Evolution
This page describes the organizational forces that limit change.  It explains how to overcome them when necessary. 

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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This page uses the example of HP's printer organization freeing itself from its organizational constraints to sell a printer targeted at the IBM pc user. 
The constraints are described. 
The techniques to overcome them are implied. 
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Evolutionary action

Summary
This page reviews the implications of selection, variation and heredity in a complex adaptive system (
This page introduces the complex adaptive system (CAS) theory frame.  The theory provides an organizing framework that is used by 'life.'  It can be used to evaluate and rank models that claim to describe our perceived reality.  It catalogs the laws and strategies which underpin the operation of systems that are based on the interaction of emergent agents.  It highlights the constraints that shape CAS and so predicts their form.  A proposal that does not conform is wrong. 

John Holland's framework for representing complexity is outlined.  Links to other key aspects of CAS theory discussed at the site are presented. 
CAS
).  The mechanism and its
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Physical forces and constraints follow the rules of complexity.  They generate phenomena and support the indirect emergence of epiphenomena.  Flows of epiphenomena interact in events which support the emergence of equilibrium and autonomous entities.  Autonomous entities enable evolution to operate broadening the adjacent possible.  Key research is reviewed. 
emergence
are discussed. 
Introduction
Adaptive in evolutionary biology is a trait that increased the number of surviving offspring in an organism's ancestral lineage.  Holland argues: complex adaptive systems (CAS) adapt due to the influence of schematic strings on agents.  Evolution indicates fitness when an organism survives and reproduces.  For his genetic algorithm, Holland separated the adaptive process into credit assignment and rule discovery.  He assigned a strength to each of the rules (alternate hypothesis) used by his artificial agents, by credit assignment - each accepted message being paid for by the recipient, increasing the sender agent's rule's strength (implicit modeling) and reducing the recipient's.  When an agent achieved an explicit goal they obtained a final reward.  Rule discovery used the genetic algorithm to select strong rule schemas from a pair of agents to be included in the next generation, with crossing over and mutation applied, and the resulting schematic strategies used to replace weaker schemas.  The crossing over genetic operator is unlikely to break up a short schematic sequence that provides a building block retained because of its 'fitness';  In Deacon's conception of evolution, an adaptation is the realization of a set of constraints on candidate mechanisms, and so long as these constraints are maintained, other features are arbitrary. 
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
initially
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Physical forces and constraints follow the rules of complexity.  They generate phenomena and support the indirect emergence of epiphenomena.  Flows of epiphenomena interact in events which support the emergence of equilibrium and autonomous entities.  Autonomous entities enable evolution to operate broadening the adjacent possible.  Key research is reviewed. 
emerge
due to the strategic advantages obtained from the autocatalytic construction of their initial
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 structures
.  These structures induce directly or indirectly advantages in the collection and utilization of natural resources from the environment.  The competitive advantage initiates evolutionary action. 

Evolution is the interaction of: selection, variation and heredity.  Complex adaptive systems evolve due to the pressure of competitive selection on each agent.  The structure of the schematic plan provides:
The emergence of evolutionary action is necessary for
This page introduces the complex adaptive system (CAS) theory frame.  The theory provides an organizing framework that is used by 'life.'  It can be used to evaluate and rank models that claim to describe our perceived reality.  It catalogs the laws and strategies which underpin the operation of systems that are based on the interaction of emergent agents.  It highlights the constraints that shape CAS and so predicts their form.  A proposal that does not conform is wrong. 

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 systems
(CAS) to exist.  It is likely to involve properties seen in CAS components: dynamic
Flows of different kinds are essential to the operation of complex adaptive systems (CAS). 
Example flows are outlined.  Constraints on flows support the emergence of the systems.  Examples of constraints are discussed. 
flows and control
, auto-catalysis, self-assembly, generation and preservation of constraints, energy leverage and storage, information association and competition.  Terrence Deacon outlines a scenario

The schematic specification and control of operational cascades provides evolution with its point of association.  Schematically induced
Desmond & Moore paint a picture of Charles Darwin's life, expanded from his own highlights:
  • His naughty childhood, 
  • Wasted schooldays,
  • Apprenticeship with Grant,
  • His extramural activities at Cambridge, walks with Henslow, life with FitzRoy on the Beagle,
  • His growing love for science,
  • London: geology, journal and Lyell. 
  • Moving from Gower Street to Down and writing Origin and other books. 
  • He reviewed his position on religion: the long dispute with Emma, his slow collapse of belief - damnation for unbelievers like his father and brother, inward conviction being evolved and unreliable, regretting he had ignored his father's advice; while describing Emma's side of the argument.  He felt happy with his decision to dedicate his life to science.  He closed by asserting after Self & Cross-fertilization his strength will be exhausted.  
Following our summary of their main points, RSS frames the details from the perspective of complex adaptive system (CAS) theory.  Darwin placed evolution within a CAS framework, and built a network of supporters whose complementary skills helped drive the innovation. 
 
Darwinian
pre-adaptations, initially termed pre-adaptation refers to the coopting of some function for a new use.   can recur generation after generation.  If these pre-adaptations become selective advantages in particular niches an association between the niche and the phenotype is the system that results from the controlled expression of the genes.  It is typically represented by a prokaryotic 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.   is formed and a schematic amplifier can emerge. 

Evolution drives forward the development of CAS. 
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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
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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. 
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