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
Rob's Strategy Studio aims to celebrate and illuminate complex adaptive systems (CAS) just as an art studio does art. 

By way of introduction it notes the complex interactions that led to the partnership that discovered the DNA double helix and genetic code. 

The page introduces the RSS vision 'I act, therefore I think' approach and the RSS mission. 

It describes how many key aspects of the world are CAS including businesses. 

Finally the page explains each of the framed pictures displayed at the studio including the other aspects explored in this vision frame. 

Vision frame
Opportunity
This page looks at how scenarios allow people to relate to the possible evolution of the business and its products and services.  The Long view process is highlighted. 

Value based customer segmentation is reviewed.  Keirsey's psychological categorization and 'crossing the chasm' are highlighted. 

Three alternate systems are framed as long view scenarios (1) development of a billing mediation business, (2) development of the Grameen Bank the first micro loan bank and (3) some classic chess games. 

Some of the scenarios will be referenced in the SWOT and planning pages of this frame.  In particular the complex adaptive system (CAS) goals used will be referenced by the planning pages schemetic goals. 
Situation
The complex adaptive system (CAS) nature of a value delivery system is first introduced.  It's a network of agents acting as relays. 

The critical nature of hub agents and the difficulty of altering an aligned network is reviewed. 

The nature of and exceptional opportunities created by platforms are discussed. 

Finally an example of aligning a VDS is presented. 
Value delivery
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
Developing and delivering discrete modules of software works for Linux.  The approach is discussed along with the constraints. 
Modularity
The page discusses the search dilemma.  It describes how evolution solves the dilemma.  It introduces some problems that impact non evolutionary searches and suggests some strategies for such situations. 
Search
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
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. 
Ideas & plans
The page reviews how complex systems can be analyzed. 
The resulting analysis supports evaluation of system events. 
The analysis enables categorization of different events into classes. 
The analysis helps with recombination of the models to enable creativity. 
The page advocates an iterative approach including support from models. 

Analysis
This page introduces some problems that make it hard for a business to execute effectively. 
It then presents a theory of execution. 
It describes what the theory says must be done to execute effectively. 
It reviews General Electric's use of adaptive planning to support effective execution. 
Then it details the execution requirements. 
Execution
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. 
Programs
This page describes the organizational forces that limit change.  It explains how to overcome them when necessary. 

Progress & tradition
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. 
Tipping point
Meaning
Frames
Checklists
This page presents a search result list covering each linked region of rob's strategy studio. 


Search list
Glossary
References
Primary Navigation

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):
  • 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 explains how the examples relate to each other, why we all have trouble effectively comprehending these systems and explains how our inexperience with CAS can lead to catastrophe.  It outlines the items we see as key to the system and why. 

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 (
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):
  • 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 explains how the examples relate to each other, why we all have trouble effectively comprehending these systems and explains how our inexperience with CAS can lead to catastrophe.  It outlines the items we see as key to the system and why. 

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.  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
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:
  • 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 
Then he explains how in a physical world without time its perception can emerge:
  • 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

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 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
Carlo Rovelli resolves the paradox of 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 
Then he explains how in a physical world without time its perception can emerge:
  • 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

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.  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:
  • 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
Carlo Rovelli resolves the paradox of 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 
Then he explains how in a physical world without time its perception can emerge:
  • 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

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).  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:

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.

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.  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.  


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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 | 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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