Opportunity
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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Responding successfully to opportunities while limiting key uncertainties

Summary
First describes the dynamic nature of any complex adaptive system (
This web frame explores very significant example real world complex adaptive systems (CAS).  It explains how the examples relate to each other, why we all have trouble effectively comprehending these systems and outlines the items we see as key to the system and why.  By understanding these summaries you can better frame the interdependencies of important events such as war in Iraq, new iPhone releases or a cancer diagnosis and see how they are impacting you. 

CAS
). 

It then introduces the broad effects of change which includes opportunities and risks, is an assessment of the likelihood of an independent problem occurring.  It can be assigned an accurate probability since it is independent of other variables in the system.  As such it is different from uncertainty. 
/uncertainties is when a factor is hard to measure because it is dependent on many interconnected agents and may be affected by infrastructure and evolved amplifiers.  This is different from Risk.  

As a CAS grows opportunities become undermined so they must be acted on quickly

Uncertainties are also transformed and relayed by the dynamic network.  In particular the recombination of current and new ideas brought in from the network is discussed. 

The dynamic nature of a CAS
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
of
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
that makes up a complex adaptive system (
This web frame explores very significant example real world complex adaptive systems (CAS).  It explains how the examples relate to each other, why we all have trouble effectively comprehending these systems and outlines the items we see as key to the system and why.  By understanding these summaries you can better frame the interdependencies of important events such as war in Iraq, new iPhone releases or a cancer diagnosis and see how they are impacting you. 

CAS
) seeks out and transforms resources.  Using a variety of distribution techniques agents attempt to gain access to different
This web page reviews opportunities to find and capture new niches based on studying fitness landscapes using complex adaptive system (CAS) theory. 
environmental
niches with the aim of finding, capturing and exploiting resources.  The growth rates of agent populations potentially increase exponentially when there are abundant resources.  However, population growth and supply perturbations, as induced by
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. 
infrastructure amplification
, mean that resource availability is likely to vary with time. 
Change adds opportunity and risk
Change can introduce opportunity and risk, is an assessment of the likelihood of an independent problem occurring.  It can be assigned an accurate probability since it is independent of other variables in the system.  As such it is different from uncertainty. 
/uncertainty is when a factor is hard to measure because it is dependent on many interconnected agents and may be affected by infrastructure and evolved amplifiers.  This is different from Risk.   into a CAS.  The effect of change cascades around the network of adaptive
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
.  The scope of change is large.  The environment varies over time and space.  The
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. 
rule base
generating the agents can change.  Resource availability can change.  Agent capabilities can change. 
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 plans
will change due to the action 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. 
genetic operators
and selection pressure. 
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
Models
and valuations will change as agents respond 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
from the environment, their own operations and other agents. 

For a product business significant changes demand re-evaluation of
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. 
plans
and value judgments to enable the executives to adjust the business and designers to reflect any changed
The drive to fulfill current customer requirements can result in the innovator's dilemma.  While the customer interest can diminish typical requirements databases continue to reflect the earlier desire. 

Accurate modeling of the customer's roles and goals creates a more predictive indicator.  Close relationships with sentinel customers for key target segments help build the models. 

Processes should also support the migration of product and customers to the winning architecture in a positive return market. 
product requirements
in the design.  Iterative development in partnership with early-adopter customers has enabled this process to proceed rapidly. 
Developing and delivering discrete modules of software works for Linux.  The approach is discussed along with the constraints. 
Re-factoring of the current design and implementation must be assumed
to allow the team space to work towards a competitive modular design. 

The schematic structure is represented by ideas in the minds of the executives and designers.  These are the associations they make through allocation and sharing of goals, models, values and proposed actions.  The agreement on values, responsibilities and priorities builds coherence when the underlying ideas are synergistic.  Written representations of these structures, the plans and designs, should also reflect the associations, goals, models, evaluations and actions. 
Opportunities must be acted on rapidly
If opportunities are not recognized, or taken, when they appear a profit oriented enterprise will over time become internally constrained, and financially committed, by the need to show high profit from any new developments. This drives the executives to limit the resources allocated to the
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent businesses
.  With a limited
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. 
resource supply
the new business will die.  In effect one must either:

Uncertainty is also relayed and transformed by the adaptive network
The complex battle between computer systems vendors to promote adoption of their proprietary data networking products was transformed by the introduction of government backed solutions: OSI (OSI), a set of communications interconnection standards defined by the International Standards Organization (ISO) global standards body.  OSI competed unsuccessfully with the IETF's TCP/IP.  The basic seven layer 'model' of OSI is still influential.   and in particular TCP (TCP), a point-to-point connection oriented protocol specified and standardized by IETF and widely implemented in Internet communications. 
and IP (IP), a datagram based connectionless protocol specified by the IETF.  It can be used by TCP as its network layer protocol when sending and receiving data packets.   due to the presence of the IETF iterative processes, and the profit limiting effect of standardization.  The processes allowed broad assimilation of the networking protocol specifications, and implementations of the protocols by the development community.  The
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 extensibility was matched by the broadened customer choice of products fulfilling their needs.  The cost structure, which had previously been a powerful competitive weapon, of the major enterprises capable of providing complete
This page reviews the strategy of bundling multiple products within a single offer in a complex adaptive system (CAS).  The mechanism is discussed with examples from biology and business. 
bundled
proprietary solutions was too high to maintain in the changed environment, while the
This page reviews the inhibiting effect of the value delivery system on the expression of new phenotypic effects within an agent. 
phenotypically aligned network
of suppliers, partners and customers demanded continued commitment to DECnet and IBM's SNA.  The process induced a
This page reviews Christensen's disruption of a complex adaptive system (CAS).  The mechanism is discussed with examples from biology and business. 
disruptive
transformation of the proprietary networks. 

A CAS model of the development activity highlights that the process induces critical genetic operations.  The integration of new businesses and the activity of design are genetic operations which will recombine ideas, goals and actions into the schematic plans that structure the enterprise's decision making. 

Entrepreneurs put their reputations at risk sponsoring software development projects that may end up supplying the wrong product, late and over budget.  Fred Brooks likens large software project management to struggling to get out of a Tar Pit. 

A product extension is typically of limited risk.  Development of Hewlett-Packard's OpenMail messaging server did not end up with continual cost and schedule over-runs so what was different?  With life-cycle practices captured from the results of earlier attempts to use similar technologies to develop and deploy equivalent products, good access to the target market, and the iterative selection of appropriate project life-cycle approaches the development can repeatedly hit the schedule.

During periods when the
The drive to fulfill current customer requirements can result in the innovator's dilemma.  While the customer interest can diminish typical requirements databases continue to reflect the earlier desire. 

Accurate modeling of the customer's roles and goals creates a more predictive indicator.  Close relationships with sentinel customers for key target segments help build the models. 

Processes should also support the migration of product and customers to the winning architecture in a positive return market. 
requirements
remain consistent, as they did between HP's Deskmanager and OpenMail the architects could take their previous experience into account in designing the system.  Each engineer, who also developed the automated tests, was aware of the strategies that had worked for his sub-system and what improvements they proposed to introduce.  For OpenMail the result was a layered modular system, with well understood module inter-dependencies clean automation interfaces and limited combinations of sub-systems that required testing.

Starting a project implies corporate
Agents can manage uncertainty by limiting their commitments of resources until the environment contains signals strongly correlated with the required scenario.  This page explains how agents can use Shewhart cycles and SWOT processes to do this. 
commitments which need to be matched to pre-conditions
which if met suggest an acceptable risk is being taken.  Institutionalized procedures can allow management to represent corporate goals in the operations activities. 

We were careful to match the market situation to the development life-cycle.  If you are just starting a new software development project I would recommend to you adopting some market focused methodology with a good associated process life-cycle aligned with the corporate goals.  

However, when the environment changes dramatically the experience base that had been supporting the design process may not be valid.  Broadening the experience base introduces uncertainty, since the recombined set of ideas and processes may conflict. 

The difficulty is that as the environment changes the strengths, weaknesses, opportunities and threats (
The page describes the SWOT process.  That includes:
  • The classification of each event into strength weakness opportunity and threat.  
  • The clustering process for grouping the classified events into goals.  
  • How the clusters can support planning and execution. 
Operational SWOT matrices and clusters from the Adaptive Web Framework (AWF) are included as examples. 
SWOT
) to a particular business can all change. 

There are additional uncertainties implicit in attempting to integrate with a disjoint business providing solutions in an unrelated target market.  Without the right
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
models
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. 
sensors
to detect significant changes the same process that was working satisfactorily in a business will likely fail in unexpected ways.  When it is possible to merge experience bases and
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. 
iterative processes
allow feedback to stimulate shared learning it is possible to adapt to the new situation, but the uncertainty is still much higher and the expectations should be set appropriately.  


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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. 
Program Management
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