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Modeling emergent systems

Summary
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
in complex adaptive systems (
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
) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
Introduction
A core problem for any
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
is how to learn to differentiate strategies that are effective from those that are not.  Since agents act via
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent
phenomena and epiphenomena
Russ Abbott explores the impact on science of epiphenomena and the emergence of agents. 
even the rule sets they obey are not grounded in the core physics
.  As such it is unlikely that theories of operation will be accurate.  Instead approaches accept approximation and then leverage 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
effects of the results of real actions.  Adaptive systems use two such mechanisms.  One
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
Darwinian evolution
is fundamental and starts with no predefined plan.  The other, Samuel's method, enabled by evolution can formalize goal based plans. 

Arthur Samuel defined an approach to modeling based on a weighted valuation of predicted actions. 

Samuel confronted a number of problems:
Samuel reasoned that features of the environment in particular situations can be associated with sets of strategies used to achieve a goal.  The limited sensors of the agent can induce an emergent 'feature' categorization of each new circumstance, and so identify sub-goal sequences. 

His solution included an abstraction of an opponent using the best predicted results of the agents own models.  Feedback was obtained when the results of real actions were out of step with the predictions.  By adjusting the values of the predictors in light of the mismatch a learning feedback loop was induced. 

Samuel argued that predictions were likely to be more accurate towards the end of a goal or sub-goal.  Indeed when the goal has been completed it is known how successful the strategies adopted have been.  Hence Samuel adjusted earlier predictions moving them towards later values.  Significantly however, he only adjusted them by small increments, since the predictions are based on limited samples, and are unlikely to match reality.  The iterative accumulation of small increments represents an average of a feature over its various significant situations. 

Since traps offer early success to induce actions that lead to subsequent failure it is also necessary to offset the positive predictions with negative predictions of the specific trap situations.  Such representations are often implemented as a default hierarchy. 

This page introduces the complex adaptive system (CAS) theory frame.  The theory is positioned relative to the natural sciences.  It catalogs the laws and strategies which underpin the operation of systems that are based on the interaction of emergent agents. 
John Holland's framework for representing complexity is outlined.  Links to other key aspects of CAS theory discussed at the site are presented. 
Complex adaptive system
(CAS)
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
have further issues based on the changing nature of the systems they operate within.  With the right mechanisms they can even benefit overtime from natural selection's effects on the models.  For that to be true the models must be
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent


An approach to such modeling is to leverage two aspects of CASs:
  1. 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
    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. 
    state
    &
    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. 
    flow control
    based representations - of the models and actions. 
  2. 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. 
    prioritized parallel
    operation of
    Plans are interpreted and implemented by agents.  This page discusses the properties of agents in a complex adaptive system (CAS). 
    It then presents examples of agents in different CAS.  The examples include a computer program where modeling and actions are performed by software agents.  These software agents are aggregates. 
    The participation of agents in flows is introduced and some implications of this are outlined. 
    agents

With parallel operation of agents many alternative model and action processes can be scheduled for execution.  The various agents in effect respond to system feedback both from the environment and their own schemata in different ways allowing for competing approaches to be tested by their results. 

Biological 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 are supported by glial cells.  Neurons include a:
  • Receptive element - dendrites
  • Transmitting element - axon and synaptic terminals 
networks, a network of interconnected neurons which perform signalling, modeling and control functions.  In Cajal's basic neural circuits the signalling is unidirectional.  He identified three classes of neurons in the circuits:
  • Sensory, Interneurons, Motor; which are biochemically distinct and suffer different disease states. 
use changes in the strengths of the active mediating synapses, a neuron structure which provides a junction with other neurons.  It generates signal molecules, either excitatory or inhibitory, which are kept in vesicles until the synapse is stimulated when the signal molecules are released across the synaptic cleft from the neuron.  The provisioning of synapses is under genetic control and is part of long term memory formation as identified by Eric Kandel.  Modulation signals (from slow receptors) initiate the synaptic strengthening which occurs in memory. 
to represent learned associations.  The sensory deconstruction of the real world, and evolved models of responses associated with them, generates the
This page discusses the tagging of signals in a complex adaptive system (CAS).  Tagged signals can be used to control filtering of an event stream.  Examples of CAS filters are reviewed. 
tag filtering
that identifies the set of activated neurons in which the synaptic weightings are adjusted. 

When models are defined as schematic representations of value functions
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
evolution
can act, over generations, on the schema enabling emergence of new models. 


Psychologists note that modeling the world is an open-ended problem.  The implications of any conclusion can be applied broadly.  But instead the mind is able to focus its analysis rapidly onto the probem it is dealing with.  Identifying just the relevant implications appears subjectively effortless which hides the underlying difficulty of this frame problem describes the difficulty of accurately representing changes over time in dynamic systems without a combinatorial explosion of constraints.  It was highlighted by the artificial intelligence pioneers John McCarthy and Patrick Hayes in 1969.  They noted that using situation calculus to formally describe changes in the situation requires not only details of the changed actions, but also a frame axiom for every pair of action and conditions so that the action does not affect the condition. 
.  In later chapters of
Computational theory of the mind and evolutionary psychology provide Steven Pinker with a framework on which to develop his psychological arguments about the mind and its relationship to the brain.  Humans captured a cognitive niche by natural selection 'building out' specialized aspects of their bodies and brains resulting in a system of mental organs we call the mind. 

He garnishes and defends the framework with findings from psychology regarding: The visual system - an example of natural selections solutions to the sensory challenges of inverse modeling of our environment; Intensions - where he highlights the challenges of hunter gatherers - making sense of the objects they perceive and predicting what they imply and natural selections powerful solutions; Emotions - which Pinker argues are essential to human prioritizing and decision making; Relationships - natural selection's strategies for coping with the most dangerous competitors, other people.  He helps us understand marriage, friendships and war. 

These conclusions allow him to understand the development and maintenance of higher callings: Art, Music, Literature, Humor, Religion, & Philosophy; and develop a position on the meaning of life. 

Complex adaptive system (CAS) modeling allows RSS to frame Pinker's arguments within humanity's current situation, induced by powerful evolved amplifiers: Globalization, Cliodynamics, The green revolution and resource bottlenecks; melding his powerful predictions of the drivers of human behavior with system wide constraints.  The implications are discussed. 

How The Mind Works
, Steven Pinker shows how
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
natural selection
has used the specifics of: the African savanna is the environment where hunter-gatherers primarily evolved.  Its grassland supported large herbivores that could be hunted.  Clumps of trees & rocks supported places to hide from large carnivores.  Streams and paths add to the signals enabling orientation. 
, the cognitive niche is Tooby & DeVore's theory that reflects a flexible competitive strategy, described by Steven Pinker, which leverages the power and flexibility of intelligence to defeat the capabilities of genetically evolved specialists focused on specific niches.   accessed by humans, the architecture of conscious access is, argues Stanislas Dehaene, when some attended information eventually enters our awareness and becomes reportable to others. 
; to limit the frame problem's combinatorial explosion


If the environment contains situations that are not modeled by an agent the situation will act as a
This page discusses the methods of avoiding traps.  Genetic selection and learning to avoid traps are reviewed. 
trap
, which will select against this agent and its schemata driving evolution forward.  Alternatively the agent can include a mechanism to create new models, and/or have a model that detects failure of its other models and the success of which results in an action that moves the agent away from the trap.  The brain's dopamine circuit acts in this way. 
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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. 
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