Lewis Works

This open, on-line Newsletter is published weekly, every Friday Afternoon at 5:30 PM PST. It is updated with new announcements and articles each week.

Lewis Works Newsletter*

*Back to Basics II

The E-zine of Applied General Systems Science

By Hugh M. Lewis, PhD, MA, general editor

Vol. I, No. 13

4/23/04 Copyright 2004 ©, Hugh M. Lewis.  Facsimiles of this page or parts of this page may be printed and distributed for non-profit research, consulting and educational purposes only, as governed by fair use policy.

On Top

We are announcing our newest Nepal Program & Penang Project, as well as the launching of our Local Worlds in the British Virgin Islands, Dunedin (New Zealand), Invercargil (New Zealand) & Stewart Island, (New Zealand). Details will follow in next weeks Newsletter.

We are also announcing our new three-tiered membership framework. Details for membership may be found next week by following this link to our new membership page: 

http://www.lewismicropublishing.com/Newsletter/Membership/

Criticisms/Comments, then Provide Feedback

On Top

Our newsletter is published once a week at 5:30 PM, Pacific Standard Time, Fridays.

We are continuing with our back to basics. We are focusing this week on some basic concepts of simplification and complication in systems development with an eye and ear to saying something as non-trivial as possible in this subject area.

We invite your open involvement in our framework. We are creating a new membership program, open to all comers. The full details below, upon three levels: free membership, basic membership & premium membership. 

Mission Statement

Lewis Works is dedicated to realizing new human adaptive possibilities in order to create alternative long-term frameworks for human & biological systems development on earth and beyond.

The primary mission of Lewis Works is to fundamentally empower all human beings, without regard or reference to their individual or cultural differences, so that they may function in a more constructive and non-violent manner by means of their integration within an applied systems framework that enables them to contextualize and focus their independent developmental efforts toward comprehensive solutions to common problems in resource distribution, environmental adaptation, and social-structural interaction.

• 1. Lewis Works seeks alternative meta-systems based development through applied general systems with the main goals of:

• a. Achieving a mutually stable and harmonic balance between future human systems and earthbound biological systems.

• b. Providing all human beings in unbiased structural or cultural contexts the alternative systems-based frameworks for their individual & social development by means of increased opportunities, productivity, security and resource availability that they would not otherwise have in conventional frameworks.

• c. Developing the infra-structural context and means for the regular extension of human and biological systems beyond the boundaries of the earth.

• 2. Lewis Works is dedicated to achieving a better world for all people and for all life-forms through the implementation and articulation of an applied general systems framework to general and specific problem sets that occur in the adaptive organization of human behavior in a shared natural environment.

• 3. Lewis Works is non-exclusive, open, non-authoritarian, philanthropic and pacifist in orientation.

• 4. Lewis Works pursues a combination of both profit and non-profit programs and projects to the achievement of its main goals.

• 5. Lewis Works protects and promotes universal human rights and human responsibilities throughout its various programs and projects by the systematic pursuit of human development strategies.

• 6. Lewis Works is law abiding and honest in all its dealings and transactions in all contexts, and respects and honors the customs and manners of all peoples and all ethnocultural groupings.

• 7. Lewis Works protects and promotes the confidentiality and legitimate interests of its clients and customers under all circumstances and in all cases.

• 8. Lewis Works seeks to efficiently provide a comprehensive range of profit-based services and related product lines within an open, web-based forum of exchange that is global in scope, regional in character, and local in focus, and that serves as the basis for the development of a structurally open meta-systems based context in the world transcending local, regional and national identities and affiliations.

• 9. Lewis Works seeks to promote non-profit programs in alternative human development for the sake of alleviating human suffering, educating people openly and in an unbiased manner, and promoting pro-social human development.

• 10. Lewis Works seeks to create trans-national meta-cultural orientations in the world through various organizational frameworks that promote open, democratic principles of government, fair-play and the rule of just law, and through the development of anti-structural multi-media based systems that provide humanity a common symbolic context for their meta-cultural integration.

Simplicity & Complexity are mostly relative concepts that are critical to the understanding, symbolic representation and heuristic modeling of real systems or the realistic representations of ideal systems. These concepts are largely complementary to one another in implied or explicit meaning, and the use of each implies the other as an antithesis and an antonym. We really cannot comprehend something like "absolute simplicity" unless we refer to a concept like "nothing" or absence. We are even hard pressed to define either simplicity or complexity in a certain, non-relative or non-comparative way. 

What I am searching for is a single, abstract model of an ideal simple system--this is something like a simple machine, a screw perhaps, or a lever, or an inclined plane. By means of explicit contrast, I am also searching for a valid, abstract definition of complexity that would aid our fundamental understanding of all kinds of systems generally.

I will deal first with each definition separately, and then consider the relationship between the two contrasted/complementary concepts:

A. Simplicity

A simple model of an ideal automaton approaches the definition of a single simple system. It presupposes some initial input string with start/stop signals, some process of transformation that is a consequence of systematic operations of the input string, and some set of outputs that reflect the predictive transformations to the original input string. This, by itself, does not necessarily define a very simple system. Key to this model is the concept of change or transformation of the input into some predictable set of outputs. 

At this juncture, we may distinguish real versus ideal systems in terms of the nature and degree of determinancy of the output of such a system--only ideal systems can have perfectly determined outputs (in the sense of being potentially predictable) while all real systems can have only partially determined outputs, the measure of productivity & informational fidelity of the system being the overall percent of output that can be correctly determined based upon the information of the inputs and an understanding of the transformational process that lead to the outputs. We can only approximate the output conditions of any real system, and this approximation takes both the forms of the entropy of a physical system, and the informational loss or noise of the pattern system.

All real systems may be defined therefore on two corresponding & complementary levels--the informational patterning level of the system, which largely defines states and state relationships occurring between variables, and the thermodynamic (and gravitational dynamic) patterning of the system upon purely physical level, which defines the measures of energy transaction occurring in the system under specific conditions. I would regard this as a basic characteristic of all real systems--they can be represented in physical and non-physical (abstract) terms.

Ideal systems can in theory only be represented in abstract (non-real) terms, but they can be modeled and exemplified in an applied and approximate (inexact) manner in terms of real systems. Ideal systems therefore come to represent in a general manner real systems, and to be generally represented by real systems through phenomenal instantiation.

In terms of real systems, we may stipulate that any system, to be classified as a real system, must be capable of being demonstrated on two levels simultaneously--in terms of the informational patterning represented by the relational organization of the components of the system, and in terms of the actual physical articulation of the system itself. If anything lacks these two levels of systems integration, then they cannot be called a "system." For instance, if we consider the minimal definitions of basic living systems, the cell for instance, we see the basic prerequisites met for a simple (read general or theoretical) system. All cells must be capable of performing two sets of complementary functions, retention & transmission of information (DNA code) and the metabolic-energetic functions of maintenance, repair & replication of physical cell structure (generally accomplished through RNA transcription processes leading to protein molecule production & function.) Of course there are minimal cellular structures that are universally associated with these primary functions and purposes of the cell as a living system, and these can be identified in any bacteria or in any plant or animal tissue.

To extend our example and metaphor a bit, we may state that while the function of all cell walls will be to provide protection from an outside environment for creating an internalized cell environment, a "boundary maintenance mechanism," we may also state that a cell membrane, characteristic of Eukaryotic cells, unlike common and simple bacteria, is to also mediate the transport of molecules between external and internal environments, and in many cases to maintain tissue structure for the resultant organism as a whole. Bacterium generally do not form coherent extra-cellular structures unlike multi-cellular Eukaryotic systems, unless we count the molds and colonies found on agar gels of petri dishes as having significant organismic structure. Whether any particular cellular structure is a simple cell wall or a more complex cell membrane construction is perhaps a debatable issue depending upon one's frame of reference, but it seems to make all the difference in the living world between a relatively simple bacterium and a complex organism like an elephant or a tree.

Another way of putting this is to state that one of the basic differences of cellular structure separating on the most basic level all known living organisms, is in the fundamental structure of the "boundary maintenance" mechanism which serves to partition and mediate between the cell as an internalized system from its extra-cellular environment. This is of course not the only difference, the other being the presence/absence of a complex nuclear structure as well as the presence/absence of other organelles or cellular structures. How DNA is wrapped or not within the Nucleus, and how it becomes translated through RNA into protein structures outside the Nucleus, and how it becomes replicated as new DNA, are other critical differences that fundamentally distinguish between basic kinds of all known living systems.

Returning to the main problem of definition Simplicity as a central systems concept, it should be remembered that a cell is anything but a simple system, as attested to by the billion plus protein structures and interactions that recur regularly within its narrow confines (from a standard, everyday human point of view.) Nature has not yet yielded to human observation or human thought genuinely simple systems, unless we are willing to consider philosophically randomness as an indication of simplicity. From this standpoint, death is perhaps the simplest possible state of all living systems--but a dead system is no longer a living system, it is merely an meta-biotic environment for other living systems. Think about it, without the intervention of other meta-biotic systems, death would not necessarily entail decay, only cessation of functional process in an orderly manner. We have examples of bacteria free environments in which preservation of tissue structures is extended indefinitely--the only change agents being desiccation or molecular level replacement/exchange of structures.

The closest perhaps I've come to the proposition of genuine simplicity in physical systems is in terms of any hypothesized "zeroth enterty" that is perhaps in its behavior anything but simple, being able to occur anywhere at once, and potentially having an infinite speed of travel.  Perhaps binary information is the simplest form of information we can conceive of, even if it quickly leads to complex string structures in digital computing systems--either yes or no, on or off, up or down, right or left. It perhaps makes sense to think of hypothetical "nth-particulates," as constructions of "zeroth enterties" (i.e., energy-entities), as somewhat minimal systems structures that may carry single-minded properties like "on/off" or "right/left" or "up/down." The specification of the exact property would ultimately be arbitrary, the important point being that such properties would embody complementary & contrastive relationships of states, however temporary, however small in size.

I have beaten around the bush of simplicity, but I have not broached the problem of simplicity directly. For any system, we may stipulate some sequence of states of finite entities:

A. Start (Input) State.

B. Intermediate Transformation State (State Change).

C. Stop (Output) State.

This formula does not yet completely describe a system, as there is a feedback relation that is part of the boundary-maintenance mechanism that must be accounted for, such that Stop State (1) becomes Start State (2) in a reiteration of the cycle:

A1. Start (Input) State.

B1. Intermediate Transformation State (State Change).

C1. Stop (Output) State

D1. Stop State (C) = Start State A2; repeat Cycle.

A system is therefore somehow cyclical in its operation with one set of output states leading back to the next set of input states. While this is diagramed in a discontinuous and reiterative manner, it doesn't need to be so--we speak of recursion in programming functions in which feedback is implicit to the formula itself and continuous and automatic in function.

Since a state of no-change cannot be considered a system, the simplest possible kind of system we can construct is one for which every input or input state, there is definable one and only one state change leading to a single output state. From this standpoint, such a simple system would be considered as totally determined--transformation states can permit no variation of pattern leading to alternative output states. Such a structure is not changeless, or unchangeable, but the process of change has been narrowed down to a single set of processes along a single set of dimensions.

In my small mind at least a simple thermostat, used as the classic example of a basic resonance feedback/cybernetic system, is representative of a relatively simple kind of system. Change the dial, heat turns on or off until a new state of equilibrium is reached, then heat turns off. A toaster is another "dead brain" mechanism that is relatively simple, put in fresh slices of bread, push down the lever, wait a few seconds, and out pops burned toast (given that the toaster is plugged in, off course).

Another gross example of a relatively simple system would be Pavlov's classic condition experiments with dogs--S-R conditioning such that the dogs brain & physical response set comes to automatically, reflexively associate the ringing of a bell with the immediate availability of food, with the expected consequence of salivation. Of course, a dog in natural behavioral settings is anything but a simple S-R type of system--watch dogs well trained with their masters and it is evident they have some conception of a larger behavioral gestalt guiding their actions, and they initiate their actions autonomously, actively, without need for conditioned reflex. 

Ardent behaviorists used to argue that all dog behaviors are fundamentally S-R, if we could reduce it systematically down to basics. They would like to include all human behaviors, and all behavior of biological organisms, in this kind of reductivist analytical model. But from a purely brain standpoint, such a system seems overly complicated and unwieldy--at least to the observer trying to figure it out, if not actually to the dog performing a complex series of sequences on the fly. The model breaks down in consideration to natural dog behavior largely because of its overextension of simplified forms, and the inherent complication this entails. We must hypothesize that somewhere in the process the dogs brain kicks in upon a higher level, and initiates a form of patterned response that is at least partially independent of libidinal drive, stimulus activation or association, or instinctive reflex. Instinct itself must be seen as a form of patterned response, not needing to be learned, that is inherent to the structural organization and function of the dogs brain and behavior. This strictly speaking cannot therefore be merely S-R type response patterning.

Another way of looking at simplicity in behavior and behavioral development is in terms of the definition of relative differentiation of function and articulation associated with progressive development. Simple processes and patterns of organization tend to be relatively unspecialized (i.e., non-specific), undifferentiated, unelaborated, with relatively direct results deriving from basic and relatively singular causes. This kind of definition of simplicity is especially applied to systems-based perspectives in human development and behavior. 

Complexity

Now that we have beat around the bush of simplicity for a while, it is worthwhile switching directions and attacking the thornier complementary problem of complexity. I will start by saying that there is a critical difference between a system that is complex on a basic level, and one that is complicated by extension of otherwise simple relational patterning. We may refer to this kind of difference as between basic complexity, on one hand, and derivative or elaborated complexity, on the other. Or, more curtly, complexity is not synonymous with complication.

It can be said that all natural systems, in their development, start out complex and tend toward greater complication. This is a trend apparently in reverse of thermodynamic-entropic tendencies towards randomized state equilibrium of systems with their environment. This is a basic difference highlighted by Von Bertalanffy in distinguishing between what he defined as open and closed systems. A further difference he noted was in terms of the interrelation complexity of open systems that went far beyond simple cybernetic or feedback models, and encompassed an entire range of variables in interaction.

We might offer a model of a complex system based upon the paradigm of simple systems we offered above:

For any system, we may stipulate some sequence of states of finite entities:

A1. Multiple Possible Start (Input) States.

B1. Multiple Possible Intermediate Transformation State (State Change).

C1. Multiple Possible Stop (Output) State

D1. Alternative Stop State (C) = Alternative Start State A2; repeat Cycle.

For consideration of complex systems, we may also stipulate at any point of articulation in the above framework the insertion of one or more sub-cycles that define critical subsystems in the larger framework. Thus, in the reiteration of the cycle between A1 and A2, we may interpose another transition/transformation state that further modulates the system and that therefore yields greater complexity than suggested in the above logical schema.

Unlike simplicity, complexity in systems may be measured in a relatively straight-forward way by the number of components, number of interactions per unit time, or number of relationships between components. We may state a relative size of a complex system, compared to any other alternative similar system. 

We may also look at systems complexity in terms of the stratification of such systems internally, or the number of levels of articulation that they comprehend. A complex system may be said to be internally differentiated for functional purposes, and hence stratified in particular ways. The patterning of this stratification will be unique and representative of the kind of system involved.

There is a finite limit to complexity in systems. All real systems are by definition finite systems, as they must be encompassed within some meta-systemic environment. This entails that systems may be very large and very complex, but not infinitely large or infinitely complex. At the same time, a very large system is not necessarily a very complex system. There is some evidence to suggest that the space-time continuum of the universe, the largest system we can comprehend or possibly imagine in a physically real sense, may in fact upon a fundamental level be one of the simplest systems of all, compared to all the systems that are emergent from it.

Complexity and stratification of systems of all kinds can be said therefore to go hand-in-hand, and to be part and parcel of the same definition.  Inherent to the definition of complexity in systems is the principle of emergences or of emergent properties that become associated with and designative of systems. Emergent properties are holistic patterns of the system that operates as an integrated whole, beyond the capabilities and patterning of its component parts. Emergent properties are the indicators and measures of the kind and degree of integration achieved by a system at a certain level of its elaborative development. All systems in nature are interconnected and stratified within a single meta-systems framework--stratification of systems into distinctive levels of order or scale is largely a product of the emergent properties that are inherent to these systems at different levels of integration. We may speculate that upon each level or higher order of stratification and emergence of properties that are products of the system formed by emergent properties of subsystems, and so on reductio ad infinitum, a system becomes more complex by analytical definition, and more simplified in terms of its emergent identity, by an order of magnitude, compared to the next lower level of subsystems components.

I see the emergent properties that are associated with systems integration to be natures way of dealing with complexity by means of simplification of the complexity into a single integrated whole. The functional patterns of the component subsystems, or parts, of the whole system become subordinated in a coordinate matter for service to the purposes of the whole system--the result is the emergence of properties at a new level of organization of reality, and an embedding of the subsystems as a result.

One would be hard pressed to determine whether a totally randomized system represented one that is entirely simplified or complicated in the extreme. It would really depend upon one's primes and one's primary point of view. In detail, accounting for every detail of occurrence, a totally randomized system would appear almost infinitely complicated. 

In sum, as a whole albeit orderless, meaningless system, lacking any informational patterning except the pattern of randomness itself, such a system would be construed as simplified in a nearly absolute sense. On the other hand, a totally determined system (a physical impossibility, by the way) in which every relationship between every component is completely predictable and ordered, would represent a picture of a system that is maximally complicated if seen from a holistic point of view, where every component relationship must be specified as a unique definition of the whole system as an integrated system and a problem of integration, and a picture that is maximally simplified if understood from the standpoint of the specific relationships occurring between the determined components of the system--every variable is specified and every relationship encompassed by the system, made known.

Systems change. This is inherent to the definition of systems--they change in form and content, they develop along a given state path trajectory through a rather expectable sequence of stages. Systems structurally also remain resistant to change, and remain in structural ways consistent over time, unchanging in a relative sense. This change/stasis aspect of system is the heart of the paradox of understanding systems, their organization and behavior.

If systems change, we may state that in general they will change in terms of the relative overall degree of complexity, and they will tend towards either greater complication or, on the other hand, in the case of randomization, in the direction toward simplification of structure.

A stable system may be said to be one that maintains its overall structural profile over the long term, and though may fluctuate within a range varying about some complex set of means, will not tend overall towards greater simplification or complication over time. But in the larger scheme of things, all systems will tend toward greater complication first, and then towards greater simplification in the long run before they fall apart as systems at the given level of integration.

The tendency of any developing system to increase in complexity over time is apparent in all of nature. Associated with this increase in complexity are many factors of growth, development, progressive evolution, extension, elaboration, etc. In biological systems we associate all these processes under the rubric of "growth."

The tendency for any developing system, as a system to achieve some plateau or arc in their trajectory, which plateau may be represented by a relatively long lived and stable configuration, in which change towards complexity tends to be balanced by factors of change towards simplicity.

In the long run, changes towards simplification win out and come to predominate over a system, leading to the eventual demise of the system as such, and the return of the components of the system, to the environment in which they are situated.

This trajectory of all natural systems is well represented by a general logistic curve that is bell shaped--it may be platykurtic or fairly sharp and angular in reaching a critical changing point, but overall all natural systems, in their state-path trajectory, follow this three stage process of development, whether it be a tornado forming from a confluence of winds and large storm cloud formations, or it be a large population of rodents in a city or the development and life-cycle of the human brain.

The level of complication and complexity achieved by natural systems can be truly astounding. Because all natural systems are inherently underdetermined, such systems exhibit processes of simplicity & complexity at the same time. When we refer to chaos theory, the popular misconception of chaos is that of the total randomization of systems rather than the inherent order and simplification underlying otherwise complicated and highly elaborated systems that are undetermined in terms of their final output states.

Stable complexity or relative simplicity may be understood as the relative even ratio of input states over final input states--a static system overall would have a ratio of one or close to one. A system that has an overall trend towards complication would have a tendency towards a greater number of output states in relation to the number of input states in any given period. A system that has an overall trend towards simplification would have a tendency towards a greater number of input states in relation to the number of output states. If we set up the arbitrary rule of output over input, then we can see that as output shrinks and input grows, the denominator will grow larger and the numerator smaller, and the system value will tend to shrink as a decimal number to smaller and smaller fractions. As the output grows and the input shrinks, the net system will increase and grow larger in value. I would call this the value of relative complication of a system.

It can be seen that in very complex systems, sub-cycles occur or develop that may tend in one direction or the other in terms of their value of simplification/complication. Some sub-cycles can be in a trend towards complexity, counteracting the trend of other sub-cycles toward simplification. The net balance of these cycles of complexity/simplicity would thus be a measure of the overall stability/instability of the system as a whole.

It should be noted that such measures would in theory be independent of the size of the system, and hence these are scale-free measures of systems. We may further speculate that if we take a ratio of the net measure of complication over the net measure of simplification of the system, we would have an estimate of the value of stability or "K" of the system over a given period of time. We would expect, according to the principles above, that as K approximates 1, it would tend towards stable equilibrium, and as it moved higher or lower in value, it would tend towards increasing instability, either in terms of complication or growth of the system, or towards simplification and demise of the system as such.

One more set of points seem worthwhile to highlight at this point, and this concerns the definition of systems in terms of the emergent properties associated with systems and used to generally characterize and classify such systems in nature. An emergent property or set of properties may be said to be the non-linear consequence of the interaction of the components of the system acting in a state of equilibrium. Properties that are stable and attributed to systems as characteristic of such systems must be properties that arise and are maintained primarily during the intermediate period of stasis of such a system, during which process of complication/simplification tend to be balanced in equilibrium.

From the standpoint of set theory, we can characterize any system as a special kind of set, defined by emergent properties that are shared by the members of the set, and that arise from the interaction of the component variables within the set. The analogy of the set to a system is not completely appropriate I believe, because a system implies relational interactions between components that do not necessarily occur in a population of a set that may be merely a collection of otherwise unrelated entities. 

The emergent properties that are associated with a particular kind of system and that are used to typify that kind of system in a general manner, regardless of the possible state variations occurring in a larger population of similar systems, may be said to be isomorphic or a synergistic consequence of the relational patterns occurring between variables of the system, and that serve to structurally stabilize the system over the long term of its trajectory. In this case, we have a clear means of deriving what amounts to a qualitative, discontinuous statement of the general emergent property associated with and typical to a certain kind of system, with the quantitative and continuous variables that underlie and account for that property or composite set of properties.

If we consider the problem for instance of anthropogenesis, or the rise of Homo sapiens in evolutionary history, we must attribute to the condition of modern human beings a complex, composite set of attributes (bipedality, language, manual dexterity, large brain, post-partum infant dependency, etc.) that cannot be isolated or taken to independently account for the human condition or for the human capacity for cultural construction and intelligent behavior. But we analytically consider each of the set of variables, both independently and in relation to all the other variables, to account specifically for those properties emergent in Homo sapiens that are used to distinguish his species as unique, and uniquely special, compared to all other life-forms on earth. Thus we have the dilemma of evolutionary explanation of the "gradual rise" of humankind on one hand or the sudden "bio-cultural miracle" of the rapid emergence of a uniquely human species. This kind of dilemma is more a hen & egg type problem that fails to see the systemic relationships between the variables involved in the explanation.

It follows that because all real or natural systems are by fact of their occurrence complex systems, then the general trend towards theoretical solution of explanation of such systems will be towards simplification. This is known in a loose way as parsimony or Occam's razor in scientific explanation. All models represent simplified versions of the real counterpart. Models are themselves real, but in simplified and reduced form, whether they are scalable or not. Inherent measures of complexity should be in theory at least scale free, hence applicable to any size model--but there are practical working limits to model construction which determines the achievable level of complexity we may obtain in such a manner. Any model is only a rough approximation of the system it seeks to represent, and it follows logically that any "system" represented by a model, or alternatively by the structural patterning of a real system, is an abstracted theoretical model of a system that is largely simplified in ideal terms in order to be as broadly generalizable as possible. In other words, any "system" as a symbolic representation of a real pattern that is somehow structured is in essence an ideal model, simplified, of the real thing or class of things and relations that it purports to represent. The difference between ideology and scientific theory in this regard is the necessary reference of the latter system of thinking and abstract representation to criteria of measurable, objective, empirical substantiation. Scientific theory must be capable of yielding sufficient explanations for phenomena that is empirically based, and that is at least partly determinative of the outputs of systems they purport to represent. The ability to informatively predict the outputs of systems, albeit in an experimental manner or through naturalistic behavioral observation, leads to the ability to physically manipulate and modulate the behavior of such systems to increase their complexity and to further determine their actual outcomes.

Solutions to complex problems always solve the Von Neumann bottleneck of the information explosion that is created by unresolved complications of systems. They lead logically to formulaic, syllogistic simplification of information in a general sense that applies to all related systems as part of larger general set that can be described taxonomically in terms of their associated characteristic attributes and emergent behavioral properties.

Of course, this does not mean that all scientific explanation must be simple and uncomplicated and that complex scientific theories cannot exist and be as equally valid as simpler models. Descriptive explanations, particularly of surface phenomena of systems, tend towards extreme complexity, and some fields that deal more directly with complexity in systems, like ecology, tend towards this kind of theoretical elaboration and complication of explanation on a descriptive level. Such fields tend to lack paradigmatically unifying, formulaic generalizations or comprehensive theories that would serve to contextualize and frame various descriptive explanations in a consistent manner. This is not because such paradigms are not possible, but only, given the inherent complexity of the systems involved, highly unlikely and very difficult to achieve. 

Supercomputing models have helped in the challenges of descriptive explanation of such complex systems, for instance and in particular in meteorology and description of weather patterns. But even with such ability to handle large amounts of information quickly and systematically, we remain little closer to actually predicting weather in a reliable manner in the long run. Weather can be modeled on the basis of a very small number of key variables, but these variables interact in complicating ways over the structure of the long run to produce extremely complex patterns with a great deal of inherent indeterminancy and chaos associated with its patterning.

We would like herein to announce the tentative & proposed initiation of our new Nepal Program & associated Penang Projects. These will be highlighted in next week's newsletter.

Our local worlds are coming on line. We have organized initially local worlds for the British Virgin Islands, Dunedin, New Zealand, Invercargil, New Zealand, & Stewart Island, New Zealand. Link details will follow in next weeks newsletter.

Jobberhost.com is almost ready to fly. 

We have added additional server space to our accounts, though internal setbacks have restricted the rate at which we can expand our server resources.

Our Consolidation efforts continue on different levels. We finished the consolidation phase a month ahead  though there are many loose ends remaining to be taken care of. We have announced out promotional development campaign for the next two months which should include a coordinated and targeted marketing & advertising campaign, though we will avoid e-mail or telephone solicitations or marketing strategies.

During this phase, we hope to announce the opening of our new store-fronts, and further development of our entire framework. We are publishing our own business leverage (business cards, letter-heads, post-cards, etc) and will be setting up new contact details.

Products/Services

http://www.lewismicropublishing.com/Newsletter/Membership/

Lewis Works strives to offer a genuinely comprehensive range of services and products for the global e-consumer in an informed, non-aggressive manner. It has taken us time to develop our resources into an integrated framework that will provide largely automated self-service to our members and other customers. But persistence & a great deal of patience is finally beginning to pay-off in terms of the emergence of a real web-system with an active presence on the Internet.

We act both as a reseller for other providers, and we also are increasing the product range that we actually own or buy ourselves wholesale and then resell. We also provide a range of peripheral options through associate/affiliate accounts.

We will soon be adding a comprehensive product service catalog link here.

We will be offering an increasing array of type of service and product we can make available to our clientele within the consolidation period. This services will include:

• Systems-based Consulting & Troubleshooting
• Systems-based Computing and Web-Design Development
• Systems-based Meta-scientific research & development services
• Systems-based Digital Publication and Production Services
• Systems-based Development Services in a range of areas, including Non-profit, Consolidated Business Services, Education & Human Development, Organization, Production & Engineering

What areas are currently Non-Profit in Lewis Works?

We have several non-profit domains organized, though these have not yet been developed for content:

Human Coop: promoting development of non-exploitative, grass-roots based, cooperative development & resource exchange network frameworks.

Aid Systems: organizing and deploying critical resource management & rehabilitation teams

Human Development Systems: promoting programs for alternative human development.

Lewis Library: promoting conventional & electronic literacy worldwide, developing an open, distributed-integrated common reference resource & comprehensive knowledge compendium resources.

Human Synergetics: promoting health in holistic, alternative lifestyles

We also recommend our current Link Palette for related links & portals, though most of these are as yet unfinished.

For external topic-organized links, we recommend Hugh's Hot Links

For popular, top-search links, we recommend Haut Lynx

Query us for advertising on our Advertising Pages that are shown throughout our web-system on more than a eleven hundred distinct URLs.

Subscribe to our Newsletter below: