A special set of rules
This page describes the organizational forces that limit change.  It explains how to overcome them when necessary. 

Power& tradition holding back progress
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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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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Physics - a small set of rules that drives emergence

Summary
This page discusses the physical foundations of complex adaptive systems (
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. 
CAS
).  A small set of rules is obeyed.  New [epi]
This page describes the Smiley infrastructure and codelets that instantiate the epiphenomena defined in the Meta file and Slipnet. 
Infrastructure sensors are introduced. 
The role of phenomena in shaping the environment is discussed. 
The focusing of forces by phenomena in Smiley is discussed. 
The Meta file association of case keywords with phenomena is included. 
The codelets and supporting functions are included. 
phenomena
then
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emerge
.  Examples are discussed. 
Introduction
At the base of any
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 system
(CAS) is a set of rules that defines the elemental forces that are present. 

The fundamental forces that these rules express reflect various phenomena, and epiphenomena, with which
Agents use sensors to detect events in their environment.  This page reviews how these events become signals associated with beneficial responses in a complex adaptive system (CAS).  CAS signals emerge from the Darwinian information model.  Signals can indicate decision summaries and level of uncertainty. 
sensors
interact such as:
Additional rules utilize these effects in combinations that express and respond to the [epi]phenomena. 

Only if the rules allow for a
This page discusses the potential of the vast state space which supports the emergence of complex adaptive systems (CAS).  Kauffman describes the mechanism by which the system expands across the space. 
very large number of different states of the system
to exist will a system have the opportunity to be complex and adaptive.

Constraint based emergent phenomena
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. 
Constraints on flows
can induce effects which result in the
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergence
of additional phenomena. 

Epiphenomena reflect the effects of the operation of underlying core phenomena, within a specific operating range, but they
Russ Abbott explores the impact on science of epiphenomena and the emergence of agents. 
are usefully defined in terms that do not depend on any underlying mechanism instantiated
with the core phenomena. 

Epiphenomena, like phenomena, operate based on rules and these can be
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
modeled
, described in theories, and the implications tested experimentally to check the alignment of the theory and practice.  However, the models will have arbitrary bases, so an alignment failure may indicate a misrepresentation of the operation of the phenomena, or missing aspects of the real phenomena in the model.  Missing aspects limit the creative predictions of models. 

Physical [epi]phenomena such as differential absorption and reflection of light energy are only indirectly associated with the form of the object hit by the light rays. 
Read Montague explores how brains make decisions.  In particular he explains how:
  • Evolution can create indirect abstract models, such as the dopamine system, that allow
  • Life changing real-time decisions to be made, and how
  • Schematic structures provide encodings of computable control structures which operate through and on incomputable, schematically encoded, physically active structures and operationally associated production functions. 
Receptors
can be structured to use the effect to associate general patterns with important situations. 

Rather than oppose the direct thrust of some environmental flow agents can improve their effectiveness with indirect responses.  This page explains how agents are architected to do this and discusses some examples of how it can be done. 
Indirect association
of specific rule sets and
Agents use sensors to detect events in their environment.  This page reviews how these events become signals associated with beneficial responses in a complex adaptive system (CAS).  CAS signals emerge from the Darwinian information model.  Signals can indicate decision summaries and level of uncertainty. 
events
enables the 'construction', and
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
evolution
, of a CAS
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.
  The agent's behavior is determined by a aggregation is when a number of actions become coordinated and operate together.  In the adaptive web framework's Smiley, codelets become coordinated by their relative position in the deployment cascade.  The cascade's dynamics are dependent on the situation, the operating codelets responses to that situation and the grouping of schematic strings they are associated with.  The aggregate affect is a phenotype the adaptive agent. 
of rules which specify how the agent responds to stimuli. 

An example of a programmed CAS system, the
This page describes the Adaptive Web framework (AWF) test system and the agent programming framework (Smiley) that supports its operation. 
Example test system statements are included.  To begin a test a test statement is loaded into Smiley while Smiley executes on the Perl interpreter. 
Part of Smiley's Perl code focused on setting up the infrastructure is included bellow. 
The setup includes:
  • Loading the 'Meta file' specification,
  • Initializing the Slipnet, and Workspaces and loading them
  • So that the Coderack can be called. 
The Coderack, which is the focus of a separate page of the Perl frame then schedules and runs the codelets that are invoked by the test statement structures. 
Smiley test system
, includes a physical environment (initial situation defined in the
This page describes the Copycat Slipnet. 
The goal of the Slipnet is reviewed. 
Smiley's specialized use of the Slipnet is introduced. 
The initial Slipnet network used by the 'Merge Streams' and 'Virtual Robot' agent-based applications is setup in initchemistry and is included. 
The Slipnet infrastructure and initialization functions are included. 
Slipnet
and rule specification) and leverages
Agents use sensors to detect events in their environment.  This page reviews how these events become signals associated with beneficial responses in a complex adaptive system (CAS).  CAS signals emerge from the Darwinian information model.  Signals can indicate decision summaries and level of uncertainty. 
sensors
to detect its underlying
This page describes the Smiley infrastructure and codelets that instantiate the epiphenomena defined in the Meta file and Slipnet. 
Infrastructure sensors are introduced. 
The role of phenomena in shaping the environment is discussed. 
The focusing of forces by phenomena in Smiley is discussed. 
The Meta file association of case keywords with phenomena is included. 
The codelets and supporting functions are included. 
phenomena


The myriad states that can be created from the rules is fundamental to a system being
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent





























































































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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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