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Power& tradition holding back progress
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  • A business can gain focus from targeting key customers,
  • Business planning activities performed by the whole organization can build awareness, empowerment and coherence. 
  • A program approach can ensure strategic alignment. 
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The constraints are described. 
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Tool enhanced systems

Summary
Tools and the businesses that produce them have evolved dramatically.  W Brian Arthur shows how this occurred.
The Nature of Technology
In W. Brian Arthur's book 'The Nature of Technology' he highlights the fundamental mechanisms which have driven the build out of the technologies over time.  He demonstrates the expanding effect of technology on the economic system and its basis in
This page introduces the complex adaptive system (CAS) theory frame.  The theory is positioned relative to the natural sciences.  It catalogs the laws and strategies which underpin the operation of systems that are based on the interaction of emergent agents. 
John Holland's framework for representing complexity is outlined.  Links to other key aspects of CAS theory discussed at the site are presented. 
complex adaptive
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergence
.  The adaptive responses of agents in the economy to technological change are shown to imply cascades of transformation located in
This page discusses the benefits of geographic clusters of agents and resources at the center of a complex adaptive system (CAS). 
networked clusters
modeled with power law statistics.  The nature of technological change undermines the consensus of equilibrium economics. 

A father of complexity, M. Mitchell Waldrop describes a vision of complexity via:
  • Rich interactions that allow a system to undergo spontaneous self-organization
  • Systems that are adaptive
  • More predictability than chaotic systems by bringing order and chaos into
  • Balance at the edge of chaos 
science, Arthur develops a theory of technology so as to frame what technology is and how it works in the world. 

He explains how technology emerges in leveraging
This page discusses the physical foundations of complex adaptive systems (CAS).  A small set of rules is obeyed.  New [epi]phenomena then emerge.  Examples are discussed. 
phenomena
to solve human needs.  An
Plans are interpreted and implemented by agents.  This page discusses the properties of agents in a complex adaptive system (CAS). 
It then presents examples of agents in different CAS.  The examples include a computer program where modeling and actions are performed by software agents.  These software agents are aggregates. 
The participation of agents in flows is introduced and some implications of this are outlined. 
agent's
goal (or purpose) becomes associated with an action which is enhanced by the technology.  The specific phenomena a technology expresses typically defines its compatibility.  As such components of toolboxes of technologies (domains) can be combined together to create desired effects.  Engineers, having developed
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
understandings
of the underlying phenomena and the attributes of the components of the toolboxes enable the combinations. 

Arthur explains that each new technology combination is dependent on a network of supporting technology components.  Each component is similarly dependent creating a recursive network of emergent technologies.  Since the components must be phenomenologically compatible as new networks deploy out replaced components and their supporting networks collapse.  Further each technological component will typically introduce additional problems which are overcome by adding more component networks.  Arthur makes it clear that technological change will be fractal like. 

The implications for economics are significant.  Rather than adopting Newtonian foundations: order, closedness, equilibrium; the foundations become open endedness, indeterminacy and emergence of perpetual novelty.  The economy becomes involved with creating new combinations, new configurable offerings.  Startup managers must aim to frame an undefined situation so it can be dealt with.  They must aim to transform a stock of deep expertise into new strategic combinations. 
Combinatorial evolution
Arthur adds that the evolution of technology is analogous to but different from
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
Darwinian evolution
.  His combinatorial evolution is driven by problems resolved by goals enacted recursively by combinations of compatible technologies.  The difference, he highlights, is in the ability to directly combine technologies, whereas Darwinian evolution 'cannot select pieces from different systems and combine these at one go.  It is hemmed in by the strictures of genetic evolution: a new combination must be created by incremental steps; these steps must produce something viable--some living creature--at all stages; and new structures must be elaborated in steps out of components that exist already.  '

However, it does not seem to us that the difference is profound and may depend on the system
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
model
adopted to understand the
Plans are interpreted and implemented by agents.  This page discusses the properties of agents in a complex adaptive system (CAS). 
It then presents examples of agents in different CAS.  The examples include a computer program where modeling and actions are performed by software agents.  These software agents are aggregates. 
The participation of agents in flows is introduced and some implications of this are outlined. 
agents
(in this case engineers)
Plans emerge in complex adaptive systems (CAS) to provide the instructions that agents use to perform actions.  The component architecture and structure of the plans is reviewed. 
schematic plans
which will define their actions.  It appears, to us, that technologies do not evolve so directly.  Instead of corresponding to the genetic plan the combinations of technologies seem more analogous to the translated outputs is the process where messenger m-RNA is cross coded by Ribosomal agents and t-RNA into an amino-acid polymer.   of a transcription process is the process where DNA is converted into messenger m-RNA.  A complex of enzymes cooperates to bind to the DNA and generate the m-RNA copy.  There are a number of such transcription complexes which are based on RNA polymerase I, II or III. 
- i.e. in cell biology the technology complexes would be represented by collections of amino-acids are the building blocks of proteins.  The 20 main variants differ by the nature of their side chain.  Some are positively charged, others negatively charged.  Some are water seeking while others are fat seeking.  The genetic code mapping of DNA base pair triplets thus specifies the primary sequence of amino-acids in any protein polymer. 
that combined together create oligomeric protein, a relatively long chain (polymer) of peptides.  Shorter chains of peptides are termed polypeptides.   complexes rather than corresponding to the genetic material (DNA (DNA), a polymer composed of a chain of deoxy ribose sugars with purine or pyrimidine side chains.  DNA naturally forms into helical pairs with the side chains stacked in the center of the helix.  It is a natural form of schematic string.  The purines and pyrimidines couple so that AT and GC pairs make up the stackable items.  A code of triplets of base pairs (enabling 64 separate items to be named) has evolved which now redundantly represents each of the 20 amino-acids that are deployed into proteins, along with triplets representing the termination sequence.  Chemical modifications and histone binding (chromatin) allow cells to represent state directly on the DNA schema.  To cope with inconsistencies in the cell wide state second messenger and evolved amplification strategies are used. 
). 

We see the genetic plans as ideas in the minds of men, rather than the technologies themselves.  The schematic plans, will have goals (Arthur's purpose),
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
models
and valuations of the alternative associated actions. 

If the system is viewed as including the educational, scientific, administrative and engineering infrastructure as well as engineering businesses then the germ-line, a master copy of the schematic structures is maintained for reproduction of offspring.  There will also be somatic copies which are modified by the operational agents so that they can represent their current state.   ideas will reside within the minds of educators and academics. 

These ideas may have been initially formed by scientists and engineers tinkering with prior technologies, but they must then be accepted and structured into courses at educational institutions attended by trainee engineers.  In effect the ideas have been evaluated, and selected by
Plans change in complex adaptive systems (CAS) due to the action of genetic operations such as mutation, splitting and recombination.  The nature of the operations is described. 
genetic operators
.  Sometimes the ideas have to struggle to enter the active schematic structure when they compete to replace alternative alleles, one of multiple alternative forms of a schematic sequence with the same address on a schematic string. 
.  For example the idea of evolution itself can be seen currently competing with alternative religious alleles in state school curriculum with the evaluation being a political rather than technical judgment. 

If a technology focused idea is accepted into the teaching curriculum it can be learned by the trainee engineer (replicated).  The qualified engineer when subsequently thinking about a problem may include the approach, in combination with others (phenotypic is the system that results from the controlled expression of the genes.  It is typically represented by a bacterial cell or the body of a multi-cell animal or plant.  The point is that the genes provide the control surface and the abstract recipe that has been used to generate the cell. 
differentiation) in his design proposal (transcription is the process where DNA is converted into messenger m-RNA.  A complex of enzymes cooperates to bind to the DNA and generate the m-RNA copy.  There are a number of such transcription complexes which are based on RNA polymerase I, II or III. 
).  If the idea is found to work when enacted with technology components (translation is the process where messenger m-RNA is cross coded by Ribosomal agents and t-RNA into an amino-acid polymer.  ) a new valuable combination emerges (translated technology complex).  This process seems quite Darwinian.  Indeed we can see the bottleneck as the resistance to accept conflicting ideas into a curriculum, prior to the teaching of the curriculum to each trainee engineer along with the requirement for engineers to possess credentials and fit in with their organization's norms. 

Although it can be countered that an engineer can throughout his or her career repeatedly augment their toolbox of ideas and technologies while the biological phenotype is the system that results from the controlled expression of the genes.  It is typically represented by a bacterial cell or the body of a multi-cell animal or plant.  The point is that the genes provide the control surface and the abstract recipe that has been used to generate the cell. 
's germ-line DNA is fixed, neither of these statements is straight forward.  R-factors and plasmids provide bacteria with a way to transfer parts of their DNA complement with one another.  The effect is to ensure that useful mutations can become rapidly distributed within a population of bacteria.   allow bacteria, a single cell system exemplified by the bacteria.  Prokaryotes have their own DNA and infrastructure within a single enclosure.   to enhance their genetic material.  It is actually quite challenging for engineers to deploy radical ideas into their solutions.  Arthur comments that inventions often benefited from special situations:
We would suspect the inventors were removed from the constraints of
This page reviews the inhibiting effect of the value delivery system on the expression of new phenotypic effects within an agent. 
extended phenotypic alignment


Never the less, as Richard Rhodes comments 'The Nature of Technology is a seminal work, thrilling to read and rich in implications for business as well as engineering and the social sciences'.  Indeed Arthur's foundation of emergence based on domains of re-combinable technologies can be seen to be reflected in the schematic architecture which allows
To benefit from shifts in the environment agents must be flexible.  Being sensitive to environmental signals agents who adjust strategic priorities can constrain their competitors. 
flexible
Plans are interpreted and implemented by agents.  This page discusses the properties of agents in a complex adaptive system (CAS). 
It then presents examples of agents in different CAS.  The examples include a computer program where modeling and actions are performed by software agents.  These software agents are aggregates. 
The participation of agents in flows is introduced and some implications of this are outlined. 
agents
to
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emerge
from codelet associations in the Perl testing framework.  


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integrating quality appropriate for each market
 
This page looks at schematic structures and their uses.  It discusses a number of examples:
  • Schematic ideas are recombined in creativity. 
  • Similarly designers take ideas and rules about materials and components and combine them. 
  • Schematic Recipes help to standardize operations. 
  • Modular components are combined into strategies for use in business plans and business models. 

As a working example it presents part of the contents and schematic details from the Adaptive Web Framework (AWF)'s operational plan. 

Finally it includes a section presenting our formal representation of schematic goals. 
Each goal has a series of associated complex adaptive system (CAS) strategy strings. 
These goals plus strings are detailed for various chess and business examples. 
Strategy
| Design |
This page uses an example to illustrate how:
  • A business can gain focus from targeting key customers,
  • Business planning activities performed by the whole organization can build awareness, empowerment and coherence. 
  • A program approach can ensure strategic alignment. 
Program Management
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