Traps
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Traps and avoiding them

Summary
This page discusses the methods of avoiding traps.  Genetic selection and learning to avoid traps are reviewed. 
Introduction
Any interaction by an
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 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. 
environment
, or other agents, has the potential to advance towards a problematic situation that would be better avoided.  Sometimes the path is selected because the short term feedback is positive.  Such a situation is termed a trap. 

Available methods of avoiding traps include:
  1. Genetic
    This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
    selection
    ,  and
  2. Forward prediction based on competitive
    The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
    modeling and adaptive learning

Some scenarios are just too costly for each individual in a population to support the defenses required to avoid the trap.  A well-known biological example is the response of prey to the lure of the angler fish.  It appears possible for the prey fish to develop a sense that they are approaching a trap - but the percentage of the prey population that is trapped is small, any change in the prey would also have to cope with likely responses in the angler fish population, and the change may have a cost in terms of diverting investment from other aspects of the prey's fitness.
Genetic selection of trap avoidance
In a collection of
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) agent's variation and selection provide a mechanism for developing avoidance at the population level.  The sub-set of the population that succumbs to the trap will be selected against.  Strategies based on this process require the development of a stable of alternatives which are subjected to selection.  Scientific research networks can be seen to work in this way.  Political leaders, Multi-business corporations and venture capital is venture capital
strategies also leverage the principle. 

Adaptive learning to avoid traps
Alternatively by observation of past traps, a CAS agent can develop a model of high risk scenarios that have led to the traps.  It's not a trivial exercise, given the need to:
  • Identify the key events that lead to the trap.
  • Attribute causal effects to the sequence of events and actions,
  • Accurately define the actions, including responses, of other agents;
All without a clear awareness of the correctness of the model. 

In relatively simple CAS environments, such as Chess, collection of the sequences and results of vast numbers of games provides a mechanism to improve models and gain deep understanding, of what a particular position implies. 

More generally the likelihood of never repeated strategic accidents implies no fool proof adaptive method. 

Given the uncertainty is when a factor is hard to measure because it is dependent on many interconnected agents and may be affected by infrastructure and evolved amplifiers.  This is different from Risk.   of failure in trap avoidance those who can, should use position to support their use of genetic selection and leverage the results of traps directly and indirectly.  
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This page looks at schematic structures and their uses.  It discusses a number of examples:
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Each goal has a series of associated complex adaptive system (CAS) strategy strings. 
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  • A business can gain focus from targeting key customers,
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  • A program approach can ensure strategic alignment. 
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