Agent based flows
This page describes the organizational forces that limit change.  It explains how to overcome them when necessary. 

Power& tradition holding back progress
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  • 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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The constraints are described. 
The techniques to overcome them are implied. 
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Models of agent-based flows

This web page reviews opportunities to benefit from modeling agent based flows 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. 
) theory. 
John Holland's strategic direction towards a theory of complexity, M. Mitchell Waldrop describes a vision of complexity via:
  • Rich interactions that allow a system to undergo spontaneous self-organization
  • Systems that are adaptive
  • More predictability than chaotic systems by bringing order and chaos into
  • Balance at the edge of chaos
described in Hidden Order was two parts:
  1. 'The lower tier concerns itself with the flow of resources between agents of different kinds.  The combination of rapid mixing within each kind, and random contact between kinds, makes possible a mathematical model much like the billiard ball model discussed in the first chapter.  That is we can treat each kind of agent as a kind of billiard ball, and for each pair we can determine a reaction rate.  The rate is directly determined by the exchange condition and the exchange scoring mechanism specified for each agent in Echo. ... The result is an array of reaction rates.  Once this array has been computed, we are close to having a mathematical model that describes changes in flow over
    Carlo Rovelli resolves the paradox of time. 
    Rovelli initially explains that low level physics does not include time:
    • A present that is common throughout the universe does not exist
    • Events are only partially ordered.  The present is localized
    • The difference between past and future is not foundational.  It occurs because of state that through our blurring appears particular to us
    • Time passes at different speeds dependent on where we are and how fast we travel
    • Time's rhythms are due to the gravitational field
    • Our quantized physics shows neither space nor time, just processes transforming physical variables. 
    • Fundamentally there is no time.  The basic equations evolve together with events, not things 
    Then he explains how in a physical world without time its perception can emerge:
    • Our familiar time emerges
      • Our interaction with the world is partial, blurred, quantum indeterminate
      • The ignorance determines the existence of thermal time and entropy that quantifies our uncertainty
      • Directionality of time is real but perspectival.  The entropy of the world in relation to us increases with our thermal time.  The growth of entropy distinguishes past from future: resulting in traces and memories
      • Each human is a unified being because: we reflect the world, we formed an image of a unified entity by interacting with our kind, and because of the perspective of memory
      • The variable time: is one of the variables of the gravitational field.  With our scale we don't register quantum fluctuations, making space-time appear determined.  At our speed we don't perceive differences in time of different clocks, so we experience a single time: universal, uniform, ordered; which is helpful to our decisions

    .  ... To adopt a term from physics, the lower tier gives us a mathematical model of the fast dynamics of the system.'
  2. 'For a mathematical theory of [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. 
    to be effective, the fast dynamics of the flows must be successfully coupled to the slow dynamics of long-term adaptation in evolutionary biology is a trait that increased the number of surviving offspring in an organism's ancestral lineage.  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. 
    This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
    .  In the two-tiered model, it is the upper tier that specifies the evolution of the agents.  It uses a genetic algorithm to change the structures of offspring. ...'
Its a vision that is inspiring in its elegance and seeming simplicity. 
The real world has many examples 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. 
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. 
.  But Holland was striving to gain clarity and mathematical rigor by discarding the peripheral details.  
Still to
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. 
the agent-based
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. 
must be processed by
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. 
defined operations, that in aggregate represent the agent, so that the reproductive success will be reflected in persistence of the operation.  As such each agent has a choice of how to enable the operation.  But it is constrained by the type 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. 
it needs to process.  It can:

In each case signals can be sent between agents allowing for sensors to initiate synchronized adaptive operations. 

The agent must be able to sustain the force that drives its operations in the required direction.  That also creates requirements to move the store of energy to the desired operating
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. 

When a structurally enhanced network of agents is required it is necessary to schematically define its topology, and the capabilities of each agent in the network as well as the 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. 
of the network over time. 
Further it becomes necessary for the schematic models used by the agents to be represented effectively in the structure and operations of the agent network. 
This requirement for
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. 
representational versatility
suggests a significant dependence on a flexible building block like the carbon atom, or
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. 
objec 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. 
and its list structures and functions.  This represents a situation seen repeatedly: The capability to control the network flows, including the
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. 
of operations, induces strategic differences which can also become represented in the schematic structures.  The epigenetic controls 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. 
managed and made heritable through the
This page discusses the strategy of modularity in a complex adaptive system (CAS).  The benefits, mechanism and its emergence are discussed. 
integration within a 'new'
This page reviews the implications of reproduction initially generating a single child cell.  The mechanism and resulting strategic options are discussed. 
are then amplified and sustained in 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.   stem cell is a biological cell which is partly or wholly undifferentiated.  A totipotent cell can generate a complete embryo and placenta.  Embryos include pluripotent cells which can generate any tissue in the body.  Adult humans' cells have turned off this ability but still include multipotent stem cells that differentiate into multiple cell types.   Typically a cell's local environment will have the signals required for it to obtain context and differentiate appropriately.  This will include both the external environment and the internal state of the cell which has replicated from a parent and obtained its epi-genetic state.   So introduction of undifferentiated stem cells into an injured area is not likely to have either aspect of the environment suitable.  Consequently development is aiming to encourage differentiation to progenitor cells for the damaged region.  This requires delivering the cells to the appropriate part of the body.  To avoid rejection by the immune system techniques aim to use cell lines developed from the patient's cells.  The techniques to generate these cell lines include: SCNT, iPS.  Possible mechanisms of stem cell therapy are: Generation of new differentiated cells, Stimulation of growth of new blood vessels to repopulate damaged regions, Secretion of growth factors, Treatment of diabetes (1 and 2) with addition of pancreatic cells, Assistance of other mechanisms; lines.  This seems logically distinct from the experimental attempts to deploy Yamanaka genes is somatic cell nuclear transfer where a mature cell's nucleus is inserted into an egg cell.  This allows generation of totipotent stem cell lines.  Its effect can be achieved with the Yamanaka genes: Oct3/4, Sox2, Klf4, c-Myc; which are highly expressed in embryonic stem cells.  Initially SCNT based cloning was found to be unsafe with the cloned organisms having serious health problems.  But a shift to retroviral-mediated expression of mouse fibroblasts selected for the gene NANOG resulted in iPSCs that function identically to embryonic stem cells.  Although Dolly the cloned sheep died young her 'sisters' lived long, apparently healthy lives.  It turned out that most young cloned higher animals have problems but those that live longer are fine and it is not known why. 
to roll back state changes in 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.   structures (Dec 2016). 
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
demonstrates how emergence and the challenges of real world, real time decisions and actions shape the architectures of the agents and concentrates the strategic goals into a constrained set.  From billiard ball like operations
This page introduces a series of asymmetries which encourage different strategic approaches.   
The differences found in business, sexual selection, gamete structure, as well as in chess encourage escalations in the interactions. 
And yet the systems including these asymmetries can be quite stable. 
This page reviews the inhibiting effect of the value delivery system on the expression of new phenotypic effects within an agent. 
strategic alignment
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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. 
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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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