|
In this page we:
- Introduce some current
problems of complexity for humanity
- Explain how to use
the site.
There are two main starting points:
- The example systems
frame and
- The presentation
frame.
- And then there is how
we use the site.
The site uses lots of click through. That's so that you
can see the underlying principles that are contributing to the
system being discussed. We hope that as you internalize
and reflect on the principles the system should appear in a new
light. At this site clicking is good!
Opportunities frame |
This web page reviews persistent business challenges with
complex adaptive system (CAS)
theory.
Exploring opportunities |
Dark webs can enhance
individual creativity, local operational autonomy, enterprise
strategic alignment and organizational learning. In this
page we summarize the opportunity.
Dark webs |
The productivity of
complex adaptive system (CAS) is reviewed highlighting the most
significant variables: access to raw
materials, agency based leverage of additional wage
laborers/consumers to build a SuperOrganism during
cliodynamic up-cycles, wealth
amplifying infrastructure build-out,
trading
network time capture offset by instability of amplifier
driven bubbles requiring strategic management and extended
phenotypic alignment and disruption; when they expand markets
for goods & services. The CAS and classical economic
approaches are compared.
Important CAS aspects are highlighted:
- CAS reflect the history of
all the events of the network of agents and their environment
- The relevant economic
history is reviewed demonstrating the contribution of
power, politics, war...
- Chemical
structures capture and preserve important recipes that
allow agents to increase search/operational effectiveness
and wealth & the
system to be robust
- Environment matched
to system strategy: Superorganism
and beetle
- Cliodynamic models of historical agent networks allows a
realistic assessment
of productivity over a full network cycle
- The models must be matched
to the proximate environment
- Internal failures
of the agent network
- Existential
threats to the agent network
Human agents must dedicate: focus, time,
coherence and skills; to productively generate wealth. And they could
do much more - learning to develop
and use formal schematic plans
during their education, and using the skill when participating
in a superorganism.
CAS level
productivity improvements are due to:
- Collective solidarity ensures evolved amplifiers are fully
expressed
- Valuable schematically defined, emergent actions must be
accessible to resource controlling and allocating schemata
and their agents
- Meta ideas that can be reused and recombined
- Distribution of these ideas allow parallel searching
- Trading to gain time
- Isolated agents can be integrated into the current network
during each growth phase, but cliodynamic assessments show
agents are dropped again from the network during the decline
phase of the cycle
- Network
effects and leverage of power drive productivity
improvements.
Human agent level productivity
- Agent level productivity
improvements of significance
- More time: Increased light,
reduced moving & travelling, quicker & better
eating, reduced rework, motivated & effective
- Broader utilization with adoption of standards &
undermining of monopoly
constraints
- Weapons & armor
- Power available: Driving
flows &
actions in required direction
- Iterative theory & practice
- Infrastructure & tools: catalytic
reduction in cost of repeated operations
- Agent level productivity improvements of
limited effect
Productivity of CAS |
Representative democracy's robustness is dependent on emotional
and cultural
aspects of humanity. The impact of YouTube's
recommendation engine on the adolescent mind has
undermined the genetic
operators provided by culture. Typical parental constraints on
the associations allowed to adolescents are undermined and
emotional links are built to the most emotive ideas, based
simply on their capacity to sustain attention to YouTube.
An outline
mechanism is described that reintroduces 'parental'
constraints. Legislative enforcement of the capability is
required.
Details of the theoretical complex adaptive system (CAS)
requirements of genetic operations are introduced. The
minds implementation of the schematic operators is
explained. Traditional cultural constraints limiting large
changes in the schema base are outlined.
Aligning YouTube & democracy |
Organizations can benefit from understanding and leveraging
creativity. In this page we review what creativity is,
highlight the
opportunity - including when it is
appropriate to apply, how to do
that organizationally, and when it might
be avoided, and the challenges with
enabling it when it is desirable.
We introduce the aspects of the creative process.
Leveraging creativity |
In Gray Matter Michael Graziano asks Are we Really
Conscious? He argues that we build inaccurate models of
reality and then develop intuitions based on these problematic
models. He concludes we can't use intuitions to understand
consciousness. Instead he promotes 'brain science' as more
accurate and argues it suggests we are not conscious. In
this page we summarize his article and then use complex adaptive
system (CAS) theory to review his
arguments. Constrained by CAS theory and mechanisms of
emergence we see a requirement for consciousness.
Graziano's consciousness |
Consciousness is no longer mysterious. In this page we use
complex adaptive system (CAS)
theory to describe the high-level
architecture of consciousness, linking sensory networks,
low level feelings and
genetically conserved and deployed neural structures into a high
level scheduler. Consciousness is evolution's
solution to the complex problems of effective, emergent,
multi-cellular perception based strategy.
Constrained by emergence and needing
to avoid the epistemological
problem of starting with a blank slate with every birth,
evolution was limited in its options.
We explain how survival value allows evolution to leverage
available tools: sensors, agent relative position, models, perception
& representation; to solve the problem of mobile
agents responding effectively to their own state and proximate environment.
Evolution did this by providing a genetically
constructed framework that can
develop into a conscious CAS.
And we discuss the implications with regard to artificial
intelligence, sentient robots,
augmented intelligence, and
aspects of philosophy.
On the nature of conscious things |
John Searle's influential thought experiment implied to him that
computers cannot understand. Complex adaptive system (CAS) theory indicates that this is
not the case.
Understanding the Chinese room |
In his talk 'The Science of Ending Aging' Aubrey de Grey argues
we should invest more in maintenance of our bodies. In
this page we summarize his video comments and then use complex
adaptive system (CAS) theory to
review his arguments. Focusing the lens of CAS theory and
mechanisms of emergence on the system we highlight the pros and
cons of ending aging.
Ending aging |
The essence of
a library complex adaptive system (CAS) is defined.
Implications
for the future development of contemporary libraries
are reviewed.
Libraries evolving |
This web page reviews opportunities to benefit from modeling
agent based flows using complex adaptive system (CAS) theory.
Agent based flows |
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.
Fitness landscapes |
Biologically inspired computing |
|
|
Potential of biologically inspired computing
Summary
This web page reviews opportunities to enhance computing theory
and practice by using biological mechanisms and complex adaptive
system ( This page introduces the complex adaptive system (CAS) theory
frame. The theory provides an organizing framework that is
used by 'life.' It can be used to evaluate and rank models
that claim to describe our perceived reality. It catalogs
the laws and strategies which underpin the operation of systems
that are based on the interaction of emergent
agents. It highlights the
constraints that shape CAS and so predicts their form. A
proposal that does not conform is wrong.
John Holland's framework for representing complexity is
outlined. Links to other key aspects of CAS theory
discussed at the site are presented.
CAS) theory.
Introduction
Using biology's computational model as the future of computing
is proposed to:
- Overcome the Von
Neumann fetch compute store, John was a brilliant Hungarian mathematician who published the earliest paper specifying architecture for digital computing. It ensured this computing architecture was not patentable. The architecture has a central processing unit (CPU), random access storage addressable by the CPU and a sequencer. The architecture encourages a serial software architecture that matches the logic of the sequencer and processing operations on program and data. Von Neumann, his history, computing architecture and some alternative architectures are reviewed by Melanie Mitchell. bottleneck with massively
parallel systems.
- Increase the robustness of computations by duplication of
processing.
- Improve 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 of
programmed systems by use of an 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.
architecture.
- Leverage biological
designs which have been optimized over billions of
years.
Today the
Internet engineering task force (IETF, the internet engineering task force controls the processes that manage the architecture of the internet. )
process and the Internet show how effective the parallel
computing model can be. The IETF's request for comment
(RFC) specification process is highly analogous to 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 a 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 database of RFCs. The
fitness is, according to Dawkins, a suitcase word with at least five meanings in biology: - Darwin and Wallace thought in terms of the capacity to survive and reproduce, but they were considering discrete aspects such as chewing grass - where hard enamel would improve the relative fitness.
- Population geneticists: Ronald Fisher, Sewall Wright, J.B.S. Haldane; consider selection at a locus where for a genotype: green eyes vs blue eyes; one with higher fitness can be identified from genotypic frequencies and gene frequencies, with all other variations averaged out.
- Whole organism 'integrated' fitness. Dawkins notes there is only ever one instance of a specific organism. Being unique, comparing the relative success of its offspring makes little sense. Over a huge number of generations the individual is likely to have provided a contribution to everyone in the pool or no one.
- Inclusive fitness, where according to Hamilton, fitness depends on an organism's actions or effects on its children or its relative's children, a model where natural selection favors organs and behaviors that cause the individual's genes to be passed on. It is easy to mistakenly count an offspring in multiple relative's fitness assessments.
- Personal fitness represents the effects a person's relatives have on the individual's fitness [3]. When interpreted correctly fitness [4] and fitness [5] are the same.
of each new
generation of protocol specification is tested by deployment
through multiple parallel implementations. When these are
found to operate satisfactorily, and the RFC and deployed
systems are accepted as being useful, the RFC becomes a standard
track protocol and becomes represented widely in Internet
infrastructure.
The set of networking services and protocols that are covered by
the IETF's open RFC process have grown broadly as the early platform is agent generated infrastructure that supports emergence of an entity through: leverage of an abundant energy source, reusable resources; attracting a phenotypically aligned network of agents. 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 construction, deployment and
revision cycle, enabled 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.
niche
expansion.
The robustness and increased performance of massively parallel
computing applications is demonstrated by Google search.
Leveraging the opportunity that a networked web of HTML pages
offered, Google's capture, analysis and search processes are
designed to leverage racks of computers to break up the search
problem into parallel activities. But the algorithms are
still designed, implemented and operated by software
engineers.
Google deploys its map, reduce applications over a distributed
storage infrastructure that ensures the applications are close
to a copy of the data they are working with. Biological
systems use cell division to generate a similar effect.
Since complex adaptive system ( This page introduces the complex adaptive system (CAS) theory
frame. The theory provides an organizing framework that is
used by 'life.' It can be used to evaluate and rank models
that claim to describe our perceived reality. It catalogs
the laws and strategies which underpin the operation of systems
that are based on the interaction of emergent
agents. It highlights the
constraints that shape CAS and so predicts their form. A
proposal that does not conform is wrong.
John Holland's framework for representing complexity is
outlined. Links to other key aspects of CAS theory
discussed at the site are presented.
CAS)
based 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 replicate common 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 data, programs with a CAS
architecture, such as This page describes the Adaptive Web framework (AWF) test system
and the agent programming framework (Smiley) that supports its
operation.
Example test system statements are included.
To begin a test a test statement is loaded into Smiley while
Smiley executes on the Perl
interpreter.
Part of Smiley's Perl code focused on setting up the
infrastructure is included bellow.
The setup includes:
- Loading the 'Meta file'
specification,
- Initializing the Slipnet,
and Workspaces and loading them
- So that the Coderack
can be called.
The Coderack, which is the focus of a separate
page of the Perl frame then schedules and runs the codelets
that are invoked by the test statement structures.
the adaptive test
framework, can be deployed over a distributed storage
infrastructure, such as HADOOP is an open source implementation of the 'big data' distributed file system architecture used by Google to support its map-reduce programs. The map-reduce programs construct associations between vast numbers of web pages and typical search terms. .
HADOOP can then be leveraged to help ensure that only light
weight 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 need be sent between
the computers on which the agents are distributed.
The general computing paradigm does not reflect biology in its
architecture. While the business activities supporting
product development is a phase during the operation of a CAS agent. It allows for schematic strategies to be iteratively blended with environmental signals to solve the logistical issues of migrating newly built and transformed sub-agents. That is needed to achieve the adult configuration of the agent and optimize it for the proximate environment. Smiley includes examples of the developmental phase agents required in an emergent CAS. In situations where parents invest in the growth and memetic learning of their offspring the schematic grab bag can support optimizations to develop models, structures and actions to construct an adept adult. In humans, adolescence leverages neural plasticity, elder sibling advice and adult coaching to help prepare the deploying neuronal network and body to successfully compete.
are 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 and 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. the application
paradigm is not. Google not withstanding the general
approach still reflects Von
Neumann's arguments, John was a brilliant Hungarian mathematician who published the earliest paper specifying architecture for digital computing. It ensured this computing architecture was not patentable. The architecture has a central processing unit (CPU), random access storage addressable by the CPU and a sequencer. The architecture encourages a serial software architecture that matches the logic of the sequencer and processing operations on program and data. Von Neumann, his history, computing architecture and some alternative architectures are reviewed by Melanie Mitchell. with:
- A central processing unit (CPU) resource supplied with
instructions and data to process. Biology uses a
massively distributed processing model which reduces the CPU
access bottleneck. Consequently biological operations
do not have to limit competitive invocations, and are less
likely to need transactional is an operation which guarantees to complete a defined set of activities or return to the initial state. For a fee the postal service will ensure that a parcel is delivered to its recipient or will return the parcel to the sender. To provide the service it may have to undo the act of trying to deliver the parcel with a compensating action. Since the parcel could be lost or destroyed the service may have to return an equivalent value to the sender.
protection.
- Instructions processed in series, unless a jump is
executed.
- Applications are designed to directly implement specific
functional requirements. Maintenance issues and
engineering quality strategies including limited
connections, reuse, directness and information hiding have
promoted
Bertrand Meyer develops arguments, principles and strategies for
creating modular software. He concludes that abstract data
types and inheritence make object orientation a superior
methodology for software construction. Complex adaptive
system (CAS) theory suggests agents provide an alternative strategy
to the use of objects.
object oriented methods and
tool chains. Biology uses Rather than oppose the direct thrust of some environmental flow agents
can improve their effectiveness with indirect responses.
This page explains how agents are architected to do this and
discusses some examples of how it can be done.
indirect methods which encourage
emergence.
- Testing is applied to the operation reflecting the design
of the modules and the requirements of the system. The
testing process aims to select for
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.
fitness,
filtering instances of the implementation which fail the
criteria. This is an This page reviews the implications of selection, variation and
heredity in a complex adaptive system (CAS).
The mechanism and its emergence are
discussed.
evolutionary
selection
process, but it acts directly on the source code
instead of the schematic structures, such as the engineer's
understandings of the requirements and designs. The This page describes the Adaptive Web framework (AWF) test system
and the agent programming framework (Smiley) that supports its
operation.
Example test system statements are included.
To begin a test a test statement is loaded into Smiley while
Smiley executes on the Perl
interpreter.
Part of Smiley's Perl code focused on setting up the
infrastructure is included bellow.
The setup includes:
- Loading the 'Meta file'
specification,
- Initializing the Slipnet,
and Workspaces and loading them
- So that the Coderack
can be called.
The Coderack, which is the focus of a separate
page of the Perl frame then schedules and runs the codelets
that are invoked by the test statement structures.
Adaptive web framework (AWF) testing
infrastructure (2)
explores more biologically aligned alternatives.
- The released application then competes in the
marketplace. Finally selection has an impact on the
schematic structures.
The application does not respond flexibly to the
situation. It expects specific input 'signals' and uses
these to transition between its predetermined set of
states. If the application is given unexpected inputs it
can only reject them. If its understanding of the real
world is flawed when it detects a mismatch at best it can
perform error recovery to return to a defined state.
A biologically inspired computing architecture would have
improved 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 should
enable adaptive 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.
learning. it
would include:
Each of these aspects turns out to be complex.
Biological sensors
Real world sensors have to detect relatively low level aspects
of the physical world. The input signals are typically multi-modal, real world effects include sound, light, touch etc. at the same time. so multiple
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 will detect aspects of a
signal. Physiological psychologists have studied
how animals perform this type of sensing. Vast
collections of networked sensors deployed peripherally detect
the low level aspects of each signal. Detected signals are
flowed through layers of a network where coincidences are
identified and used to associate the aspects of the signals with
potential higher level effects of the real world. At the
highest levels these associations have been integrated into a
multi-mode association which is related to the animals
understanding of its own situation and that of its immediate 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.
environment.
Peripheral sensors can include Representing state in emergent entities is essential but
difficult. Various structures are used to enhance the rate
and scope of state transitions. Examples are
discussed.
mechanisms
to:
- Resolve basic aspects of the signal locally so that only
highly significant attributes are passed on to other agents
in the network.
- Align detector deployment with local aspects of the
environment.
Use of
biological structures and procedures to improve the fitness of
designs
The development of 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 structures
in the form of genetic and neuronal, specialized eukaryotic cells include channels which control flows of sodium and potassium ions across the massively extended cell membrane supporting an electro-chemical wave which is then converted into an outgoing chemical signal transmission from synapses which target nearby neuron or muscle cell receptors. Neurons are supported by glial cells. Neurons include a: - Receptive element - dendrites
- Transmitting element - axon and synaptic terminals. The axon may be myelinated, focusing the signals through synaptic transmission, or unmyelinated - where crosstalk is leveraged.
- Highly variable DNA schema using transposons.
data structures introduces the possibility of supporting human 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 with Tools and the businesses that produce them have evolved
dramatically. W Brian Arthur shows how this occurred.
tools
that utilize schematic This page discusses the interdependence of perception and
representation in a complex adaptive system (CAS). Hofstadter
and Mitchell's research with Copycat is
reviewed. The bridging of a node from a network of 'well
known' percepts to a new representational instance is discussed
as it occurs in biochemistry, in consciousness and
abstractly.
representations
and support perceptions and 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.
exploration
of the adjacent possible.
Already wikis provide support for the shared development of germ-line, a master copy of the schematic structures is maintained for reproduction of offspring. There will also be somatic copies which are modified by the operational agents so that they can represent their current state.
representations - most notably wikipedia. The
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 updating of this data is an
area of focus for the wiki community, but it does not include
use of This page reviews the implications of reproduction initially
generating a single initialized child cell. For
multi-cellular organisms this 'cell' must contain all the germ-line schematic
structures including for organelles and multi-generational epi-genetic
state. Any microbiome
is subsequently integrated during the innovative deployment of
this creative event. Organisms with skeletal
infrastructure cannot complete the process of creation of an
associated adult mind, until the proximate environment has been
sampled during development.
The mechanism and resulting strategic options are
discussed.
a single cell developmental
bottleneck for initializing the distributed somatic, Schematic structures which are used to support the operation of the agent. They are modified as the agent's state changes unlike the germ-line schemata. state.
Potentially the somatic This page discusses the interdependence of perception and
representation in a complex adaptive system (CAS). Hofstadter
and Mitchell's research with Copycat is
reviewed. The bridging of a node from a network of 'well
known' percepts to a new representational instance is discussed
as it occurs in biochemistry, in consciousness and
abstractly.
representation,
could include perceptions of the: active 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, First describes the dynamic
nature of any complex adaptive system (CAS).
It then introduces the broad effects of change
which includes opportunities and risks/uncertainties.
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.
target
niches, 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.
operational 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 stores and 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, and 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 system;
Web frames
As a start, in line with our understanding of cognitive is the ability to orchestrate thought and action in accordance with internal goals according to Princeton's Jonathan Cohen. processing,
AWF's event processor supports the
construction of frames of tailored
web pages. Each page of a frame aims to include only
information, and links, relevant to the specific focus of the
web page. A frame of pages allows different points of
focus to be constructed, each linked together to provide the
reader with a paced and, hopefully, comprehensive
perspective.
Networks
of agents with shared state
Agent-based programming, as instantiated in AWF's This page describes the Adaptive Web framework (AWF) test system
and the agent programming framework (Smiley) that supports its
operation.
Example test system statements are included.
To begin a test a test statement is loaded into Smiley while
Smiley executes on the Perl
interpreter.
Part of Smiley's Perl code focused on setting up the
infrastructure is included bellow.
The setup includes:
- Loading the 'Meta file'
specification,
- Initializing the Slipnet,
and Workspaces and loading them
- So that the Coderack
can be called.
The Coderack, which is the focus of a separate
page of the Perl frame then schedules and runs the codelets
that are invoked by the test statement structures.
adaptive test framework extends the
processing of events to adapting to them with agents which
emerge from schematic codelet
aggregates.
Automated genetic algorithms provide a vision for increasing 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 creativity
of designed systems.
Infrastructure
supporting adaptive coordinated deployment of the network of
agents
Douglas
Hofstadter and Melanie Mitchell's This page discusses the interdependence of perception and
representation in a complex adaptive system (CAS). Hofstadter
and Mitchell's research with Copycat is
reviewed. The bridging of a node from a network of 'well
known' percepts to a new representational instance is discussed
as it occurs in biochemistry, in consciousness and
abstractly.
Copycat
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, 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
and 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 define the basis of our
emergent architecture for deploying agents.
Hofstadter and Mitchell's Copycat infrastructure needs
augmentation with This page describes the Smiley
infrastructure that supports the associative binding of schematic strings to codelets
defined in the Meta file
and Slipnet.
The infrastructure supporting the associations is introduced.
The role of Jeff Hawkins neocortical attributes is discussed.
Relevant Slipnet configurations are
included.
The codelets and supporting functions are included.
associative labeling
of the 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. Mathematicians view anything that can be defined and used in deductive reasoning and mathematical proofs as an object. These mathematical objects can be values of variables, allowing them to be used in formulas. ,
but is otherwise essentially able to provide a parallel
environment for the codelet based agents.
The infrastructure also needs to operate in parallel to reflect
the massively parallel biochemical systems. A start is to
support:
- GPU streams within the Copycat infrastructure, and
- Networks of cooperating Copycats.
There seems to be much potential to gain from biologically
inspired computing. But the difficulty of identifying the
operations of highly parallel and 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. biological
systems and correctly interpreting biological mechanisms has so
far resulted in speculative proposals that over simplify the
aggregate operation of biological systems.
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