Fitness landscapes
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Representation of fitness landscapes

Summary
This web page reviews opportunities to find and capture new niches based on studying fitness landscapes using 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. 
Introduction
The geneticist Sewell Wright suggested that each point in a genetic combinatorial set be assigned a measure of its adaptiveness.  As a three dimensional representation this has been described as a fitness landscape. 

While its an attractive metaphor and it has inspired key work on
This web page reviews opportunities to enhance computing theory and practice by using biological mechanisms and complex adaptive system (CAS) theory. 
evolutionary computation
the vision of a landscape can be misinterpreted.  Stuart Kauffman's 'adjacent possible' seems to better illustrate the way
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
explore niches.  That fitness changes as the agents 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. 
environment
alter adds further complications. 

The early adjacent possible is going to be limited.  One can imagine that early agents had access to a very small set 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 plans
, types 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. 
sensor
or
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 the environment.  Indeed the agents may well have been identical and placing each other under heavy threat in using the same strategies to compete directly for the few niches they could utilize. 

But
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 the effect of
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. 
gathering mechanisms
that create, or gain access to, additional niches into the schematic grab bag.  The pressure to specialize and the capability to do so ensure that any action that enhances a mechanism that creates, or improves access to, an additional niche will be captured.  Epistasis tends to help extend the portfolio of the schematic structure.  Significantly the niches that evolution gains access to are almost all of its creation

Contrary to the pressure to change are mechanisms that drive superior phenotypic strategies into dominance and
This page reviews the inhibiting effect of the value delivery system on the expression of new phenotypic effects within an agent. 
extend phenotypic alignment
beyond the
This page reviews the implications of reproduction initially generating a single child cell.  The mechanism and resulting strategic options are discussed. 
organism


Hence the presence of disjoint, environmentally different, niches can have the effect of assisting in additional niche capture and support
This page reviews Christensen's disruption of a complex adaptive system (CAS).  The mechanism is discussed with examples from biology and business. 
disruption
of aligned networks.  Operations, such as 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.   formation and tooth construction, that actively generate layered deployments of new niches, such as in coral reef formation, extend the adjacent possible by:
In any sequence of adaptive interactions
This page discusses the impact of random events which once they occur encourage a particular direction forward for a complex adaptive system (CAS). 
frozen accidents
will also ensure that the situation will evolve in a totally unique way. 

So schematic fitness seems to depend both on the specific environmental opportunities, and the specific ordered actions of the current set of adapting agents and a landscape metaphor should be used with caution. 

The application of fitness functions by
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 algorithms
to
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent
environments certainly requires careful scrutiny.  It utilizes desired target values of selected capabilities to represent fitness.  Schemata controlling agents which most closely meet those targets in any particular generation are selected to develop subsequent generations.  Due to the testing of multiple offspring and the representation of many parts of the fitness landscape within each schematic string selection will drive subsequent generations to increasingly match the target capabilities. 

The evolutionary process applied to generations of competing agents will select for optimum competitive use of accessible niches.  However, the genetic algorithm leverages the designer's choices of capabilities and target values to explore the fitness landscape.  Evolution, in contrast, must use the schematically associated collection of tools available to agents, and the evolved relationship between these and the environment that the agents exist in.  With these evolution supports competition in indirectly forcing adjacent possibilities into becoming accessible opportunities.  Evolution's genetic operators probe the nature of the tool set and environment, and are supported in the search by the constraining nature of the changes operators can make to the schematic strings. 

Adaptive agents can successfully tie actions to schematic structures analogous to genetic algorithms when the environment is constrained and
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. 
rules
are known.  It is also important that the parallel search amplification of genetic algorithms be present. 
Evolving with an adapted environment
Early 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
) agents would be formed from a limited schematic database.  The schematic building blocks of proteins, a relatively long chain (polymer) of peptides.  Shorter chains of peptides are termed polypeptides.   are 20 amino acids are the building blocks of proteins.  The 20 main variants differ by the nature of their side chain.  Some are positively charged, others negatively charged.  Some are water seeking while others are fat seeking.  The genetic code mapping of DNA base pair triplets thus specifies the primary sequence of amino-acids in any protein polymer. 
associated by translation is the process where messenger m-RNA is cross coded by Ribosomal agents and t-RNA into an amino-acid polymer.   with the 61 active triplets of the genetic code (DNA), a polymer composed of a chain of deoxy ribose sugars with purine or pyrimidine side chains.  DNA naturally forms into helical pairs with the side chains stacked in the center of the helix.  It is a natural form of schematic string.  The purines and pyrimidines couple so that AT and GC pairs make up the stackable items.  A code of triplets of base pairs (enabling 64 separate items to be named) has evolved which now redundantly represents each of the 20 amino-acids that are deployed into proteins, along with triplets representing the termination sequence.  Chemical modifications and histone binding (chromatin) allow cells to represent state directly on the DNA schema.  To cope with inconsistencies in the cell wide state second messenger and evolved amplification strategies are used. 


The limited set of triplets constrains mutational events to triplet deletions, triplet additions, codon, a DNA triplet that represents a specific amino-acid, or termination sequence of the genetic code.   replacements as well as missense replications.  So the mutational operator is well-defined and limited in scope. 

The 20 amino acids, deployed by the translation is the process where messenger m-RNA is cross coded by Ribosomal agents and t-RNA into an amino-acid polymer.   of the DNA (DNA), a polymer composed of a chain of deoxy ribose sugars with purine or pyrimidine side chains.  DNA naturally forms into helical pairs with the side chains stacked in the center of the helix.  It is a natural form of schematic string.  The purines and pyrimidines couple so that AT and GC pairs make up the stackable items.  A code of triplets of base pairs (enabling 64 separate items to be named) has evolved which now redundantly represents each of the 20 amino-acids that are deployed into proteins, along with triplets representing the termination sequence.  Chemical modifications and histone binding (chromatin) allow cells to represent state directly on the DNA schema.  To cope with inconsistencies in the cell wide state second messenger and evolved amplification strategies are used. 
schematic string, indirectly associate the plan with chemical, molecules obtain chemical properties from the atoms from which they are composed and from the environment in which they exist.  Being relatively small they are subject to phenomena which move them about, inducing collisions and possibly reactions with other molecules.  AWF's Smiley simulates a chemical environment including associating the 'molecule' like strings  with codelet based forces that allow the strings to react based on their component parts, sequence etc. 
and physical
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
.   The translation process is limited to building blocks that can be directly, or indirectly specified by the schematic string, and yet it extends the forces associated with the schemata more broadly. 
The resulting polypeptide, an intermediate length amino-acid polymer.  Longer lengths are termed proteins.   chains can interact with themselves, and other instances of polypeptides, as well as other environmental structures expressing these polarity, charge, size and shape phenomena. 

The competitive development of schematically defined cooperative structures with phenotypic value such as
This page reviews the strategy of setting up an arms race.  At its core this strategy depends on being able to alter, or take advantage of an alteration in, the genome or equivalent.  The situation is illustrated with examples from biology, high tech and politics. 
substrate complementary enzyme active sites
results directly from the genetic operations.  Each addition of new structure potentially extended the environment and schematically reachable possibilities. 

The presence of phenomenologically active building blocks that can be built up, with incorporated free energy, and torn down, with release of energy, by agents constructed from the same building blocks supports the efficient development of platforms, 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 chains
as described above
Strategic clustering
Inspecting the grab bag of mechanisms and how these are deployed so creatively, in biology, to leverage specific niche opportunities gains some credence from the recurrence of
This page looks at schematic structures and their uses.  It discusses a number of examples:
  • Schematic ideas are recombined in creativity. 
  • Similarly designers take ideas and rules about materials and components and combine them. 
  • Schematic Recipes help to standardize operations. 
  • Modular components are combined into strategies for use in business plans and business models. 

As a working example it presents part of the contents and schematic details from the Adaptive Web Framework (AWF)'s operational plan. 

Finally it includes a section presenting our formal representation of schematic goals. 
Each goal has a series of associated complex adaptive system (CAS) strategy strings. 
These goals plus strings are detailed for various chess and business examples. 
schematic goals
, such as:
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
,
This page discusses the effect of the network on the agents participating in a complex adaptive system (CAS).  Small world and scale free networks are considered. 
network effects
,
This page discusses the benefits of geographic clusters of agents and resources at the center of a complex adaptive system (CAS). 
geographic clustering
; at each level of emergent agent. 

While the structures, phenomena and mechanisms change the goals are often similar.  But the dynamics and partitioning used by real agents, challenges the scientist's techniques.  The use of computer models that reflect schematic cascades, partitioning and control of flows can support the understanding of real agent's operating strategies. 

The state space of real agents is represented in the aggregate schematic structures, schematic controls and deployed infrastructure controls.  Computer programmed models demand accurate control of the state system to function at all so they can help reject impossible scenarios from the alternatives scientists are struggling to rationalize.  The performance of real agents is also typically critical so
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. 
the management of state is constrained by the requirement to respond in a timely manner
.  Additionally real agents must
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. 
amplify significant signals
so that they ensure to transform the collective state representation of the agent. 

The presence of amplified second messengers, provide an amplified form of signals within a cell.  Since cells need to stabilize their overall state with many operations happening in parallel second messengers are useful in clarifying the signals effect.  The second messenger strategy is seen repeatedly in CAS including: neuro-transmitter guidance signals such as dopamine distribution in the brain, corporate positioning email messages in response to new situations, newspaper articles aligning the population;  provides this service.  However, it means that a specific agent must have its
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. 
deployed infrastructure pre allocated
to the required response of the initiating signal.  Hence multi agent systems have a multitude of similar infrastructure, from the evolved grab bag, configured for the specific responses particular agents are responsible for.  Aggregate analysis, ignoring the distributed deployment, just ends up with a bewildering collection of similar infrastructure apparently responding to the same second messengers. 

The schematic structures operations are also likely to be far more
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. 
flexible
than initially conceived by geneticists.  Inspection of the operation of a computer program, such as 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) test infrastructure
, which utilizes
This page discusses how Smiley provides deployment guarantees to its agent-based applications. 
Smiley's transaction services are reviewed. 
The complex interactions of codelets participating in a deployment cascade are discussed including: 
  • The implementation of schematic switches. 
  • The cooperative use of goal suppression.  
  • Evaluator codelets promotion of other siblings. 
Challenges of initiation of a cascade are discussed. 
Tools to associate transaction protection to an operon deployed codelet are described. 
Special support for sub-program codelets is described.  Completion of transactional sub-programs presents special challenges. 
Priority and synchronization support includes:
  • Delaying the operaton of the cascade sponsor. 
  • Delaying the notgcompleting cascade participant. 
  • Waiting for completion of parallel operations with the wait and relay service.  
The need to sustain resource pools is reviewed. 
The use of signals to coordinate siblings is described. 
The structural binding operon for the wait and relay service is included. 
The codelets and supporting functions are included.
schematic cascades
, partitioning and control of flows indicates that there is little benefit to sticking with one process.  The evolutionary grab bag (epi-genetic structures represent state surfaces within cells and eggs which can be operationally modified so as to provide a heritable structure.  DNA, histones and other stable structures provide surfaces where these states may be setup.  Egg carriers are in a particularly powerful position to induce epi-genetic changes.  Sapolsky notes [childhood] events which persistently alter brain structure and behavior via epi-genetic mechanisms including: pair-bonding in prairie voles, as they first mate, is supported by changes in oxytocin & vasopressin receptor gene regulation in the nucleus accumbens. 
, proteins, a relatively long chain (polymer) of peptides.  Shorter chains of peptides are termed polypeptides.  , structural domains, evolution conserves many useful structures including DNA base sequence (content) addressable binding regions, protein active sites and signal structures which can then be reused through the mutation genetic operator. 
, 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 other active structures) will be used when there is advantage in doing so. 

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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. 
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