Frozen accidents
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Chance events become initiators of emergence

Summary
This page discusses the impact of random events which once they occur encourage a particular direction forward for a complex adaptive system (
This page introduces the complex adaptive system (CAS) theory frame.  The theory provides an organizing framework that is used by 'life.'  It can illuminate and clarify complex situations and be applied flexibly.  It can be used to evaluate and rank models that claim to describe our perceived reality.  It catalogs the laws and strategies which underpin the operation of systems that are based on the interaction of emergent agents.  It highlights the constraints that shape CAS and so predicts their form.  A proposal that does not conform is wrong. 

John Holland's framework for representing complexity is outlined.  Links to other key aspects of CAS theory discussed at the site are presented. 
CAS
). 
Introduction
The
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
in adaptive in evolutionary biology is a trait that increased the number of surviving offspring in an organism's ancestral lineage.  Holland argues: complex adaptive systems (CAS) adapt due to the influence of schematic strings on agents.  Evolution indicates fitness when an organism survives and reproduces.  For his genetic algorithm, Holland separated the adaptive process into credit assignment and rule discovery.  He assigned a strength to each of the rules (alternate hypothesis) used by his artificial agents, by credit assignment - each accepted message being paid for by the recipient, increasing the sender agent's rule's strength (implicit modeling) and reducing the recipient's.  When an agent achieved an explicit goal they obtained a final reward.  Rule discovery used the genetic algorithm to select strong rule schemas from a pair of agents to be included in the next generation, with crossing over and mutation applied, and the resulting schematic strategies used to replace weaker schemas.  The crossing over genetic operator is unlikely to break up a short schematic sequence that provides a building block retained because of its 'fitness';  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. 
systems react to a '
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. 
stream
' of events.  Sometimes the response of the adaptive system is
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. 
emergent
.  The event becomes a foundation for the future direction of development of the system. 

Through chance, events compete for the direction that the system will take.  Depending on the order of events the system will adapt differently.  A 'frozen accident' in effect sets the direction of the system. 

A frozen accident initially referred to quantum mechanical extends mechanics to atomic and subatomic scales.  Energy, momentum, angular momentum are all restricted to quantized values and all objects have particle and wave properties.  There are various ways to understand quantum mechanics including the cascade of ideas initiated by Hugh Everett. 
events providing a far greater contribution to the future direction than the fundamental rules of a
This page introduces the complex adaptive system (CAS) theory frame.  The theory provides an organizing framework that is used by 'life.'  It can illuminate and clarify complex situations and be applied flexibly.  It can be used to evaluate and rank models that claim to describe our perceived reality.  It catalogs the laws and strategies which underpin the operation of systems that are based on the interaction of emergent agents.  It highlights the constraints that shape CAS and so predicts their form.  A proposal that does not conform is wrong. 

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)


Each quantum mechanical alternate history was a theoretical physicist, working at the Pentagon's Weapons Systems Evaluation Group, who developed the current approach to quantum mechanics.  This included introducing many alternative histories of the universe, which were considered alike by the theory except for their different probabilities.  The approach was used by theoretical physicist, Murray Gell-Mann and theoretical cosmologist, James Hartle, to clarify how quantum mechanics should be conceived, particularly in relation to the quasiclassical domain.  Richard Feynman formulated quantum mechanics in terms of Everett's histories. 
of the universe depends on the results of an inconceivably large number of accidents.  The accidents have chance outcomes as required by quantum mechanics.  The effective complexity, M. Mitchell Waldrop describes a vision of complexity via:
  • Rich interactions that allow a system to undergo spontaneous self-organization and, for CAS, evolution
  • Systems that are adaptive
  • More predictability than chaotic systems by bringing order and chaos into
  • Balance at the edge of chaos
of the universe receives only a small contribution from 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. 
fundamental laws
.  The rest comes from the numerous regularities resulting from frozen accidents.  These are chance events to which the particular outcomes have a multiplicity of long-term consequences, all related by their common ancestry. 

A specific class of frozen accidents
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
Darwinian pre-adaptations
, are incidental features of an adaptive
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
with no selection significance in one environment which turn out to have selective significance in another environment. 

The chance nature of frozen accidents means that the progressive adaptation in evolutionary biology is a trait that increased the number of surviving offspring in an organism's ancestral lineage.  Holland argues: complex adaptive systems (CAS) adapt due to the influence of schematic strings on agents.  Evolution indicates fitness when an organism survives and reproduces.  For his genetic algorithm, Holland separated the adaptive process into credit assignment and rule discovery.  He assigned a strength to each of the rules (alternate hypothesis) used by his artificial agents, by credit assignment - each accepted message being paid for by the recipient, increasing the sender agent's rule's strength (implicit modeling) and reducing the recipient's.  When an agent achieved an explicit goal they obtained a final reward.  Rule discovery used the genetic algorithm to select strong rule schemas from a pair of agents to be included in the next generation, with crossing over and mutation applied, and the resulting schematic strategies used to replace weaker schemas.  The crossing over genetic operator is unlikely to break up a short schematic sequence that provides a building block retained because of its 'fitness';  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. 
of a CAS is highly specific.  It is the realized outcome of a huge number of potential alternative combinations that de-cohered as course-grained, averaged, histories. 

The effect of frozen accidents limits the usefulness of
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Evolution's schematic operators and Samuel modeling together support the indirect recording of past successes and their strategic use by the current agent to learn how to succeed in the proximate environment. 
models
that attempt to represent all outcomes, to characterizing those systems that have a very limited set of combinations.  For example in integrated circuit design the use of
This page discusses the strategy of modularity in a complex adaptive system (CAS).  The benefits, mechanism and its emergence are discussed. 
design rules
that require designs to use rectangular layouts etc. reduces the combinatorial explosion. 
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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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