Network effects
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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Network effects

Summary
This page discusses the effect of the network on the
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
participating 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
).  Small world and scale free networks are considered
Introduction
Network effects sustain improved efficiency of
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
between connected
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
, and enhanced
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
for major connectivity 'hubs'.  The effects can
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emerge
as agents connect to each other.  Topology confers specific properties and constraints on the network.  A change to the topology, just one additional link, can alter the networks properties and constraints. 
Small world & scale free networks
Closely connected agents will often be connected in a
This page discusses the benefits of geographic clusters of agents and resources at the center of a complex adaptive system (CAS). 
cluster
.  Outside of the cluster weak links and hubs connect other parts of the network. 

Moore argues that the development of a market begins when technologists develop the bare bones technology.  Visionaries then become the backers of early deployments.  A chasm is them bridged by hub communicators spreading the details broadly to the early majority who initiate general deployment and mainstream market growth. 

It is typical that the details are
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. 
memetic
: they are easily copied, and potentially confer benefits on those agents that become infected.  The presence of hubs and the ability of the meme to re-infect ensure recirculation can occur indefinitely. 

Networks are robust to random node failures.  They are highly impacted if hubs fail.  The adaptive nature of agents and their need for the resources supplied by the flows through the network results in cascading failures once a hub fails.  Hence it is worth
This page discusses the benefits of proactively strengthening strong points. 
prophylactic
ally overprotecting hubs. 
Processes and operations
Often the agents in the network will include some
  • Performing an action (an operation) which transforms some resource into a needed output. 
  • While others assist in the process of connecting and buffering one operation with another. 
The aggregate nature of the agents, the limited scope of agent's
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
, their adaptive architecture and the limitations to the
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
models
they use to interpret their environment allow the operations to become highly interdependent.  Unless the operations have been designed to be
This page discusses the strategy of modularity in a complex adaptive system (CAS).  The benefits, mechanism and its emergence are discussed. 
modular
changes to them may have unexpected side effects.  The situation can be mitigated if the effort is made to represent accurately the current state of the whole network of operations. 

In all
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 systems (CAS)
the drive to connect with the key hubs will ensure the hubs benefit from positive returns, W Brian Arthur's conception of how high tech products have positive economic feedback as they deploy.  Classical products such as foods have negative returns to scale since they take increasing amounts of land, and distribution infrastructure to support getting them to market.  High tech products typically become easier to produce or gain from network effects of being connected together overcoming the negative effects of scale.  
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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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