CAS action agents
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. 
Be responsive to market dynamics
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. 
Overcome reactionaries
Primary Navigation

Creating action in a complex adaptive system

Summary
Plans are interpreted and implemented by agents.  This page discusses the properties of agents 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
). 
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. 
Introduction
In 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)
system goals are
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. 
indirectly associated
with actions.  The actions can be both operational and
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
modeling
activities.  They are performed by agents in response to
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
, using a
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
to define the
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. 
associations
.  The agents are
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent
dynamic flow based [epi]
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
, autonomous entities utilizing
Terrence Deacon explores how constraints on dynamic flows can induce emergent phenomena which can do real work.  He shows how these phenomena are sustained.  The mechanism enables the development of Darwinian competition. 
constraints generally
and 
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. 
control of an energy flow
in particular to act and manage its state.  The indirect nature of the association means that agents can be extended by schematic aggregation

In biochemical systems each cell becomes a supporting infrastructure, for its agents, through 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. 
DNA schemata
and corresponding cascaded proteins, a relatively long chain (polymer) of peptides.  Shorter chains of peptides are termed polypeptides.   that catalyze, an infrastructure amplifier.   reactions with their
This page reviews the catalytic impact of infrastructure on the expression of phenotypic effects by an agent.  The infrastructure reduces the cost the agent must pay to perform the selected action.  The catalysis is enhanced by positive returns. 
active sites
.  Cells also
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emerge
as agents at a higher level, as a result of
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. 
natural selection
s influence on their architectures.  Other CAS have analogous hierarchies of specialized agents with tailored reactive structures. 

Just as enzymes, a protein with a structure which allows it to operate as a chemical catalyst and a control switch. 
are linked into chains of reactions, with rate controls maintained at entry and exit points of the flows, other CAS provide a
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. 
rate control mechanism


Businesses are specialized cellular agents of national economies, where the transformations and flows that make up each business operation are performed by lower level hierarchy of human agents and supporting tools.  The flow rates are controlled by management processes.  Particular skills and actions can be introduced into a team of human agents by aggregation.  Such recombinations should be under schematic control. 

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. 
adaptive web framework (AWF) test infrastructure
, a computer program architected as a CAS, has flows that are sustained by agents, such as the
This page discusses how Smiley can support the start of the development phase of an agent-based application. 
Startup is an artificial operation not found in living systems.  But Smiley must do it and so we discuss an example of starting the development phase. 
With the Smiley infrastructure and the application integrated the application's development phase is reviewed.
The association of structural Workspaces for state representation is discussed. 
The aggregation of schematic associations of codelets defines a development agent.  At the application level it processes the application's schematic strings. 
The schematic nature of the data processed by the test application suggests the use of an indirect integration framework.  This supports the binding of codelets to the schematic data and detecting and responding to the control operons. 
An application polymerase complex emerges. 
The codelets and supporting functions are included. 
development agent
.  The agents are composed of
This page discusses how Smiley provides deployment guarantees to its agent-based applications. 
Smiley's transaction services are reviewed. 
The complex interactions of codelets participating in a deployment cascade are discussed including: 
  • The implementation of schematic switches. 
  • The cooperative use of goal suppression.  
  • Evaluator codelets promotion of other siblings. 
Challenges of initiation of a cascade are discussed. 
Tools to associate transaction protection to an operon deployed codelet are described. 
Special support for sub-program codelets is described.  Completion of transactional sub-programs presents special challenges. 
Priority and synchronization support includes:
  • Delaying the operaton of the cascade sponsor. 
  • Delaying the notgcompleting cascade participant. 
  • Waiting for completion of parallel operations with the wait and relay service.  
The need to sustain resource pools is reviewed. 
The use of signals to coordinate siblings is described. 
The structural binding operon for the wait and relay service is included. 
The codelets and supporting functions are included.
cascades
of codelets associated via schematic structures.  Once again the flow rates are managed. 

The rate control systems must ensure maintenance of the ubiquitous resources that sustain the CAS. 

These mechanisms are highly correlated with the fitness of the CAS.  They contain highly conserved structures but respond to
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
evolutionary selection
, by modular recombination, tailored 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. 
and differential translation is the process where messenger m-RNA is cross coded by Ribosomal agents and t-RNA into an amino-acid polymer.   of the overall population's
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. 
genetic structures

  Market Centric Workshops
The Physics - Politics, Economics & Evolutionary Psychology
Politics, Economics & Evolutionary Psychology

Business Physics
Nature and nurture drive the business eco-system
Human nature
Emerging structure and dynamic forces of adaptation


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
| Home

Profiles | Papers | Glossary | E-mail us