On the nature of conscious things
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On the nature of conscious things 

Summary
Consciousness is no longer mysterious.  In this page we use 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
) theory to help explain the nature of consciousness.  Consciousness is
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
evolution's
solution to the complex problems of effective, emergent, multi-cellular perception based
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
.  Constrained by
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
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:
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
,
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. 
agent
relative position,
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
models
,
This page discusses the interdependence of perception and representation in a complex adaptive system (CAS).  Hofstadter and Mitchell's research with Copycat is reviewed. 
perception & representation
; to solve the problem of mobile agents understanding their
This page discusses the potential of the vast state space which supports the emergence of complex adaptive systems (CAS).  Kauffman describes the mechanism by which the system expands across the space. 
environment
.  Evolution did this by providing 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. 
genetically
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. 
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. 
Introduction
Advances in how we research and model biological systems and build engineered systems have helped reveal significant aspects of what it means to be a conscious individual.  We can now combine these discoveries with our understanding of
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
to understand:

Firstly
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
evolution
was able to avoid the epistemological problem of starting each new moving
This page reviews the implications of reproduction initially generating a single child cell.  The mechanism and resulting strategic options are discussed. 
organism
with an uncompetitive understanding of its local
This page discusses the potential of the vast state space which supports the emergence of complex adaptive systems (CAS).  Kauffman describes the mechanism by which the system expands across the space. 
environment
.  A problem often referred to as the Blank Slate. 

The problem of learning about ourselves and our local environment
Starting off simply, Prokaryotes, a single cell system exemplified by the bacteria.  Prokaryotes have their own DNA and infrastructure within a single enclosure.   with membrane, formed from a lipid (fat) bilayer which creates a barrier between aqueous (water soluble) media.  In AWF a key property of membranes - their providing a catalytic environment and supporting the suspension of enzymatically active proteins within the membrane; is simulated with a Workspace list where 'active' structures can be inserted and codelets can detect and act on the structure's active promise configured as an association in the Slipnet.   associated flagella, a set of whip shaped structures (flagellum) which can be actively moved. 
can move around to find food, and avoid toxins.  They have to be able to cope with a changing external environment.  And due to sharing of plasmids and R-factors provide bacteria with a way to transfer parts of their DNA complement with one another.  The effect is to ensure that useful mutations can become rapidly distributed within a population of bacteria.   they can develop differently over time.  So they have the need to understand their current
This page discusses the potential of the vast state space which supports the emergence of complex adaptive systems (CAS).  Kauffman describes the mechanism by which the system expands across the space. 
internal and external situation
.  And
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
evolution
requires that each discovery be based on details built by chemical
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
from
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. 
genomic information
based on chance
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. 
mutations
.  Hence the prokaryotes understanding of their proximate environment can only be based on
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
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
models
represented in the evolved
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
that make the
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
adapt. 

An example is how the prokaryote seeks out food. 
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
detecting an increasing food gradient can signal the flagella to drive the prokaryote forward.  Otherwise the flagella can be reversed to tumble the bacteria to randomly select a new direction to try. 

The point is that the benefit of finding food has been encoded into the genes through the success 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. 
mutations
that enabled
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. 
agency
to both model its environment and then consequently to move the prokaryote towards food.  Evolution leverages long timespans and competition to
Richard Dawkin's explores how nature has created implementations of designs, without any need for planning or design, through the accumulation of small advantageous changes. 
capture a grab bag of useful tools
that can be deployed.  But to select the right tool at the right time requires
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
modeling, learning
and
This page describes the specialized codelets that provide life-cycle and checkpoint capabilities for Smiley applications. 
The codelets implement a Shewhart cycle. 
The structural schematic nature of the cycle is described. 
Transcription factor codelets operate the phase change controls. 
How inhibitory agents are integrated into the cycle is described. 
An application agent with management and operational roles emerges. 
The codelets and supporting functions are included. 
managing
.  The genes can depend on evolution to provide learning about situations that are repeatedly experienced over generations and demand deployment of particular tools.  But perceiving and representing the current situation works best with more immediate representations used in
This page describes the specialized codelets that provide life-cycle and checkpoint capabilities for Smiley applications. 
The codelets implement a Shewhart cycle. 
The structural schematic nature of the cycle is described. 
Transcription factor codelets operate the phase change controls. 
How inhibitory agents are integrated into the cycle is described. 
An application agent with management and operational roles emerges. 
The codelets and supporting functions are included. 
managing
.  And since that provides evolutionary benefit and can be built with the available tools it is also provided. 


Multi-cell eukaryotes, a relatively large multi-component cell type from which yeast and multi-celled plants and animals, including humans, is constructed.  It contains modules including a nucleus and production functions such as mitochondria.   can utilize the same tricks that evolution captured with single cell progenitors.  But the details about themselves and how different cells in the organism interact with the external environment demand additional mechanisms.  As mutations create these beneficial mechanisms the genes will indirectly provide details of the use of the new tools including models of the appropriate situations for their use and how the tools were deployed. 

Reproduction of mammals uses positional
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 placenta to add asymmetry to the initial dividing
This page reviews the implications of reproduction initially generating a single child cell.  The mechanism and resulting strategic options are discussed. 
organism
.  The
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
on the cells' membranes detect the signal gradients and activate
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
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. 
networks
which differentially alter the
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 structures
operating on and through the
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. 
genes
of the cluster of cells.  Differentiation results from this process and it will proceed all but identically in the vast majority of cases of a developing new organism.  But it is necessary for the developing multi-cell organism to build an understanding of its external environment and how it, and its kin, has
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
evolved


A position oriented network of cellular agents is setup during development using genetic information which evolution has provided with maps is a patchwork of neurons dedicated to fragments of shape.  Keiji Tanaka experimentally identified these invariants within the temporal cortex.  Various of these combinatorial codes exist at points in the visual system: V1, V2, TEO;
- captured
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
models
of
This page discusses the interdependence of perception and representation in a complex adaptive system (CAS).  Hofstadter and Mitchell's research with Copycat is reviewed. 
percepts and representations
that helped with survival: 
This is an evolved process that has been coopted to
Reading and writing present a conundrum.  The reader's brain contains neural networks tuned to reading.  With imaging a written word can be followed as it progresses from the retina through a functional chain that asks: Are these letters? What do they look like? Are they a word? What does it sound like? How is it pronounced? What does it mean?  Dehaene explains the importance of education in tuning the brain's networks for reading as well as good strategies for teaching reading and countering dyslexia.  But he notes the reading networks developed far too recently to have directly evolved.  And Dehaene asks why humans are unique in developing reading and culture. 

He explains the cultural engineering that shaped writing to human vision and the exaptations and neuronal structures that enable and constrain reading and culture. 

Dehaene's arguments show how cellular, whole animal and cultural complex adaptive system (CAS) are related.  We review his explanations in CAS terms and use his insights to link cultural CAS that emerged based on reading and writing with other levels of CAS from which they emerge. 

allow us to read which has been well studied



Modeling in the visual system
The sensory neurons, 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 include a:
  • Receptive element - dendrites
  • Transmitting element - axon and synaptic terminals
in the eyes provide us with a jigsaw like set of sense data.  The brain then uses an iterative assembly like process to model the situation.  The process selects a base map object, from a set specified genetically, such as a cartoon like face, and then associates additional attributes to the base object, in an iterative review of the associative match between the input pieces and mapped signals.  The result is to reach agreement on a best fit between the models and the data.  The associations are positional, and depend on the sensory inputs being mapped to various positional regions consistently by the mapping process.  The result is the ability to rapidly build a best fit representation of an object, such as President Bill Clinton's face, from associations:
It seems likely that other sensory modes operate similarly. 

Why do we 'see' the assembled model?  Because using the model in this way provided a competitive advantage and was analogous to prior simpler agents performing sensory processing: touch, smell; the visual agent network has been retained and elaborated over evolutionary time.  Montague
Read Montague explores how brains make decisions.  In particular he explains how:
  • Evolution can create indirect abstract models, such as the dopamine system, that allow
  • Life changing real-time decisions to be made, and how
  • Schematic structures provide encodings of computable control structures which operate through and on incomputable, schematically encoded, physically active structures and operationally associated production functions. 
illustrates
how a model, such as the dopamine network, can support integration and modulation of a myriad of sensory and control signals to 'head' towards a real world goal.  He notes that these systems use abstract critic signals and depend on context to provide meaning.  So modeling experience has value.  Why present the model experientially?  The qualia are the direct qualities of percepts according to Haikonen.  He argues they do not require interpretation or any evocation of meaning.  Colors are colors and pain is pain.  The human visual hierarchy seems at odds with this interpretation with meaning being associated with letters by signalling from the letterbox to the frontal lobes and used in the feedback flows that identify and prime morphemes. 
are all present and an experiential model can leverage these since it presents itself as reality.  Any other solution would be much more problematic to keep consistent and valuable. 

Where do we see it? 
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
Emergent
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
capabilities of this sort are positional, networked and distributed and depend on cooperative signalling to coordinate the positional agents into a coherent emerged consciousness.  For example there is no master ant, bee or termite for the colonies general operations.  CAS robustness strategies use parallelism.  Indeed
E. O. Wilson & Bert Holldobler illustrate how bundled cooperative strategies can take hold.  Various social insects have developed strategies which have allowed them to capture the most valuable available niches.  Like humans they invest in specialization and cooperate to subdue larger, well equipped competitors. 
insect superorganisms'
dependence on one queen to facilitate reproduction of the colony allows parasites
This page discusses the strategy of confusing the control system of a complex adaptive system (CAS). 
opportunities to unbalance the leader and flourish
.  Having a single queen helps ensure there is a
This page reviews the implications of reproduction initially generating a single child cell.  The mechanism and resulting strategic options are discussed. 
single individual developmental bottleneck
to define the next superorganism. 


In contrast, deep-learning networks are representational models that achieve high performance on difficult pattern recognition problems in vision and speech.  But they need specialized training methods such as greedy layerwise pre-training or HF optimization.   do not have direct access to genetic infrastructure.  They have sensors built to accept array-processing algorithms selected by designers to match the problem they are focused on.  And while they simulate the adaptive connections and weighting mechanisms of neurons, 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 include a:
  • Receptive element - dendrites
  • Transmitting element - axon and synaptic terminals
, the associative hidden layer settings depend on designers to select appropriate large networks of data to observe.  The result is what Andrew Ng termed "effective, but brittle."  Operational systems like Deepmind's AlphaGo add a separate
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
Samuel based competitive learning process


Similarly robots have no intrinsic mechanism to evolve and do not include the vast set of evolved tools collected by evolution. 
Haikonen juxtaposes the philosophy and psychology of consciousness with engineering practice to refine the debate on the hard problem of consciousness.  During the journey he describes the architecture of a robot that highlights the potential and challenges of associative neural networks. 

Complex adaptive system (CAS) theory is then used to illustrate the additional requirements and constraints of self-assembling evolved conscious animals.  It will be seen that Haikonen's neural architecture, Smiley's Copycat architecture and molecular biology's intracellular architecture leverage the same associative properties. 

Haikonen's sentient robot
demonstrates the power of [auto-] associative networks but it depends on human engineering to 'evolve'. 


Perception, fast and slow
Fast and slow matching of perception and representation must be present in all moving organisms.  For single-cell organisms the slow representations are mutation based and the fast structures are built by genes.  For multi-cellular organisms with neuron, 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 include a:
  • Receptive element - dendrites
  • Transmitting element - axon and synaptic terminals
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. 
networks
, both genetic and memetic representations are used: 

Qualia are the direct qualities of percepts according to Haikonen.  He argues they do not require interpretation or any evocation of meaning.  Colors are colors and pain is pain.  The human visual hierarchy seems at odds with this interpretation with meaning being associated with letters by signalling from the letterbox to the frontal lobes and used in the feedback flows that identify and prime morphemes. 
can be seen to represent both the jigsaw of genetically defined sensory signals in visual processing and the unconsciously position-associated networks of genetically encoded neurons, 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 include a:
  • Receptive element - dendrites
  • Transmitting element - axon and synaptic terminals
which both recognize and can replace the sensory signals in the modeling process that performs
This page discusses the interdependence of perception and representation in a complex adaptive system (CAS).  Hofstadter and Mitchell's research with Copycat is reviewed. 
perception and representation


Additionally it has been evolutionarily valuable to think strategically about certain problems.  Relatively slow, but abstract mechanisms are included in evolution's tool box: 
Slow conscious processes
As
Haikonen juxtaposes the philosophy and psychology of consciousness with engineering practice to refine the debate on the hard problem of consciousness.  During the journey he describes the architecture of a robot that highlights the potential and challenges of associative neural networks. 

Complex adaptive system (CAS) theory is then used to illustrate the additional requirements and constraints of self-assembling evolved conscious animals.  It will be seen that Haikonen's neural architecture, Smiley's Copycat architecture and molecular biology's intracellular architecture leverage the same associative properties. 

Haikonen
notes combinatorial explosion can be a problem for associative networks.  Various mechanisms can be used to constrain this including modulatory networks and
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. 

conscious access


Once a map object and its associations have been selected for access to consciousness then other alternative representations are suppressed allowing the prime choice to be amplified and associated with additional infrastructure that provides focus, appropriate orientation of sensors, or an emotional shift. 

If the percepts are internal appearences of the external world and the body according to Haikonen.  RSS views them as evolved models that are:
  • Associated schematically with the signals generated in response to epi-phenomena detected by sensory receptors and
  • Acted on by emergent agents.  
enabling access to consciousness stop then attention will shift and no further reinforcement will occur.  

Sleep facilitates salient memory formation and removal of non-salient memories.  The five different stages of the nightly sleep cycles support different aspects of memory formation.  The sleep stages follow Pre-sleep and include: Stage one characterized by light sleep and lasting 10 minutes, Stage two where theta waves and sleep spindles occur, Stage three and Stage four together represent deep slow-wave sleep (SWS) with delta waves, Stage five is REM sleep; sleep cycles last between 90-110 minutes each and as the night progresses SWS times reduce and REM times increase.   Sleep includes the operation of synapse synthesis and maintenance through DNA based activity including membrane trafficking, synaptic vesicle recycling, myelin structural protein formation and cholesterol and protein synthesis. 
allows the analysis, destruction and garbage collection 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. 
memetic details
which are not found significant enough to be retained long term. 


No explanatory gap found
With perceptions being based on
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. 
genetic and memetic
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
responding to signals and state inputs by
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
modeling
and performing actions there is no explanatory gap
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
Evolution
has been able to make the models an effective representation of reality, removing the epistemological gap, but is not required to achieve an exact match as is illustrated powerfully by the existence of illusions

Distributed agents signalled by the modeling agents are aware enough to respond even at the prokaryotic, a single cell system exemplified by the bacteria.  Prokaryotes have their own DNA and infrastructure within a single enclosure.   enzymatic, a protein with a structure which allows it to operate as a chemical catalyst and a control switch. 
level. 

As a slow, conscious activity
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emerged
within the networked, distributed set of eukaryotic, a relatively large multi-component cell type from which yeast and multi-celled plants and animals, including humans, is constructed.  It contains modules including a nucleus and production functions such as mitochondria.   multi-cellular agents to support practice and strategic modeling, having continued awareness of the models provided survival value. 

This critical dependence on evolution to collect the models that support consciousness is our criticism of
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. 
Searle's logic about his Chinese Room
.  Kurzweil's singularity assumes that exponential increases in computing power and leverage of redundancy in our brains will allow for the uploading of a specific brain with all the mental processes intact.  At a minimum that suggests accurate representations of the:
The theory of emergence suggests the models that Kurzweil depends on will struggle to effectively represent these aspects of the complete CAS of which the brain is a part. 

RSS places more faith in the transformative power of schematic structures such as
An epistatic meme suppressed for a thousand years reemerges during the enlightenment. 
It was a poem encapsulating the ideas of Epicurus rediscovered by a humanist book hunter. 
Greenblatt describes the process of suppression and reemergence.  He argues that the rediscovery was the foundation of the modern world. 
Complex adaptive system (CAS) models of the memetic mechanisms are discussed. 

On The Nature of Things



Amazon advancing
Amazon.com provides an interesting example of augmented intelligence.  Its organization supports:
  • A physical supply process with human operations, and or robots.  This is integrated with
  • Adaptive models about what customers' desire and are purchasing.  They use these computer based models to generate personal
  • Signal based offers to their customers.  They also have 
  • Echo inside their customers' homes providing additional feedback and encouraging voice based enhanced intelligence services that can run as
  • Amazon web services. 
Like the
A government sanctioned monopoly supported the construction of a superorganism American Telephone and Telegraph (AT&T).  Within this Bell Labs was at the center of three networks:
  1. The evolving global scientific network. 
  2. The Bell telephone network.  And
  3. The military industrial network deploying 'fire and missile control' systems. 
Bell Labs strategically leveraged each network to create an innovation engine. 
Once the monopoly was dismantled AT&T disrupted. 
Complex adaptive system (CAS) models of the innovation mechanisms are discussed. 

20th century AT&T
Amazon's network provides value and presents real-world problems that Amazon can focus its research and development skills upon a highly intelligent
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


Businesses like Amazon can use W. Brian Arthur's combinational evolution.   In contrast human evolution is constrained to use
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
Darwinian mechanisms
.  Although we see similar CAS
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. 
constraints
acting on both of these evolutionary mechanisms Striedter's analogy between business and brain evolution seems to mistakenly converge the two mechanisms which is not possible since there is:

Some acknowledgements
Many thanks go to Joffre Baker and John Corallo.  And special thanks to Marta for supporting the development of RSS.  The failures in the forgoing analysis are mine but any insights were built with their help.  Joff introduced me to
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. 

Dehaene
and
Haikonen juxtaposes the philosophy and psychology of consciousness with engineering practice to refine the debate on the hard problem of consciousness.  During the journey he describes the architecture of a robot that highlights the potential and challenges of associative neural networks. 

Complex adaptive system (CAS) theory is then used to illustrate the additional requirements and constraints of self-assembling evolved conscious animals.  It will be seen that Haikonen's neural architecture, Smiley's Copycat architecture and molecular biology's intracellular architecture leverage the same associative properties. 

Haikonen
's work on consciousness and shared his views about the subject.  Both Joff and John have reviewed my CAS based ideas. 


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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
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This page uses an example to illustrate how:
  • A business can gain focus from targeting key customers,
  • Business planning activities performed by the whole organization can build awareness, empowerment and coherence. 
  • A program approach can ensure strategic alignment. 
Program Management
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