Tags & filtering
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Tags and filtering

Summary
This page discusses the tagging of
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. 
signals
in a complex adaptive system (
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
).  Tagged signals can be used to control filtering of an event stream.  Examples of CAS filters are reviewed. 
Introduction
A typical mechanism creating order within a
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)
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. 
flow
is to tag signals.  A tag is a structure that is recognizable to 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


Filters can operate on the tags.  The
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent
nature of CAS agents is supported by using selection.  Whereas pre-defined instructions about the nature of the environment are incompatible with CAS.  Filtering can allow a similar effect to emerge from the
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. 
schematic differentiation
of agents. 

Filters respond selectively to particular events typically differentially blocking certain structures from a flow. 

A filter must be positioned in such a way that it interacts with a flow, or pool of tagged structures.  Many filters are active infrastructure agents. 
CAS agents can leverage semi-permeable
Barriers are particular types of constraints on flows.  They can enforce separation of a network of agents allowing evolution to build diversity.  Examples of different types of barriers and their effects are described. 
barriers
to gain access to flows driven by physical potentials between the separated areas.  Still the richest source of tagged structures comes from the agent generated constructs which have been evolved to offer easily tagged surfaces, and often to respond to the tags by changes in conformation and other properties.  For example the adaptive web framework (AWF)'s Perl test infrastructure supports tag deployment and detection

Histones, in the eukaryotic cell are enzymes which bind to the DNA polymers supporting them and controlling their interactions with other enzymes.  In particular sets of DNA operons can be enabled or disabled by histone induced changes in the DNA polymers shape.  In AWF the histone control of DNA has been abstracted in a codelet based implementation of operon controlled programmed case control. 
can filter the
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 plan
's tags to limit which areas of the plan are currently accessible to the agents.  Different areas can be available at different phases of the system's development is a phase during the operation of a CAS agent.  It allows for schematic strategies to be iteratively blended with environmental signals to solve the logistical issues of migrating newly built and transformed sub-agents.  That is needed to achieve the adult configuration of the agent and optimize it for the proximate environment.  Smiley includes examples of the developmental phase agents required in an emergent CAS.  In situations where parents invest in the growth and memetic learning of their offspring the schematic grab bag can support optimizations to develop models, structures and actions to construct an adept adult.  In humans, adolescence leverages neural plasticity, elder sibling advice and adult coaching to help prepare the deploying neuronal network and body to successfully compete. 
.  AWF includes a
This page describes the 'merge streams' application's codelet implementation of a 'case' architecture based on the adaptive web framework's (AWF) Smiley histone infrastructure. 
The application scenario for processing case statements is described. 
It involves a schematic binder complex for resolving the case statements. 
A case tagged application schemata. 
The Smiley infrastructure that supports the case architecture is reviewed. 
The Workspace schematic strings that implement the operon supporting histone like case control are included. 
The Slipnet concept network for the 'merge streams' application's histone like case control is included. 
The codelets and supporting functions are included. 
histone analogous filter


Neuronal, specialized eukaryotic cells include channels which control flows of sodium and potassium ions across the massively extended cell membrane supporting an electro-chemical wave which is then converted into an outgoing chemical signal transmission from synapses which target nearby neuron or muscle cell receptors.  Neurons are supported by glial cells.  Neurons include a:
  • Receptive element - dendrites
  • Transmitting element - axon and synaptic terminals 
and immunological differentiation reflects selection from a schematically encoded Darwinian
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
evolved
pool of agents, that once deployed competes to exist in the local environment. 
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
responding to
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. 
general phenomena
reinforce the benefits of the particular cell complexes.  Agents that deploy but don't operate in the local configuration are recycled. 

This page discusses the interdependence of perception and representation in a complex adaptive system (CAS).  Hofstadter and Mitchell's research with Copycat is reviewed. 
Perception and representation
architectures, such as the brain, represent large numbers of structures in parallel, but only in the unconscious regions.  The conscious region of the brain, the prefrontal cortex, is easily overwhelmed by too many different inputs.  It integrated these in ways that will distort logical reasoning if the inputs are really unrelated.  Consequently conscious inputs should be carefully pre-filtered. 

CAS agents are limited to responding to events that they can detect.  Tags allow evolutionary selection to make the system a much richer, and more logical, place. 
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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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  • 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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