This page describes the organizational forces that limit change.
It explains how to overcome
them when necessary.
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.
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.
The constraints are described.
The techniques to overcome them are implied.
Responding successfully to opportunities while limiting key uncertainties
Summary
First describes the dynamic nature of any complex adaptive system ( We are products of complexity,
but our evolution has focused our
understanding on the situation of hunter gatherers on the
African savanna.
As humanity has become more powerful we can significantly impact
the systems we depend on. But we struggle to comprehend
them. So this web frame
explores significant real world complex
adaptive systems (CAS):
CAS). - Assumptions of randomness & equilibrium allowed the
wealthy & powerful to expand the size and leverage of
stock markets, by placing at risk the insurance and
retirement savings of the working class. The
assumptions are wrong but remain entrenched.
- The US nation was built
from two divergent political
views of: Jefferson and Hamilton. It also
reflects the development
of competing ancient ideas of Epicurus and
Cyril. But the collapse of Bretton Woods forced Wall
Street into a position of power, while the middle and
working class were abandoned by the elites. Housing
financed with cash from oil and derivative transactions
helped hide the shift.
- Most US health care is still operating the way cars built in the 1940s did. Geisinger is an example of better solution. But transforming the whole network is a challenge. And public health investment has proved far more beneficial.
- Helping our children learn to be effective adults is part of our humanity, but we have created a robust but deeply flawed education system. Better alternatives have emerged.
- Spoken language, reading and writing emerged allowing our
good ideas to
become a second genetic material.
- The emergence of the global economy in the 1600s and its subsequent development;
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, although the two are deliberately conflated by ERISA. Keynes argued that most aspects of the future are uncertain, at best represented by ordinal probabilities, and often only by capricious hope for future innovation, fear inducing expectations of limited confidence, which evolutionary psychology implies is based on the demands of our hunter gatherer past. Deacon notes reduced uncertainty equates to information.
.
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 (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.
We are products of complexity,
but our evolution has focused our
understanding on the situation of hunter gatherers on the
African savanna.
As humanity has become more powerful we can significantly impact
the systems we depend on. But we struggle to comprehend
them. So this web frame
explores significant real world complex
adaptive systems (CAS):
CAS) seeks out and
transforms resources. Using a variety of distribution
techniques agents attempt to gain access to different - Assumptions of randomness & equilibrium allowed the
wealthy & powerful to expand the size and leverage of
stock markets, by placing at risk the insurance and
retirement savings of the working class. The
assumptions are wrong but remain entrenched.
- The US nation was built
from two divergent political
views of: Jefferson and Hamilton. It also
reflects the development
of competing ancient ideas of Epicurus and
Cyril. But the collapse of Bretton Woods forced Wall
Street into a position of power, while the middle and
working class were abandoned by the elites. Housing
financed with cash from oil and derivative transactions
helped hide the shift.
- Most US health care is still operating the way cars built in the 1940s did. Geisinger is an example of better solution. But transforming the whole network is a challenge. And public health investment has proved far more beneficial.
- Helping our children learn to be effective adults is part of our humanity, but we have created a robust but deeply flawed education system. Better alternatives have emerged.
- Spoken language, reading and writing emerged allowing our
good ideas to
become a second genetic material.
- The emergence of the global economy in the 1600s and its subsequent development;
This web page reviews opportunities to find and capture new
niches, based on studying fitness landscapes using complex
adaptive system (CAS) theory.
CAS SuperOrganisms are
able to capture rich niches. A variety of CAS are
included: chess, prokaryotes,
nation states, businesses, economies; along
with change mechanisms: evolution
and artificial
intelligence; agency
effects and environmental impacts.
Genetic algorithms supported by fitness functions are compared to genetic operators.
Early evolution of life and its inbuilt constraints are discussed.
Strategic clustering, goals, flexibility and representation of state are considered.
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 Genetic algorithms supported by fitness functions are compared to genetic operators.
Early evolution of life and its inbuilt constraints are discussed.
Strategic clustering, goals, flexibility and representation of state are considered.
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 Carlo Rovelli resolves the paradox of time.
Rovelli initially explains that low level physics does not include time:
time. Rovelli initially explains that low level physics does not include time:
- A present that is common throughout the universe does not exist
- Events are only partially ordered. The present is localized
- The difference between past and future is not foundational. It occurs because of state that through our blurring appears particular to us
- Time passes at different speeds dependent on where we are and how fast we travel
- Time's rhythms are due to
the gravitational field
- Our quantized physics shows neither
space nor time, just processes transforming physical
variables.
- Fundamentally there is no time. The basic equations evolve together with events, not things
- Our familiar time emerges
- Our interaction with the world is partial, blurred,
quantum indeterminate
- The ignorance determines the existence of thermal time
and entropy that quantifies our uncertainty
- Directionality of time is real
but perspectival. The entropy of the world in
relation to us increases with our thermal time. The
growth of entropy distinguishes past from future: resulting in
traces and memories
- Each human is a
unified being because: we reflect the world, we
formed an image of a unified entity by
interacting with our kind, and because of the perspective
of memory
- The variable time: is one of the variables of the gravitational field. With our scale we don't register quantum fluctuations, making space-time appear determined. At our speed we don't perceive differences in time of different clocks, so we experience a single time: universal, uniform, ordered; which is helpful to our decisions
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, although the two are deliberately conflated by ERISA. Keynes argued that most aspects of the future are uncertain, at best represented by ordinal probabilities, and often only by capricious hope for future innovation, fear inducing expectations of limited confidence, which evolutionary psychology implies is based on the demands of our hunter gatherer past. Deacon notes reduced uncertainty equates to information.
into a CAS. The effect of change cascades around the network of adaptive in evolutionary biology is a trait that increased the number of surviving offspring in an organism's ancestral lineage. Holland argues: complex adaptive systems (CAS) adapt due to the influence of schematic strings on agents. Evolution indicates fitness when an organism survives and reproduces. For his genetic algorithm, Holland separated the adaptive process into credit assignment and rule discovery. He assigned a strength to each of the rules (alternate hypothesis) used by his artificial agents, by credit assignment - each accepted message being paid for by the recipient, increasing the sender agent's rule's strength (implicit modeling) and reducing the recipient's. When an agent achieved an explicit goal they obtained a final reward. Rule discovery used the genetic algorithm to select strong rule schemas from a pair of agents to be included in the next generation, with crossing over and mutation applied, and the resulting schematic strategies used to replace weaker schemas. The crossing over genetic operator is unlikely to break up a short schematic sequence that provides a building block retained because of its 'fitness'; In Deacon's conception of evolution, an adaptation is the realization of a set of constraints on candidate mechanisms, and so long as these constraints are maintained, other features are arbitrary.
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 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.
Carlo Rovelli resolves the paradox of time.
Rovelli initially explains that low level physics does not include time:
time
and space. The Rovelli initially explains that low level physics does not include time:
- A present that is common throughout the universe does not exist
- Events are only partially ordered. The present is localized
- The difference between past and future is not foundational. It occurs because of state that through our blurring appears particular to us
- Time passes at different speeds dependent on where we are and how fast we travel
- Time's rhythms are due to
the gravitational field
- Our quantized physics shows neither
space nor time, just processes transforming physical
variables.
- Fundamentally there is no time. The basic equations evolve together with events, not things
- Our familiar time emerges
- Our interaction with the world is partial, blurred,
quantum indeterminate
- The ignorance determines the existence of thermal time
and entropy that quantifies our uncertainty
- Directionality of time is real
but perspectival. The entropy of the world in
relation to us increases with our thermal time. The
growth of entropy distinguishes past from future: resulting in
traces and memories
- Each human is a
unified being because: we reflect the world, we
formed an image of a unified entity by
interacting with our kind, and because of the perspective
of memory
- The variable time: is one of the variables of the gravitational field. With our scale we don't register quantum fluctuations, making space-time appear determined. At our speed we don't perceive differences in time of different clocks, so we experience a single time: universal, uniform, ordered; which is helpful to our decisions
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. Evolution's
schematic operators and Samuel
modeling together support the indirect recording of past
successes and their strategic use by the current agent to learn
how to succeed in the proximate environment.
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:
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 - 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.
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. 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.
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 Carlo Rovelli resolves the paradox of time.
Rovelli initially explains that low level physics does not include time:
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 Rovelli initially explains that low level physics does not include time:
- A present that is common throughout the universe does not exist
- Events are only partially ordered. The present is localized
- The difference between past and future is not foundational. It occurs because of state that through our blurring appears particular to us
- Time passes at different speeds dependent on where we are and how fast we travel
- Time's rhythms are due to
the gravitational field
- Our quantized physics shows neither
space nor time, just processes transforming physical
variables.
- Fundamentally there is no time. The basic equations evolve together with events, not things
- Our familiar time emerges
- Our interaction with the world is partial, blurred,
quantum indeterminate
- The ignorance determines the existence of thermal time
and entropy that quantifies our uncertainty
- Directionality of time is real
but perspectival. The entropy of the world in
relation to us increases with our thermal time. The
growth of entropy distinguishes past from future: resulting in
traces and memories
- Each human is a
unified being because: we reflect the world, we
formed an image of a unified entity by
interacting with our kind, and because of the perspective
of memory
- The variable time: is one of the variables of the gravitational field. With our scale we don't register quantum fluctuations, making space-time appear determined. At our speed we don't perceive differences in time of different clocks, so we experience a single time: universal, uniform, ordered; which is helpful to our decisions
This page discusses the mechanisms and effects of emergence
underpinning any complex adaptive system (CAS). Physical forces and
constraints follow the rules of complexity. They generate
phenomena and support the indirect emergence of epiphenomena.
Flows of epiphenomena interact in events which support the
emergence of equilibrium and autonomous
entities. Autonomous entities enable evolution
to operate broadening the adjacent possible.
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: Example flows are outlined. Constraints on flows support the emergence of the systems. Examples of constraints are discussed.
- Ensure a broad pipeline of developments become viable
businesses before the enterprises financial measures choke
off the resources needed. If that was not possible but
the enterprise is in the lucky position of having a powerful
profit generating business, it can
- Use market share based metrics to catalyze the development
pipeline. Here the control of resource Flows of different kinds are essential to the operation of complex adaptive systems (CAS).flows to new developments is strategically managed. Since the environment may offer alternative strategic paths forward: data network standardization might have coalesced around protocols based on 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.
Example flows are outlined. Constraints on flows support the emergence of the systems. Examples of constraints are discussed.
, SNA is IBM's system network architecture which specified the formats, protocols and architecture of their layered computer data network.
or TCP (TCP), a point-to-point connection oriented protocol specified and standardized by IETF and widely implemented in Internet communications.
/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. ; a portfolio of businesses can be setup with internal competition driving each business to achieve high productivity as the corporate portfolio covers all perceived market scenarios. Most companies won't have that freedom, so once they strangle their development pipeline, they can
- Attempt to become a branded market where partner's new products are sold to customers.
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 integrates situational context, state and signals to prioritize among strategies and respond in a timely manner. It occurs in all animals, including us and our organizations:
- Individual human decision making includes conscious and unconscious aspects. Situational context is highly influential: supplying meaning to our general mechanisms, & for robots too. Emotions are important in providing a balanced judgement. The adaptive unconscious interprets percepts quickly supporting 'fast' decision making. Conscious decision making, supported by the: DLPFC, vmPFC and limbic system; can use slower autonomy. The amygdala, during unsettling or uncertain social situations, signals the decision making regions of the frontal lobe, including the orbitofrontal cortex. The BLA supports rejecting unacceptable offers. Moral decisions are influenced by a moral decision switch. Sleeping before making an important decision is useful in obtaining the support of the unconscious in developing a preference. Word framing demonstrates the limitations of our fast intuitive decision making processes. And prior positive associations detected by the hippocampus, can be reactivated with the support of the striatum linking it to the memory of a reward, inducing a bias into our choices. Prior to the development of the PFC, the ventral striatum supports adolescent decision making. Neurons involved in decision making in the association areas of the cortex are active for much longer than neurons participating in the sensory areas of the cortex. This allows them to link perceptions with a provisional action plan. Association neurons can track probabilities connected to a choice. As evidence is accumulated and a threshold is reached a choice is made, making fast thinking highly adaptive. Diseases including: schizophrenia and anorexia; highlight aspects of human decision making.
- Organisations often struggle to balance top down and distributed decision making: parliamentry government must use a process, health care is attempting to improve the process: checklists, end-to-end care; and include more participants, but has systemic issues, business leaders struggle with strategy.
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.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.
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 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, although the two are deliberately conflated by ERISA. Keynes argued that most aspects of the future are uncertain, at best represented by ordinal probabilities, and often only by capricious hope for future innovation, fear inducing expectations of limited confidence, which evolutionary psychology implies is based on the demands of our hunter gatherer past. Deacon notes reduced uncertainty equates to information.
, 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:
SWOT)
to a particular business can all change. - 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.
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. Evolution's
schematic operators and Samuel
modeling together support the indirect recording of past
successes and their strategic use by the current agent to learn
how to succeed in the proximate environment.
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. 
 Politics, Economics & Evolutionary Psychology |
Business Physics |
 |
integrating quality appropriate for each market |
This page looks at schematic structures
and their uses. It discusses a number of examples:
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 |
- 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.
This page uses an example to illustrate how:
Program Management |
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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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