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Strategic theory

Summary
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
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent
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

John Holland's framework for representing 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
is outlined.  Links to other key aspects of CAS theory discussed at the site are presented. 
Introduction
If you are a strategy innovator who likes to frame a plan of action within a theoretical context then this web frame is focused on you.  If not we have a
This presentation introduces complex adaptive systems (CAS) looking at their components, structure and properties. 
presentation
on this theory. 

The
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. 

human mind
developed to take advantage of 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.  Pinker explains how strategic thinking supported capturing of niches previously owned by
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
evolved
specialists. 
To evaluate the strategies,
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
models
are setup by the brain including gut feel

Strategy is enabled by
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
evolution
leveraging a billion years of
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

time
.  But strategy is an
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent
functionalist regularity operating on a platform of the
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. 

human brain
.  This allows strategy to have
Matt Ridley demonstrates the creative effect of man on the World. He highlights:
  • A list of preconditions resulting in
  • Additional niche capture & more free time 
  • Building a network to interconnect memes processes & tools which
  • Enabling inter-generational transfers
  • Innovations that help reduce environmental stress even as they leverage fossil fuels

impacts far more rapidly
than the evolutionary mechanism. 

We partition the real world into three parts: physical, 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 complex.  The chemical part builds on the physical foundation.  The complex part builds on the chemical foundation.  We consider a system is complex if it operates 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. 
plans and strategies
executed by
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent
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
s.  We presume that you are working within a complex adaptive
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
, and so we will leverage
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
model
s of such complex adaptive systems (CAS). 

When you are applying strategy to complex adaptive systems, Holland (1) describes a general framework:


Formulating strategy in the dynamically changing area at the edge of a CAS can be performed effectively with an
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 associative
process in which you identify potential actions and propose
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 results of these, do a bit of the likely key aspects, and then check the results against the models, iterating round a "
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. 
plan, do, check, act
" cycle.  This helps
This page discusses the interdependence of perception and representation in a complex adaptive system (CAS).  Hofstadter and Mitchell's research with Copycat is reviewed. 
capture representations of valuable perceptions
of the problem area and limits
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

time
spent on other aspects. 

The result should be an understanding of the system, its structure, operating principles,
This page reviews the inhibiting effect of the value delivery system on the expression of new phenotypic effects within an agent. 
alignment
and dynamics. 

Adaptive systems respond to stimuli and can look chaotic provides an explanation for the apparently random period between water droplets falling from a tap.  Typically the model of the system is poor and so the data captured about the system looks unpredictable - chaotic.  With a better model the system's operation can be explained with standard physical principles.  Hence chaos as defined here is different from complexity.   to an observer without a
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
representative model
.  With an effective model consequences of the adaptive processes can be used as predictors of future states. 

Where to start?  It would seem logical to start at the goal - but that requires some domain knowledge.  A good place to spend some time initially is
Strategy gives way to tactics.  If you your company or other emergent system collapse there is no further possibility of strategic action.  This page discusses the importance of sustaining the base of operations to support subsequent strategic action. 
protecting
the
This page discusses the strategy of modularity in a complex adaptive system (CAS).  The benefits, mechanism and its emergence are discussed. 
replicator
(Richard Dawkin's term for the emergent phenotype is the system that results from the controlled expression of the genes.  It is typically represented by a bacterial cell or the body of a multi-cell animal or plant.  The point is that the genes provide the control surface and the abstract recipe that has been used to generate the cell.   like a business, army etc. ) from imminent problems (such as removal of a critical supply) and looking in depth at current and potential markets, channels and competitors. 

A CAS involves interacting agents, linked in one or more
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


Each agent contains
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. 
active structures
that perform transformations.  One class of active structure,
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
, responds to specific events, signals, generated by the system and its environment. 

The broadness of the areas that must be understood is discouraging.  However, the early chaos becomes an organizing principle when the small set of rules is applied in an iterative process, and methodologies for clustering the information identified, are used. 
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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
| Design |
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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