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.  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. 
    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 is, according to Dawkins, a suitcase word with at least five meanings in biology:
  1. Darwin and Wallace thought in terms of the capacity to survive and reproduce, but they were considering discrete aspects such as chewing grass - where hard enamel would improve the relative fitness. 
  2. Population geneticists: Ronald Fisher, Sewall Wright, J.B.S. Haldane; consider selection at a locus where for a genotype: green eyes vs blue eyes; one with higher fitness can be identified from genotypic frequencies and gene frequencies, with all other variations averaged out. 
  3. Whole organism 'integrated' fitness.  Dawkins notes there is only ever one instance of a specific organism.  Being unique, comparing the relative success of its offspring makes little sense.  Over a huge number of generations the individual is likely to have provided a contribution to everyone in the pool or no one. 
  4. Inclusive fitness, where according to Hamilton, fitness depends on an organism's actions or effects on its children or its relative's children, a model where natural selection favors organs and behaviors that cause the individual's genes to be passed on.  It is easy to mistakenly count an offspring in multiple relative's fitness assessments. 
  5. Personal fitness represents the effects a person's relatives have on the individual's fitness [3].  When interpreted correctly fitness [4] and fitness [5] are the same. 
.
Genetic selection of trap avoidance
In a collection of
This page introduces the complex adaptive system (CAS) theory frame.  The theory provides an organizing framework that is used by 'life.'  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) 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 aims to develop plans and strategies which ensure effective coordination to improve the common good of the in-group.  John Adair developed a leadership methodology based on the three-circles model. 
, Multi-business corporations and venture capital is venture capital, venture companies invest in startups with intangable assets
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 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. 
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, although the two are deliberately conflated by ERISA.  Keynes argued that most aspects of the future are uncertain, at best represented by ordinal probabilities, and often only by capricious hope for future innovation, fear inducing expectations of limited confidence, which evolutionary psychology implies is based on the demands of our hunter gatherer past.  Deacon notes reduced uncertainty equates to information. 
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:
  • 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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  • 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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