Many states
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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Vast numbers of potential states enable emergence

Summary
This page discusses the potential of the vast state space which supports the
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergence
of 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
).  Kauffman describes the mechanism by which the system expands across the space. 
Introduction
As the
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. 
physical rules
are instantiated different states will define
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. 
the system
within the environment. 

When the total set of states that can be created by different combinations of the rules being instantiated is very large it is possible for new phenomena to appear as novel states become present. 

Particular combinations may be unlikely, but the key is that the rules allow the potential for them to exist. 

The adjacent possible
Stuart Kauffman focuses on the existing set of actual combinations of organic molecular reactants and the set of adjacent points in the graph of possible combinations of organic molecular products. 

He argues that the Universe is kinetically trapped, unable to make all the products theoretically possible in the limited time available.  The Universe is non-ergodic. 

He argues there is an indefinite hierarchy of upward 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 
, of chemical, morphological, behavioral levels and beyond. 

When certain aspects of the system are able to change the likelihood of appearance of specific combinations the system gains the potential to be adaptive. 

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
can leverage the system's ability to partition across self-assembling, semi-permeable,
Barriers are particular types of constraints on flows.  They can enforce separation of a network of agents allowing evolution to build diversity.  Examples of different types of barriers and their effects are described. 
barriers
to induce
This page discusses the strategy of modularity in a complex adaptive system (CAS).  The benefits, mechanism and its emergence are discussed. 
modules
with separate sets of states.  This property allows the agents to develop state based
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
models
and
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 the states, via flows
, they operate within. 

The myriad states that can be created from the rules is fundamental to a system being
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent
.  Note this implies that the environment includes physical entities and emergent entities. 
























































































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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. 
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