are essentially periodic interharmonic oscillating devices that control the behavior of the components of the system regardless of a tolerable range of conditions external to such a system. These relations are furthermore determinable in a functional manner as rules that govern the system.

Natural systems encompasses all real systems, but it is not clear that abstract systems are completely subsumable as a subset or a special class of natural systems. Without a biotic basis of natural intelligence, we could not conceive of abstract systems, thus they would not exist. At the same time, though, it is possible to make an argument that such systems would in theory exist whether or not we could conceptualize them or not. A perfect triangle would continue to remain so regardless of whether any human beings were alive to conceive of such an ideal form or not.

At this time, given our state of knowledge, we cannot clearly answer this kind of question one way or another.

All natural systems are also, by definition, self-organizing systems. They arise stochastically as the result of isotrophic trends and patterns affecting relationships between entities. The self-organizing character of natural systems has not be given enough consideration, although it has spawned chaos theory and the theory of non-linear systems. Self-organizing systems are by definition of their openness semi-determined or  only partially determinable systems. In other words, all natural systems are functionally underdetermined systems. They maintain boundary conditions, but these conditions are  never static, continuous or total in their control.

By contrast we may distinguish real alternative systems as systems that are partially determined but that are  essentially non-self organizing in nature. They have been designed and their relations predetermined by the nature of their design and fabrication.

Another distinction to make of such systems is between biotic and abiotic natural systems, or what I refer to as the distinction between physical and biological systems. The vast majority of natural systems appear to be physical systems. In essence all systems are essentially physical on a basic level. Biological systems as we know these represent only a very tiny subset of the total amount of physical systems that occurs. The critical distinction between a biological system and a physical system can be said to be the self-organizing process of a biological system that perpetuates its own design through generational reproduction and that is subject tosome form of evolutionary development. In other words, biological systems are self-replicating systems, whereas we can say that though physical systems are self-organizing, they are not in general self-replicating. Physical systems are produced as an outcome of a combination of forces and elements that leads to a significant reaction and a product.

Biological systems harness such combinations and replicate such reactions and products in a controlled and continuous manner. A subset of biological systems that is distinctive is in general what can be referred to a natural cybernetic systems. These are what can be called loosely and generally "intelligent"  systems. Human systems are the epitome of this general class of systems, and therefore deserve special attention and study as such, though it is increasingly apparent that they are not the only possible or existing intelligent system in the universe. Intelligent systems can be said to give rise to a new level of functional organization of systems, and to the creation of new, real systems that are artificial and non-natural. This definition of an intelligent system takes away from the classical definition of intelligence as "problem-solving," but it is more consonant within a systems theoretic approach. The conception of what is a "problem" and therefore what is a solution depends upon the ability to recognize and conceive of the problem in the first place, as well the ability to solve the problem in some logical manner. Behind this capacity is the ability of a system to recognize and organize "experience" or information in some coherent or consistent manner. In other words intelligence allows for the creation and design of new systems beyond its own design template. It permits a form of state-path behavior that can be described as "problem solving" on the basis of the design and development of alternative systems.

Relative classification of systems includes the identification of the natural hierarchy of determinations that serve to define and limit the behavior of systems. We can distinguish for a system at any one level a context or set of surroundings that embraces a supersystem framework, a set of alternative systems that function at a comparable level as the system in question, and a set of subsystems that compose the system. The alternative systems will define a range of possible forms that a system may take, the total range of which will define the system in a classificatory framework in relation to other systems. It can be seen in the relative classification of systems that any system is at once a subsystem of some other system, and at the same time a supersystem to a set of subsystems that compose it.

Systems may also be classified on a non-relative or absolute scale, and the basis for this absolute scale I believe to be that of the finite size a system comprehends on a scale of size measurement. Size can be measured in different ways, upon different scales, but the delineation of size for  a system defines that system in a total framework of dimensions that ranges between the infinitely small to the infinitely large. Size can be given a discrete and discontinuous value, and hence can serve to locate a system on a total scale of measurement compared to other  systems of different sizes. The paradox of the size-scale of natural and real systems is that the scale itself may be defined mathematically as infinite--both infinitely small and infinitely large in size. We set such a scale to a standard abstract value of zero, or of no size, and we define any  object, however infinitesimal, as of some value greater than zero. We can say that a non-relative systems of measurement of systems must by definition be zeroed, or zero-based, even if there is no system that can be said to be of zero-size. This  notion of zero in the universe has important implications for our understanding of the structure of physical reality. If we hypothesize that physical systems may only approach zero in some manner, as for instance kinetic energies of such systems that always approach Absolute Zero, but never reach zero, then we can hypothesize that such systems are constrained by a zero-determinant in some fundamental way that is essentially non-linear. In other words, we may assert the following:

Advanced Alternative Systems

Advanced alternative systems will increasingly depend upon the power of information technology and processing to achieve sophisticated integration and complex articulation with the environment. Information processing systems have made tremendous advances in the last couple of decades, and remain at the forefront of the applied sciences. Artificial intelligence is the name we give for  this rapidly developing and multifaceted domain of information sciences. 

The conventional criteria for the evaluation of artificial intelligence has been the von Neuman standard of the Chinese Room--implicit to this criteria has been the model of human intelligent functioning as the goal of  artificial intelligence development. This kind of standard criteria is inherently difficult to apply in an objective manner, and, because it embraces the inherent issues of anthropological relativity, it does not transcend the basic dilemmas inherent to human knowledge and intelligence in the world.

Furthermore, it is quite apparent that machine intelligence has as well certain critical non-human constraints that is inherent to their design and functioning as human made machines. These constaints are the following:

            1. All machine intelligence exists, or functions, in a closed world. This world is one that is built, managed and operated by human beings. Intelligent pattern that is the result of machine intelligence is a product of meaningful design, and may be employed  in the production of meaningful design, but it does not by itself produce meaningful design.

            2. All machine intelligence exists, or functions, in a manner that processes information in a linear manner. It processes strings of information, in series that occur in sequential order. Even parallel processing architectures are essentially the cofunctioning of multiple strings.

            3. All machine intelligence exists, or functions, in a manner in which there is no duality of patterning--the signal string contains the information, and the information conveyed by the string is a part of the string itself. In other words, machine intelligence exhibits no duality of patterning in its signal pattern.

            4. All machine intelligence exists, or functions, in a dead, or non-living state. It cannot be attributed the essential synergistic features of living biological organisms, or of what is referred to as "life." A dead state is one that cannot change itself except entropically. Thus, intelligent machines perform a certain or general kind of work, involving energy transfers and heat as a by-product, that results in the manipulation and production of meaningful pattern. Again meaningful pattern is merely  a by-product of this work.

            5. All machine intelligence exists, or functions, in a manner that can be said to lack awareness, either of the self or of the sense of surroundings.

These constraints all occur  at the same time, and are interrelated to one another in the design of machine intelligence. These kinds of constraints are inherently non-anthropomorphic, as there is not implicit comparison or contrast to human intelligence in their determination.

Technical reductionists would argue that human intelligence can be analytically reduced to the brain wave functioning of neurons that have an electro-chemical basis. This would not be an incorrect analysis to make. In other words, our own intelligence is machine-like just as much as any computer would  be by this reductionist model, and therefore ought to be subject to the same kinds of design constraints are are intelligent machines. Indeed, too, human intelligence is not unconstrained by basic design features and limitations. A brain too large for instance, or overactive, might face a fundamental problem of heat dissipation.

But, also in a technical way, each of these points can be used to contrast human intelligence with machine intelligence. Human intelligence does not exist in a closed world. It functions in an inherently non-linear manner. It has duality of pattern in its signal processing characteristics. It is a living machine, and it can be said to have an advanced form of awareness of both the self and the world in which the self is situated.

It follows that if these are the basic kinds of constraints that predetermine the possibilities of design for intelligent machines, then the design of more intelligent machines will proceed from understanding and as much as possible circumventing or nullifying these kinds of contraints. We measure the quotient of machine intelligence in terms of the degree of sophistication achieved in its functioning and existence along each of these five sets of points.

We can go further, if we wish to adopt a more anthropomorphic model of machine intelligence, then there are further criteria that we might wish to hold as human intelligence exhibits several other features of design that appear for the most part unique to our species:

            1. We are capable of the symbolization of experience, which is the symbolic definition of experience. Indeed, symbolization is such an inherent aspect of our intelligent design, that we cannot not symbolize experience except in the most rudimentary and impulsive of ways.

            2. We are capable of generalizing knowledge from one area or domain to another, and thus devising means of applying this knowledge to alternative domains to which it is not directly derived.

            3. We are capable of creative concatenation of experience and knowledge, to derive new patterns that have no precedence.

            4. We are capable of the linguistic transmission of information that conveys such experience from one person to another. Hence, we are capable of learning new experience based upon the experiences of other people.

These secondary criteria of an anthropomorphized machine intelligence appear to be most useful to the extent that they involve a human interface in a manner that permits the adaptation and mediation of human communication and activities upon multiple levels. I therefore consider these to be extrinsic criteria versus the intrinsic criteria of the design constraints listed above.

The dilemma of designing and developing more intelligent machines then is the challenge of trying to overcome fundamental, intrinsic and extrinsic constraints of design, that ultimately cannot be overcome in any known manner or by any known means. What is really accomplished in any simple mode is merely an Elizaesque-type parlour trick. Only by means of supercomplex programming and data-base structures might these constraints be approached in any meaningful manner. The challenge is that we do not have a firm idea in any detail of what kinds of designs these may entail, or that may lead us finally beyond the boundaries that conventional machine-like intelligence set for us. One of the best examples of a limited application is in chess and other game playing machines, which machines have increased in sophistication to approach the game-playing capacity of the masters, and even to exceed this capacity in exceptional circumstances. This is a set-piece type of problem, with finite search-solution spaces. The kinds and number of possible moves to be made at each turn are finite and fully determinable, though the number of alternative pathways that can thread through the entire system approaches an astronomical number. This kind of machine-intelligence solution to a limited and deterministic problem set was not achieved easily, but only by  along period of development and application that lead to refinement and sophisticated streamlining of the protocol. To apply a similar kind of complex solution to every deterministic kind of problem set that we can encounter, in whatever area or field of applied knowledge we wish to consider, exceeds by many degrees our greatest supercomputer capacities. This is much more the case if we take into consideration an even broader range of problem sets that do not have deterministic-type solutions, but remain relatively underdetermined in character.

It seems in this regard that intelligent machining in conventional problem solving is most successful if focused upon narrowly definable goals, and if it proceeds gradually in time from the ground up. The only top-down approach that we can take at this stage is to define a machine-based system of information processing and problem solving that extends the capabilities beyond component machines to incorporate a vast network of machines that interdigitate and articulate with one another in a organic manner. In the construction of such a model, a great deal of unknown problem-solving needs to be subsumed within a critical-path flow-chart that allows an object-oriented and functional partitioning of the general system into a minimal number of component  subsystems. Each system and subsystem must be tackled both separately and interdependently. Each presents its own complex problem set that can be only solved partially and incompletely. Within a larger  system, there will occur deterministic components that define the operational efficiency and intelligent capacity of the system as a whole, though such key components may not be easily or readily identifiable as such.

This type of system puts a premium upon the communicative capacity between machines and operating systems. The information bottleneck that is based upon the ability for processors to perform a certain speed of operations, is matched by a communication bottleneck that permits different machines to transmit, and receive, processed or raw information only at certain speeds or rates. Generally, in our current state of the art, machines have the be physically connected through transmission lines, and this has posed severe restrictions upon the ability to communicate. The alternative has been a kind of amplitude and frequency modulation of electromagnetic signals. Communicative capacity between machines is as much a challenge of devising a language of mutual intelligibility that would permit signals to transmit that were in a synnonymous with the kinds of signals occurring within the operating systems of computers themselves. In other words, the encoding of communiques between devices should be in the same programming language as the computer normally operates in anyway. There should be little requirement for translation interfaces or mediation to be interposed between different systems.

The challenge of constructing a distributed information processing system is in solving the communication needs at various levels and in various areas simultaneously. Communication distribution can be seen as a kind of hypergrid, distributed multidimensionally, each dimensional unit having its own channel capacity for communication separate or at least separable from those streams other dimensional units.

Just as computer processing streams are linear, so also do communication streams tend to be linear. Making multi-linear streams of communication are one way of attacking the problem, as is broadening the transmission breadth of the communication signal. A combined stream that mixes multiple signal carriers within the same grid unit, to be filtered separately by each receiving grid, is an alternative solution to this kind of problem. Within hardwired systems, this problem is readily solved by merely multiplying the number of separate lines interconnecting the various components of the system. Such a filter can be nothing but an embedded sequence of key identifiers that can recognize, for instance, every nth point of reiteration.

The challenge of intelligent communication is therefore the challenge of constructing complex systems of non-wired transmission based upon some range or set of ranges of electro-magnetic radiation, either focused as in laser systems, or broadcast.

A distributed system can be said to be a remotely connected supercluster of multiple  processing systems interconnected by communication lines based upon broadcast transmission of signals of various forms. Clusters and subclusters of such a distributed system can be said to be hard-wire integrated multiple  processing systems within the larger supercluster grid, presumably that perform either generalized or specialized or both hybrid sets of functions in coordination with other operating clusters. Thus, an internet system such as the world wide web, that connects mostly through telephone lines, is largely as yet a kind of cluster network that is not a truly distributed system. On the other hand, infrared based transmissions connecting office equipment with computers may be considered to be a distributed system. The scale of the system is not so important, I believe, as is the structural design of the system we are dealing with at whatever level. One of the means for a distributed system to achieve a degree of partial openness is through the development of an effective form of broadcast transmission between units. It can be demonstrated anthropologically that human systems and human intelligence could not have arisen outside of the framework of open linguistic communication.

Wireless systems have developed in relation to satellite communication, and these have grown increasingly sophisticated and powerful, as well as with decreasing degrees of noise and static, though they are far from meeting the standards challenges that would be required of a genuinely distributed system.

It follows that strategies of heuristic design are of paramount importance in the consideration of top-down distributed systems in which  the theoretic components exist in complementary manner to the achieved technology. In other words, even if present state of the art technology is relatively primitive and crude to the challenges and goals of any given problem set, it is in the meeting of ground-up practical solutions with top-down design configurations that progress will be defined.

It is something of a paradox as well that devising distributed, wireless based systems on the criteria of relative openness, may be based as well upon solving several other sets of primary constraints in computing--duality of patterning of a limited form is achievable in distributed systems if these distributed systems can interconnect via a common input-output interface and if this interface includes as well feedforward or feedback loops that include effective environmental monitoring on one hand, and effective motor articulation with the environment on the other hand. I am not referring to the conventionally, anthropoidal robot that walks and talks independently of some human controller. Rather I am referring to robotized systems that function independently to achieve a limited range of functional tasks in relation to its environment--such machines can take any form and perform practically any task. The desire to put these machines to human form is as much a reflection of our own anthropocentrism regarding intelligence as anything.

Achievement of a standard of duality of signal patterning can arise when a common communicative interface can be utilized in alternative contexts to achieve a range of different functional applications by independent and remotely connected machines. It entails the creation of a generalizing symbolic language in intermachine communication that can be adapted to fit a wide and open range of possible applications. This achieves a kind of limited duality that is based upon practical application of general terms to varying contexts. This is normally a trend that is opposite from what is expected with duality of patterning, especially if we adopt a strong psycho-linguistic model of language structure and patterning, though I believe it more accurately replicates what I believe are the actual parameters of communicative design in human language. It emphasizes the social aspects of language function as a communicative system around which cultural and psychological meanings can be built. In this alternative viewpoint, it is the intermediative function of language as a communicative system that is emphasized over the subjective meaning building aspects of any particular language system.

The challenge therefore of building a distributed network supercluster of machines that can perform a wide range of information-based functions in limited dimensions, is two-fold. It is a challenge of constructing a effective system of wireless communication that will permit the long-distance transmission of both large quantities of information at very fast rates, as well as a broad range of different kinds of information transmitted simultaneously or in tandem. It is also the challenge of constructed hard-wired systems as clusters and sub-cluster networks that fit within this multi-dimensional grid structure and that are capable of performing a wide range of alternative information-processing functions simultaneously.

A third challenge arises with the issue of control and coordination structures, in both hard and soft information architectures, that will be heuristically effective in incorporating the entire grid structure in a systematic and synergistic manner. I see such control and coordination as being decentralized and itself distributed at various levels in such a system. Control and coordination remains ultimately a human endeavor, except to the extent that a sense of relative autonomy of function and design can be designed into the architectures of such systems themselves. Self-replication of structure, learning and modification of architectures to fit alternative frameworks would be standards to achieve  in such control structures. Machine systems that are capable of running and managing themselves, with the fewest possible human inputs, and are even capable of building and repairing themselves, seem to be distant science fiction goals of intelligent design.

There is a sense in this issue, when viewed from the top-down, of a central strategic problem, a general or even universal problem set, that once articulated and fully defined, will lead by deduction and logical inference to the solution of a great many different kinds of problem sets. I do not believe there exists as yet any universal programming language to date that is capable of encompassing all possible logical chaining structures that are typical of intelligent systems. Machines capable of handling such languages would also have to be designed and built, and I do not believe this has yet been accomplished either.

The problem and challenge of constructing an intelligent distributed supercluster involves  an entire range of problem sets at multiple levels, each of which must be addressed separately, as well as in relation to the entire structure. We do not know yet what the best or most streamlined design or set of designs would be for the construction of such a system. It is apparent that no single kind of programming system, whether neural networks, or object oriented programming, or Lisp or Prolog programming, will completely address every dimension and aspect of the entire problem. It entails putting together the common and conventional approaches in Artificial Intelligence research, in the various applied and theoretical areas, into a common problem set that defines a single advanced distributed system. Thus the challenge of visual pattern recognition and vision is as much a part of the general problem of building such as system as would be the problem of voice  recognition, or of symbolic dependency, or learning or decision making or robotic manipulation or circumlocation.

There occurs a higher level criteria for these kinds of systems. This has to do with the achievement of a degree of generalization of worldview and of self awareness, and what can be called the emergent pattern of mental functioning from mechanical signal transmissions. Grossly, and in an unqualified way, we can refer to this as "consciousness" and we can say that a computer system, however sophisticated in design, lacks intrinsic consciousness. We can attribute  a sense of consciousness to mice and rats, as well as to humans and dolphins. We might even attribute some kind of limited consciousness to insects and other non-mammalian animal forms. But we do not attribute a state of consciousness to Deep Blue or to any other supercomputer we have built. The critical question to be answered is "why."

Integration proceeds at different levels and in different ways in the construction and design of distributed architectures. Functions are not completely separable from one another, and there occurs a great deal of overlap that, from the standpoint of informational efficiency, represents a load and a form of noise intrinsic to an underdevelooped and partially unintegrated system. Components must replicate similar kinds of procedures in the course of normal operation. In the best of possible worlds, each procedure would only need to be performed one time by one machine: the results of this procedure would then be stored and made  available for use by any other machine further down the road. Often, there are diminishing returns if retrieval of stored information, or the storage of information itself, requires a more informationally expensive procedure than reiteration of the original procedure in the first place.

There is a fundamental trade-off it seems, between the problem of integration on one hand, that combines subsystems into a single hard-wired "cluster" and the problem of distributed processing, which serves to link different systems or clusters into a coordinate network. It seems that we can improve systems integration through hardwiring, but only at the expense of maintaining truly and remotely distributed networks. On the other hand, if we wish to extend distributed networks to encompass broader ranges, then the price we pay is in our ability to integrate systems as a single operational unit. In a sense, with the problem of distribution, the  challenge of effective communication between different systems becomes paramount over the challenge of processual integration into a single system.

The concept of unit operations is an important approach to take in applied metasystems and in the design and coordination of different systems. Operational units define unit operations as basic common functional denominators, and provide thus a shorthand for design of more complex systems. A limited number of basic operations, for instance, can be recombined in a countless number of ways to achieve alternative complex systems.

Possible Systems and System Possibilistics

Hypothetical Possibilistics

Random Statistics, Entropy, Chaos Theory & Stochastic Process

Any system that is unknown is a potentially possible system. A possible system is one that exists hypothetically, rather than as theoretically or practically demonstrated. A possible system is essentially an unknown system, and the only means we have of realizing the possibility of a system is through a means of exploratory discovery of the possibilities.

We lack a means of systematically investigating the possible, or of easily distinguishing what might be possible from what must remain always impossible. The trouble with the unknown is that we cannot tell what is merely unknown from what is ultimately unknowable.

The problem with our ignorance, and the unknown, is that we do not and cannot know before hand what is truly possible and what might be ultimately impossible. The idea that there might be systems of anti-gravity or possibly of faster-than-light travel, remain concepts that we can entertain but cannot determine the possibility of one way or another. Our dilemma becomes that if we dismiss the possibility of some idea, however seemingly impossible, we automatically preclude the possible search and discovery of what may in fact prove to be possible. Perhaps faster than light travel is ultimately impossible, and this may be why alien civilizations more technologically advanced than our own have not visited us, but this remains conjecture we cannot yet prove one way or another.

It has been shown that metaphysically and naturally, chaos underlies and is more basic to order, and all real systems tend, in the structure of the long run, to return to a state of greater disorder. It is worthwhile therefore to take into more careful consideration the problematic that the notion of chaos implies for our understanding of advanced systems.

In an abstract sense, absolute systems, as for instance, mathematical systems are only possible if they have an implicit and antithetical counterreference to absolute disorder and chaos. We can say that they achieve their coherence by the absolute determination of their values and relations, leaving no room for uncertainty. Thus, in such a world, uncertainty is excluded to a domain of the implicit. Underlying this is a sense that uncertainty and disorder are inherently and ideally disordered. Consider trying to generate a list of random numbers off the top of your head--can you be sure the list of numbers you generate are completely random. If you think about them, and attempt to rearrange them so that they appear to exhibit less patterning, might you not be imposing some sense of order upon them?

Much of probability theory can only be construed from the standpoint of a hypothetical "null space" that is defined by total randomness and randomization, which is itself an ideal state that is never attained in nature or real systems. Indeed, the entire structure of mathematics as we know it could not exist without the central notion of zero as a common point of reference. Without the notion of zero that implies nothingness and hence disorder, we could not have equations or perform many operations that are common to mathematics.

To try to treat disorder in a systematic manner, to deal with it in terms that are complementary and integral to systems theory, is to try to put a handle upon a significant aspect of reality that influences every real system that exists. The outcome of chaos theory is that even high complex systems can be based upon relatively simple operations, and relatively simple formulas can generate highly complex and unusual outcomes. But not all disorder is capable of being patterned--in any system, there should always be some residual sense of true disorder that cannot be accounted for by any means.

I hope to demonstrate thereby that there can be found order in disorder, and we can superimpose a sense of system upon a sense of disorder itself. We do it not out of some strange pathological compulsion to minimize uncertainty and chaos. We do it rather out of necessity in our theorems. If we don't, then there remains a residual possibility that, in failing to deal adequately with the tasks at hand, these issues will somehow creep into our formulas and undermine our ability to functionally extend our theories to real systems.

In attempting to do this, I am not so interested in stochastic theory and probability, as I am interested in a system of possibilistics that must underlie any kind of stochastic estimation. Before we can judge the odds, we must know the playing field we are dealing with in a manner that allows us to make such choices. However uncertain, our knowledge must somehow move from remaining remote and unknown to being proximate and at least inferable.

If we wish to derive some kind of sample, whether representative or randomly, then we must at first understand the possible sample spaces or regions that are available to being sampled, and that are defined as those that are interesting by the criteria of our theory and its operationalization. But often we cannot know beforehand the possible sampling spaces that might be important to our operational procedures.  Much that might be of value to us in possible sampling domains must remain unknown--this is part of the reason we sample in the first place. It represents a kind of exploration of unknown areas. Similarly, if we seek some solution to a problem, we are at first confronted with a potentially infinite number of possible choices and alternatives. We must pick and choose a pathway based upon some series of choices that will lead to a successful solution to the problem. Often, we cannot know not only the correct choices, but even the possible range of choices to begin with. In complex problems, we may construct initially complex search tree structures, but none of the possible outcomes may necessarily lead to the correct solution.

The question of possibilistics therefore leads directly into the problems of operationalization of procedures, an issue that will be undertaken in the next part.  It deals especially with the heuristics of problem solving, and many issues broached in this chapter relating to initial problem definition and identification will be taken up further in the second part. At this point, all I wish to do is to elaborate a form of continuous and nondiscrete statistics, or variable statistics, that can be used to conceptualize alternative possibilities for any given problem. In this regard, the problem is especially the issue of the constructive representation and application of alternative metasystems models to real working models in any number of different areas. This in itself creates a large space of possible alternatives that should be considered part of the issue.

If we start with a simple 10 by 10 matrix, such that any slot within the matrix may be filled with a number 0-9, and our job is to create all possible combinations or permutations of strings occuring in rows or columns of the matrix, we quickly find that we have an overwhelmingly complex number of possibilities, something of the order of 2 x 10¹⁰ . What appears at first as a rather simple square matrix of a very manageable size, quickly zooms to astronomical complexity when we begin searching for its solution. If we built a computer program to generate by recursion or reiteration all these possible strings, it would require a very long running time, and would be liable to consume the working memory resources of the whole computer.

As one computer science teacher told me, consider trying to realistically represent and explain the orbits of all the lunar bodies of the solar system about their planets, as these spin about the sun, and then try to fit this into a larger pattern of motion of the sun within the galactic system it occurs in. Though the motions are elegantly described by mathematical equations and are sublime observed through telescopes in the night sky, actually plotting these complex astronomical ballet moves is virtually impossible.

We could impose rules upon our matrix problem to narrow its search space. For instance, we could specify all strings that are only of a certain length, or that have a certain initial order, say 999. Doing so would limit the total space of possibilities considerably.

It is perhaps part of the project of possible statistics, or possibilistics, to be able to get an idea of the inherent complexity represented by any problem set, without having to reach a complete solution to the problem. In other words, if a simple problem proves to have an astronomically complex solution set, then it is better to represent the problem as some kind of recursion function than as a complete solution set of alternative sample points. This is clearly the case in most of the sciences, even on very basic levels. We always prefer the correct formulas to the actual solutions to any particular problem.

It is often the case that unintentionally, rather sophisticated and straightforward statistical problems require strict randomization criteria that prove almost impossible to meet, especially with large sample sets. It is the epitomy of wisdom in such cases to systematically restrict the problem set down to some narrow range of possbility within the larger spectrum in order to achieve more control and accuracy of the results. It is a case in statistics that the law of large numbers does not necessarily apply if you have a genuinely or relatively random sample--you can have the largest sets possible but it would mean nothing if they were not randomly selected.

Many theories, especially in statistics, rest upon a presupposition of purely random samples. Often, this is taken for granted, or fudged, when in fact it is truly difficult if not completely impossible to create a truly random sample, especially with people. Determinisms creep into our database in many different ways, often without our understanding. But this in itself is not necessarily a bad thing. Some kinds of surveys that can generate deep knowledge and understanding are not necessarily contradicted by the presence of bias in samples. It is possible that even with great bias, samples remain true to life and representative of the reality they purport to explain.

Probability theory has been well worked, as many people have purported to depend upon it. But possibility theory remains something of an unsolved mystery, and therefore is something more worthy of understanding for its own sake. I would call possibilistics a form of statistics that comes before description. Perhaps it can be called observational statistics. It does not necessarily presume randomness in the research design. Rather it presumes only a natural self-organization of pattern irrespective of our own observational biases we may introduce into the sample organization.

In attempting to get at abstract systems and general theories of natural systems that have universal validity, it is important to resolve a basic consideration having to do with the inherent complexity of naturally occurring systems. Multi-variable nonlinear differential equations are insufficient to express the entire problem set at even rudimentary levels of naturally occurring phenomena. Such equations remain beyond proof or even solution, but it remains possible, within a paradigm of possibilities that are defined by basic parameter variables, to create a generative system of differential equations that are interdependent upon one another in terms of their input and conditional values and variables.

The consequence and possibility is to be able to produce a parsimonious mathematical model of complexly developing systems without having to resort to working out solution sets for every differential equation that is encountered. Such equations would be derivative and based upon other equations, which in turn would be based upon and derived from yet other equations, which eventually would resolve to single variable, soluble equations that operate within a possible range of discrete or continuous input values. A complex natural system would then be expected to be solved in terms of an interrelated set of complex differential equations that would be framed within a paradigm of possibilities, a matrix that is composed of simpler sets of equations, and so on. The solution we would seek would be in terms of a simulation of the pattern based upon the set of governing equations that we define for that system. The degree of achieved detail and representative accuracy would be a measure of the degree of closeness of fit between the real system and the artificially contrived one. It becomes possible to express a complex theory of systems accurately in terms of a single general differential equation that can be unpacked by its systematic qualification of variables as derivatives of nested differential equations within a matrix hierarchy. 

It would be necessary as well to build into such equations the uncertainty factors that would provide for the under-determination of structure that all naturals systems exhibit.

It has been shown that metaphysically and naturally, chaos underlies and is more basic to order, and all real systems tend, in the structure of the long run, to return to a state of greater disorder. It is worthwhile therefore to take into more careful consideration the problematic that the notion of chaos implies for our understanding of advanced systems.

In an abstract sense, absolute systems, as for instance, mathematical systems are only possible if they have an implicit and antithetical counter-reference to absolute disorder and chaos. We can say that they achieve their coherence by the absolute determination of their values and relations, leaving no room for uncertainty. Thus, in such a world, uncertainty is excluded to a domain of the implicit. Underlying this is a sense that uncertainty and disorder are inherently and ideally disordered. Consider trying to generate a list of random numbers off the top of your head--can you be sure the list of numbers you generate are completely random. If you think about them, and attempt to rearrange them so that they appear to exhibit less patterning, might you not be imposing some sense of order upon them?

Much of probability theory can only be construed from the standpoint of a hypothetical "null space" that is defined by total randomness and randomization, which is itself an ideal state that is never attained in nature or real systems. Indeed, the entire structure of mathematics as we know it could not exist without the central notion of zero as a common point of reference. Without the notion of zero that implies nothingness and hence disorder, we could not have equations or perform many operations that are common to mathematics.

To try to treat disorder in a systematic manner, to deal with it in terms that are complementary and integral to systems theory, is to try to put a handle upon a significant aspect of reality that influences every real system that exists. The outcome of chaos theory is that even high complex systems can be based upon relatively simple operations, and relatively simple formulas can generate highly complex and unusual outcomes. But not all disorder is capable of being patterned--in any system, there should always be some residual sense of true disorder that cannot be accounted for by any means.

I hope to demonstrate thereby that there can be found order in disorder, and we can superimpose a sense of system upon a sense of disorder itself. We do it not out of some strange pathological compulsion to minimize uncertainty and chaos. We do it rather out of necessity in our theorems. If we don't, then there remains a residual possibility that, in failing to deal adequately with the tasks at hand, these issues will somehow creep into our formulas and undermine our ability to functionally extend our theories to real systems.

In attempting to do this, I am not so interested in stochastic theory and probability, as I am interested in a system of possibilistics that must underlie any kind of stochastic estimation. Before we can judge the odds, we must know the playing field we are dealing with in a manner that allows us to make such choices. However uncertain, our knowledge must somehow move from remaining remote and unknown to being proximate and at least inferable.

If we wish to derive some kind of sample, whether representative or randomly, then we must at first understand the possible sample spaces or regions that are available to being sampled, and that are defined as those that are interesting by the criteria of our theory and its operationalization. But often we cannot know beforehand the possible sampling spaces that might be important to our operational procedures. Much that might be of value to us in possible sampling domains must remain unknown--this is part of the reason we sample in the first place. It represents a kind of exploration of unknown areas. Similarly, if we seek some solution to a problem, we are at first confronted with a potentially infinite number of possible choices and alternatives. We must pick and choose a pathway based upon some series of choices that will lead to a successful solution to the problem. Often, we cannot know not only the correct choices, but even the possible range of choices to begin with. In complex problems, we may construct initially complex search tree structures, but none of the possible outcomes may necessarily lead to the correct solution.

The question of possibilistics therefore leads directly into the problems of operationalization of procedures, an issue that will be undertaken in the next part. It deals especially with the heuristics of problem solving, and many issues broached in this chapter relating to initial problem definition and identification will be taken up further in the second part. At this point, all I wish to do is to elaborate a form of continuous and nondiscrete statistics, or variable statistics, that can be used to conceptualize alternative possibilities for any given problem. In this regard, the problem is especially the issue of the constructive representation and application of alternative metasystems models to real working models in any number of different areas. This in itself creates a large space of possible alternatives that should be considered part of the issue.

If we start with a simple 10 by 10 matrix, such that any slot within the matrix may be filled with a number 0-9, and our job is to create all possible combinations or permutations of strings occuring in rows or columns of the matrix, we quickly find that we have an overwhelmingly complex number of possibilities, something of the order of 2 x 10¹⁰ . What appears at first as a rather simple square matrix of a very manageable size, quickly zooms to astronomical complexity when we begin searching for its solution. If we built a computer program to generate by recursion or reiteration all these possible strings, it would require a very long running time, and would be liable to consume the working memory resources of the whole computer.

As one computer science teacher told me, consider trying to realistically represent and explain the orbits of all the lunar bodies of the solar system about their planets, as these spin about the sun, and then try to fit this into a larger pattern of motion of the sun within the galactic system it occurs in. Though the motions are elegantly described by mathematical equations and are sublime observed through telescopes in the night sky, actually plotting these complex astronomical ballet moves is virtually impossible.

We could impose rules upon our matrix problem to narrow its search space. For instance, we could specify all strings that are only of a certain length, or that have a certain initial order, say 999. Doing so would limit the total space of possibilities considerably.

It is perhaps part of the project of possible statistics, or possibilistics, to be able to get an idea of the inherent complexity represented by any problem set, without having to reach a complete solution to the problem. In other words, if a simple problem proves to have an astronomically complex solution set, then it is better to represent the problem as some kind of recursion function than as a complete solution set of alternative sample points. This is clearly the case in most of the sciences, even on very basic levels. We always prefer the correct formulas to the actual solutions to any particular problem.

It is often the case that unintentionally, rather sophisticated and straightforward statistical problems require strict randomization criteria that prove almost impossible to meet, especially with large sample sets. It is the epitomy of wisdom in such cases to systematically restrict the problem set down to some narrow range of possbility within the larger spectrum in order to achieve more control and accuracy of the results. It is a case in statistics that the law of large numbers does not necessarily apply if you have a genuinely or relatively random sample--you can have the largest sets possible but it would mean nothing if they were not randomly selected.

Many theories, especially in statistics, rest upon a presupposition of purely random samples. Often, this is taken for granted, or fudged, when in fact it is truly difficult if not completely impossible to create a truly random sample, especially with people. Determinisms creep into our database in many different ways, often without our understanding. But this in itself is not necessarily a bad thing. Some kinds of surveys that can generate deep knowledge and understanding are not necessarily contradicted by the presence of bias in samples. It is possible that even with great bias, samples remain true to life and representative of the reality they purport to explain.

Probability theory has been well worked, as many people have purported to depend upon it. But possibility theory remains something of an unsolved mystery, and therefore is something more worthy of understanding for its own sake. I would call possibilistics a form of statistics that comes before description. Perhaps it can be called observational statistics. It does not necessarily presume randomness in the research design. Rather it presumes only a natural self-organization of pattern irrespective of our own observational biases we may introduce into the sample organization.