Program theory
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
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Using a program for progress

Summary
This page discusses the program strategy 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
).  Programs generate coherent
This page reviews the strategy of architecting an end-to-end solution in a complex adaptive system (CAS).  The mechanism and its costs and benefits are discussed. 
end-to-end
activity.  The mechanism is reviewed. 
Introduction
The program process provides a mechanism for analysis, differentiation, and focused operation of an
This page reviews the strategy of architecting an end-to-end solution in a complex adaptive system (CAS).  The mechanism and its costs and benefits are discussed. 
end-to-end coherent
activity performed by a number of agents, including the:

A typical program creates 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. 
local schematic structure
where it can both represent its environment, and its own
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. 
state
, and, through
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 operations
, select, differentiate and specialize its
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 actions. 

The program develops
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
models
of the end-to-end activity to enable controlled judgments to be made about the implications of the results of any actions carried out within the program and its
This page reviews the inhibiting effect of the value delivery system on the expression of new phenotypic effects within an agent. 
extended phenotypic
network. 

Coordination of the various agents situations and actions allows the program leaders to
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. 
catalyze
the
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
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. 
signal
s within the program.  As '
E. O. Wilson & Bert Holldobler illustrate how bundled cooperative strategies can take hold.  Various social insects have developed strategies which have allowed them to capture the most valuable available niches.  Like humans they invest in specialization and cooperate to subdue larger, well equipped competitors. 
superorganisms
' the catalytic, an infrastructure amplifier.   activity also allows for signals to be shaped and augmented and leverages the
This page discusses the effect of the network on the agents participating in a complex adaptive system (CAS).  Small world and scale free networks are considered. 
network effects of major hubs


The use of iterations of
Walter Shewhart's iterative development process is found in many complex adaptive systems (CAS).  The mechanism is reviewed and its value in coping with random events is explained. 
Shewhart cycles
allows the hypothetical models, representing each of the identified set of program agent's personal, group and team goals, and strategies to achieve these, defined as predicted valuations of success, to be improved as the results of actions provide more detailed information about the actual
This page discusses the interdependence of perception and representation in a complex adaptive system (CAS).  Hofstadter and Mitchell's research with Copycat is reviewed. 
relationship between the program's  local environment and its current goals and strategies


The flat structure of a program limits
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: the visibility 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
, and the general availability of schemata; within the group of agents, enhancing
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. 
flexibility
and the richness of modeling.  However, such a structure tends to threaten other organizational relationships that exist with the broader system and undermines specialization strategies of the program's agents. 
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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
| 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. 
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