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.
The whole is more than the sum of the parts
Summary
This page discusses the mechanisms and effects of emergence underpinning any 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). Key research is
reviewed. John Holland's framework for representing complexity is outlined. Links to other key aspects of CAS theory discussed at the site are presented.
Introduction
John Holland eloquently explains how new phenomena can emerge from certain types of systems."A
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.
small number of rules or laws can
generate systems of surprising complexity, M. Mitchell Waldrop describes a vision of complexity via: - Rich interactions that allow a system to undergo spontaneous self-organization
- Systems that are adaptive
- More predictability than chaotic systems by bringing order and chaos into
- Balance at the edge of chaos
He argues that the complexity contains recognizable features, which recur. They are also dynamic. He goes on "that
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 create the emergent behavior
of rule based systems. " ... "The simple laws of the agents
generate an emergent behavior far beyond their individual
capabilities. It is noteworthy that this emergent behavior
occurs without direction by a central executive. "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.
What makes the agents emerge? A little self-assembly is required. A structure which can
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.
support a plan and self-assemble into
an 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 amplifier can do
this. Hence Ribose Nucleic Acids (RNA (RNA), a polymer composed of a chain of ribose sugars. It does not naturally form into a paired double helix and so is far less stable than DNA. Chains of DNA are converted by transcription into equivalently sequenced messenger m-RNA. RNA also provides the associations that encode the genetic code. Transfer t-RNAs have a site that maps to the codon and match the associated amino-acid. Stuart Kauffman argues that RNA polymers may be the precursor to our current DNA based genome and protein based enzymes. In the adaptive web framework's (AWF) Smiley we use a similar paradigm with no proteins. ), which can self-assemble, support schematic plans as in m-RNA (RNA), a polymer composed of a chain of ribose sugars. It does not naturally form into a paired double helix and so is far less stable than DNA. Chains of DNA are converted by transcription into equivalently sequenced messenger m-RNA. RNA also provides the associations that encode the genetic code. Transfer t-RNAs have a site that maps to the codon and match the associated amino-acid. Stuart Kauffman argues that RNA polymers may be the precursor to our current DNA based genome and protein based enzymes. In the adaptive web framework's (AWF) Smiley we use a similar paradigm with no proteins.
and catalytic, an infrastructure amplifier. actions such as transfer (t-RNA (RNA), a polymer composed of a chain of ribose sugars. It does not naturally form into a paired double helix and so is far less stable than DNA. Chains of DNA are converted by transcription into equivalently sequenced messenger m-RNA. RNA also provides the associations that encode the genetic code. Transfer t-RNAs have a site that maps to the codon and match the associated amino-acid. Stuart Kauffman argues that RNA polymers may be the precursor to our current DNA based genome and protein based enzymes. In the adaptive web framework's (AWF) Smiley we use a similar paradigm with no proteins.
) provides a high profile
The agents in complex adaptive
systems (CAS) must model their
environment to respond effectively to it. Samuel
modeling is described as an approach.
model.
With Carlo Rovelli resolves the paradox of time.
Rovelli initially explains that low level physics does not include time:
time, emergent systems are
likely to generate specialized agents better suited to
particular tasks than the general purpose initiator. These
new agents will replace and obscure the earlier structures
essential role. 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
Agents are not required for emergence however. Stuart Kauffman explains that very basic binary networks demonstrate emergent properties. In particular the binary state of the elements can be transformed by the network into small numbers of emergent attractor regions which are stable unless the network is perturbed. Terrence Deacon
Terrence Deacon explores how constraints on dynamic flows can
induce emergent phenomena
which can do real work. He shows how these phenomena are
sustained. The mechanism enables
the development of Darwinian competition.
describes a framework of
dynamic processes that bridges from thermodynamic
chemistry to ends-focused functional systems. Physical systems, such as rivers, can be observed to demonstrate the emergence of persistent state. The state can remain as long as the resources that support the flows exist. But, the physical forces acting on the system typically perturb the situation, for example disrupting a
Barriers are particular types of constraints on flows. They can enforce
separation of a network of agents allowing evolution to build
diversity. Examples of different types of barriers: physical
barriers, chemical
molecules can form membranes, probability based,
cell membranes can include controllable
channels, eukaryotes
leverage membranes, symbiosis, human emotions, chess, business; and
their effects are described.
barrier,
allowing the resources to disperse. Epiphenomena are emergent
Abbott explains that a 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.
phenomena is
emergent if it is conceptualized independently of the platform
that implements it. I.E. it is used and modeled without a
direct dependence on the underlying phenomena and physical
mechanisms. Abbott demonstrates how emergent epiphenomena can
encapsulate conceptual rules which can be associated with
predictive theories, and these will hold while the underlying
physics is in a matching phase. Abbott identifies two classes of emergent entities:
Autonomous entities are implementations of epiphenomena's abstract designs, which can thus use
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 of flows of energy
to sustain themselves. Example flows are outlined. Constraints on flows support the emergence of the systems. Examples of constraints are discussed.
Chemical structures, emergent epiphenomena, focusing
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.
physical phenomena, can provide a 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.
re-combinable active matrix from which
agents and schematic plans can be derived. Similarly the
adaptive web framework (AWF)'s This page describes the Copycat
Coderack.
The details of the codelet architecture are described.
The specialized use of the Coderack by the adaptive web framework's (AWF) Smiley is discussed.
The codelet scheduling mechanism is discussed.
A variety of Smiley extensions to the Coderack are reviewed.
The Coderack infrastructure functions are included.
Coderack
associates re-combinable The details of the codelet architecture are described.
The specialized use of the Coderack by the adaptive web framework's (AWF) Smiley is discussed.
The codelet scheduling mechanism is discussed.
A variety of Smiley extensions to the Coderack are reviewed.
The Coderack infrastructure functions are included.
This page describes the Copycat
Workspace.
The specialized use of the Workspace by the adaptive web framework's (AWF) Smiley is discussed.
How text and XML are imported into the Smiley Workspace is described.
Telomeric aging of schematic structures is introduced.
The internal data structure used to represent the state of each workspace object is included.
The Workspace infrastructure functions are included.
Workspace objects is a collection of: happenings, occurrences and processes; including emergent entities, as required by relativity, explains Rovelli. But natural selection has improved our fitness by representing this perception, in our minds, as an unchanging thing, as explained by Pinker. Dehaene explains the object modeling and construction process within the unconscious and conscious brain. The specialized use of the Workspace by the adaptive web framework's (AWF) Smiley is discussed.
How text and XML are imported into the Smiley Workspace is described.
Telomeric aging of schematic structures is introduced.
The internal data structure used to represent the state of each workspace object is included.
The Workspace infrastructure functions are included.
, and strings, with
This page describes the Smiley
infrastructure and codelets
that instantiate the epiphenomena
defined in the Meta file
and Slipnet.
Infrastructure sensors are introduced.
The role of phenomena in shaping the environment is discussed.
The focusing of forces by phenomena in Smiley is discussed.
The Meta file association of case keywords with phenomena is included.
The codelets and supporting functions are included.
epiphenomena reflecting properties
defined in the Infrastructure sensors are introduced.
The role of phenomena in shaping the environment is discussed.
The focusing of forces by phenomena in Smiley is discussed.
The Meta file association of case keywords with phenomena is included.
The codelets and supporting functions are included.
This page describes the Copycat
Slipnet.
The goal of the Slipnet is reviewed.
Smiley's specialized use of the Slipnet is introduced.
The initial Slipnet network used by the 'Merge Streams' and 'Virtual Robot' agent-based applications is setup in initchemistry and is included.
The Slipnet infrastructure and initialization functions are included.
Slipnet or its basic
'physical' constants. The goal of the Slipnet is reviewed.
Smiley's specialized use of the Slipnet is introduced.
The initial Slipnet network used by the 'Merge Streams' and 'Virtual Robot' agent-based applications is setup in initchemistry and is included.
The Slipnet infrastructure and initialization functions are included.
Complex systems emerge from the presence of feedback during the competition of a collection of simpler autonomous entities for scarce resources.
If the competing 'entities' include an action inducing plan the feedback can include
Plans change in complex adaptive systems (CAS) due to the action of genetic
operations such as mutation, splitting and recombination.
The nature of the operations is described.
genetic operations
on the plans. 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) agents can emerge. These genetically
framed agents can support the emergence of additional predictive
mental John Holland's framework for representing complexity is outlined. Links to other key aspects of CAS theory discussed at the site are presented.
The agents in complex adaptive
systems (CAS) must model their
environment to respond effectively to it. Samuel
modeling is described as an approach.
models that improve the
genes' chance of reproduction and survival: Consciousness has confounded philosophers and scientists for
centuries. Now it is finally being characterized
scientifically. That required a transformation of
approach.
Realizing that consciousness was ill-defined neuroscientist Stanislas Dehaene and others characterized and focused on conscious access.
In the book he outlines the limitations of previous psychological dogma. Instead his use of subjective assessments opened the window to contrast totally unconscious brain activity with those including consciousness.
He describes the research methods. He explains the contribution of new sensors and probes that allowed the psychological findings to be correlated, and causally related to specific neural activity.
He describes the theory of the brain he uses, the 'global neuronal workspace' to position all the experimental details into a whole.
He reviews how both theory and practice support diagnosis and treatment of real world mental illnesses.
The implications of Dehaene's findings for subsequent consciousness research are outlined.
Complex adaptive system (CAS) models of the brain's development and operation introduce constraints which are discussed.
consciousness, Realizing that consciousness was ill-defined neuroscientist Stanislas Dehaene and others characterized and focused on conscious access.
In the book he outlines the limitations of previous psychological dogma. Instead his use of subjective assessments opened the window to contrast totally unconscious brain activity with those including consciousness.
He describes the research methods. He explains the contribution of new sensors and probes that allowed the psychological findings to be correlated, and causally related to specific neural activity.
He describes the theory of the brain he uses, the 'global neuronal workspace' to position all the experimental details into a whole.
He reviews how both theory and practice support diagnosis and treatment of real world mental illnesses.
The implications of Dehaene's findings for subsequent consciousness research are outlined.
Complex adaptive system (CAS) models of the brain's development and operation introduce constraints which are discussed.
Carlo Rovelli resolves the paradox of time.
Rovelli initially explains that low level physics does not include time:
time,
vision;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
Kauffman's adjacent possible
Kauffman explains how natural selection expands the 'adjacent possible', by exposing Darwinian pre-adaptations to new competitive situations where they provide a selective advantage. As an example he describes how the swim bladder emerged from a pre-adaptation of a fish lung. "Paleontologists have traced the This page reviews the implications of selection, variation and
heredity in a complex adaptive system (CAS).
The mechanism and its emergence are
discussed.
evolution of swim
bladders from early fish with lungs. Some of these lived
in oxygen-poor water. The lungs grow as outpouchings from
the gut. The fish swallowed the oxygen-poor water, some of
which entered the lungs, where air bubbles were absorbed, making
it easier for the fish to survive. But now water and air
were both in a single lung and the lung was pre-adapted to
evolve into a new function--a swim bladder that adjusted neutral
buoyancy in the water column. "The emergence of multi-cellular
This page reviews the implications of reproduction initially
generating a single child cell. The mechanism and
resulting strategic options are discussed.
organisms depends on the previous
development of: Agents use sensors to detect events in their environment.
This page reviews how these events become signals associated
with beneficial responses in a complex adaptive system (CAS). CAS signals emerge from
the Darwinian information model. Signals can indicate decision summaries and level of
uncertainty.
signalling
infrastructure, differential
responses
to signal strength, movement, structural binding; and a
schematic plan that allows the cellular agent to This page discusses the interdependence of perception and
representation in a complex adaptive system (CAS). Hofstadter
and Mitchell's research with Copycat is
reviewed.
respond to external position relative
signals by transposing them to, and then maintaining the, cell
local coordinate signals for its cell to produce synchronized
actions across the locally dividing population.
Techniques include segmentation, identity coordinate signalling,
and presence of an ordered schematic response to the local
coordinate identity of any segment. The emergence of order from chaos provides an explanation for the apparently random period between water droplets falling from a tap. Typically the model of the system is poor and so the data captured about the system looks unpredictable - chaotic. With a better model the system's operation can be explained with standard physical principles. Hence chaos as defined here is different from complexity. in CAS represents a powerful adaptive mechanism.
.
 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 |
Home
- 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.
Profiles | Papers | Glossary | E-mail us