Advanced Systems Sciences

Advanced Systems sciences faces a common set of structural challenges. These can be seen as the challenges of complexity, contextuality, comprehensiveness, causality and chaos:

1. Complexity: The challenge of complexity in systems approaches is the difficulty of resolving the "information explosion" when analysis is carried progressively and gradationally to lower and lower levels, or to encompass broader and broader systems.

2. Contextuality: The challenge of contextuality is to recognize that all systems are embedded at whatever level within other larger systems, and in turn encompass other sub-systems. At each point in each system, there are a larger set or framework of relationships that structure and give systemic significance to that point. In the problem of contextuality, we must ask, how much context is sufficient.

3. Comprehensivity: The challenge of comprehensiveness resides in the issue that all systems are interconnected with every other system. We cannot conceive of a perfectly isolated system. Systems are also embedded at multiple levels within other systems, giving rise to the problem of multi-plexity and the interpenetration or multiple integration of different systems. Our approaches on some level must embrace and resolve the challenge of comprehensiveness.

4. Causality: Causality is inherently a challenge to systems approaches because the classical conception of causality implies, among other things, linear relationships between variables, and logical relationships between variables and unidirectional relationships between variables. Such relationships are rarely found in systems analysis. Causality becomes multiply deterministic and under-deterministic in many systems.

5. Chaos: The problem of chaos stems from the issue of inherent randomness of under-determined relations, and from variation of patterning found within systems, and from indirect causal chains that impact upon systems at many points.

We can resolve many of these basic challenges in several related ways:

The assumption of total systems: All systems are a part of a total system, which can be comprehended scientifically as such, and its subsystems thus contextualized within its framework.

The assumption of feedback cycles: Sub-systems gain identity in the larger system by means of feedback mechanisms that are inherent to the functional integration of the system as somehow separate.

The assumption of solutions: We can develop what constitute simplifying solutions to complex systems, by identifying the main or key variables involved in the feedback relationships established within any system.

The assumptions of models: We can develop on the basis of our simplifying solutions alternative models of the system that allow us to manipulate the variables of the system in a controlled manner.

The assumptions of dynamics: All systems exist in the long run in some form of  stability or instability. External relations and internal variations that affect the system result in state changes within the system that can lead to its structural modification, its disintegration or its development.

What I propose is a meta-systemic science of systems, that in itself comes to comprise a system of all systems, that is based on the study of all systems in a systematic and controlled fashion, and that leads to the experimental application of artificial systems, as models, to a variety of specific problem sets.

The key design characteristics of all systems seems to me to be the following:

1. All systems are working systems.

a. They are imperfect systems.

b. They can be measured by their relative efficiencies.

2. All systems therefore have some form of functional integration.

a. All systems can be mechanically described in terms of their functional interrelationships.

1. The functional relationships between parts conditions the descriptive analysis of the parts.

b. All systems exhibit some measure of super-systemic synergism.

1. This synergism is conditioned by and is part of some larger system or set of systems.

c. Each part forms its own synergistic unity that is conditioned by and subsumes some other set of systems.

d. All systems defines some complex form of functional equilibrium or stability of integration.

e. This equilibrium as a system is always fluctuating.

f. This pattern of fluctuation in the long term leads to state-transition changes that affects the parts and the system as a whole.

g. The patterns of fluctuation of any system obey some theoretical set of limits that define the range of normal function, within which equilibrium can be maintained, and beyond which, equilibrium must yield to entropy.

3. All systems communicate information at multiple levels.

a. Information is either boundary-maintenance, endogenous or exogenous.

b. Information can be defined as the functional parameters of relationship between the parts of a system.

c. We can distinguish a continuum between deterministic information and noise.

d. Systemic information is always describable in two forms:

1. mathematically

2. symbolically

4. All systems can be understood and described in terms of their information, in a manner that is scientifically useful such that:

a. We can build working models of systems.

b. We can use the models to test propositions regarding the nature of the system.

c. This testing is experimentally controllable.

d. We can apply the models to the construction of new systems.

Natural systems theory is organized upon three strata of natural information functions. At all three levels there appears to be a core of related systems theoretical principles that are applied. The same core appears to me to underlie and account for almost all levels of programming in Artificial Intelligence research. I have come to the conclusion that in all working systems, there is a core set of theoretical principles that operate in the determination of such systems. As working systems, these are systems that are amenable to mathematically based analysis and modeling whatever the level of our understanding. It entails that we may reduce our units of analysis to discrete or continuous variables that enter into structural relationships. This can be applied with equal effect to human systems as to physical systems. I believe this describes a systems paradigm. My hypothesis is to delineate and explicate this core working system, that underlies all systems, and then to functionally extend it to alternative systems development.

Computer Programming

alternative programming systems design

theoretical interpretation of primes & variables

mathematically based functions

automated data-base construction

simulations & modeling

list processing structures

string producing & parsing structures

pattern recognition structures

pattern processing structures

reference-inference structures

auto-motivated response structures

1. Physical Systems Theory:

a. Mathematical model of universal relativity

b. Modeling of the dynamic structure of the universe

c. Modeling of possible sub-quantum mechanics & systematics

2. Biological Systems Theory:

a. Modeling of models of Natural Selection pathways & systems

b. Modeling of speciational pathways

c. Modeling of ecosystems

d. Modeling of evolutionary systems

e. Global modeling

3. Human Systems Theory:

a. Modeling of Human evolution

b. Modeling of Cultural systems

c. Modeling of Human languages

d. Modeling of Human symbolization

e. Modeling of Human cognition

f. Modeling of Human ideological system

g. Modeling of Human social systems

Within this primary framework, I propose to build a secondary framework that has the function of reinforcing the entire system on a foundational level. I propose as well to develop the following facilities and systems:

Computational Systems

Philosophical Systems

Philological Systems

Strategic Systems

All of these should cohere and integrate into a single working system. Its primary set of applications will be in experimental testing conditions that are artificially controlled:

Artificial Systems Design Engineering

Physical Systems Engineering Design

Hydrogen Systems Engineering

Gravitational Engineering

Biological Systems Engineering

Artificial Evolutionary Experimentation

Environmental Systems Engineering

Human Systems Engineering

Human Development Systems

I propose as well that projects and programs can be designed around these various systems of development. The extension of this program will be accomplished by means of its systematic extension through a structured exchange program . The academic/non-academic exchange program should provide the foundation for the systematic extension of the system in an international framework.