Highlighting the emergent foundations of successful technology market strategies

Summary

Introduction

RSS vision

RSS mission

The real world is full of CAS

• A business plan - including constraining habits allow higher organisms: humans, rats, flies; to perform important behaviors automatically, without involvement of consciousness.  Habits are adaptive, being promoted by the release of dopamine into the PFC and striatum, generating a feeling of pleasure and conditioning us.  As the dopamine detaches from the synaptic receptors in the PFC and striatum the motivation to perform the behavior subsides.  If the dopamine remains at the synapse for an extended period, because it is not removed as occurs when cocaine is present, or when too much dopamine is generated, the habit can become an addiction.  , expected priorities of strategic alternative actions, committed predictions such as budgets, models of the
  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
  First describes the dynamic nature of any complex adaptive system (CAS). 
  
  It then introduces the broad effects of change which includes opportunities and risks/uncertainties. 
  
  As a CAS grows opportunities become undermined so they must be acted on quickly. 
  
  Uncertainties are also transformed and relayed by the dynamic network.  In particular the recombination of current and new ideas brought in from the network is discussed. 
  
  
  valued possibilities and activities,
  

• A production function - generating valuable products or services from supplies exchanged across a set of interfaces,
  
• 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. 
  
  control function safeguarding and validating the business plan and providing the infrastructure and constraints to support and shape the operation,
  
• A
  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. 
  
  network of connections to suppliers, customers and partners,
  

• 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,
  
• 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. 
  
  Modelers and 
• 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. 
  
  Actors required to select, constrain, transform and synchronize the
  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. 
  
  flows of resources and salaries, purchase requests,
  The drive to fulfill current customer requirements can result in the innovator's dilemma.  While the customer interest can diminish typical requirements databases continue to reflect the earlier desire. 
  
  Accurate modeling of the customer's roles and goals creates a more predictive indicator.  Close relationships with sentinel customers for key target segments help build the models. 
  
  Processes should also support the migration of product and customers to the winning architecture in a positive return market. 
  
  requirements and design ideas, calls for help, payments for goods & services offered produced and supplied etc. and the flows of responses.  As long as the flows through the system allow the emergence of
  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. 
  
  value captured as a sustained profit the business will persist. 

RSS frame notes - We 'frame' a list of html pages to aggregate them into a written 'picture'

• The
  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. 
  
  theory frame describes the fundamental attributes of CAS. 
• This vision frame examines essential aspects of successful business planning and practice using CAS theory and chess strategy to support the evaluations. 
• The
  This page introduces the programs that the Adaptive Web Framework (AWF) develops and uses to deploy Rob's Strategy Studio (RSS). 
  The programs are structured to obey complex adaptive system (CAS) principles.  That allows AWF to experiment and examine the effects. 
  A production program generates the web pages. 
  A testing system tests the production program.  It uses a framework to support the test programs.  This is AWF's agent programming framework as described in the agent-based programming presentation. 
  An example of the other AWF agent-based programs that are also described in the frame is the virtual robot. 
  Finally a strength, weaknesses, opportunities and threats assessment is presented. 
  
  Perl frame examines the strengths, weaknesses, opportunities and threats (
  The page describes the SWOT process.  That includes:
  

  • The classification of each event into strength weakness opportunity and threat.  
    
  • The clustering process for grouping the classified events into goals.  
    
  • How the clusters can support planning and execution. 
    

  Operational SWOT matrices and clusters from the Adaptive Web Framework (AWF) are included as examples. 
  
  SWOT) of a CAS approach to resolving the functional separation of specification, design, implementation, testing and operation of computer systems.  In this approach:

• Codelets operate on top of a physical representation of the rules of the application.  Random exploration of the environment by codelets can result in their deployment of
  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. 
  
  descriptive signals.  The buildup of physically active descriptors can increase the salience, Douglas Hofstadter controlled the amount of attention a Workspace object in Copycat would receive from codelets via its salience.  The more descriptions, analogous to geons, an object has and the more highly activated the nodes involved therein, the more important the object is.  Modulating this tendency is any relative lack of connections from the object to the rest of the objects in the Workspace.  Salience is a dynamic number that takes into account both these factors.  In Smiley the instantaneous salience of a Workspace's objects is calculated by itsalience.  In the brain salience is modeled by the salience networks. 
  of the local regions which become the focus of more concentrated activity. 
  
• '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.  ' changes such as bonding, there are different types of chemical bonds.  Ionic bonds stabilize two oppositely charged ionized atoms when the negative atom gives up an electron to the positively charged atom.  Covalent bonds stabilize the two reactants when they adopt a more stable structure with electrons shared between them.  Hydrogen bonds stabilize charged groups when they are surrounded by water molecules.  In Copycat codelets can bond two Workspace objects together.  In the Smiley implementation a bondbuilder adds a bonding descriptor to one of the objects bonding it to the other.   and
  This page describes the adaptive web framework (AWF) Smiley agent progamming infrastructure's codelet based Copycat grouping operation. 
  The requirements needed for a group to complete are described. 
  The association of group completion with a Slipnet defined operon is described.  Either actions or signals result from the association. 
  How a generated signal is transported to the nucleus of the cell and matched with an operon is described. 
  A match with an operon can result in deployment of a schematic string to the original Workspace.  But eventually the deployed string will be destroyed. 
  Smiley infrastructure amplification of the group completion operation is introduced.  This includes facilities to inhibit crowding out of offspring. 
  A test file awfart04 is included. 
  The group codelet and supporting functions are included. 
  
  grouping enacted by codelets can concentrate the
  This page describes the Copycat Slipnet. 
  The goal of the Slipnet is reviewed. 
  Smiley's specialized use of the Slipnet is introduced. 
  The initial Slipnet network used by the 'Merge Streams' and 'Virtual Robot' agent-based applications is setup in initchemistry and is included. 
  The Slipnet infrastructure and initialization functions are included. 
  
  associated physical forces creating associative changes in receptor, in biological cells these proteins are able to span the cell membrane and present an active site which is tailored to interact with a specific signal.  When the receptor pairs with its signal, its overall shape changes resulting in changes in the part internal to the cell which can be relayed by the cells signalling infrastructure.  In neuron synapses one type of receptor (fast) is associated with an ion channel.  The other (slow) is associated with a signalling enzyme chain and modulates the neuron's response. 
  complexes.  
  
• The receptors respond by signalling, is an emergent capability which is used by cooperating agents to support coordination & rival agents to support control and dominance.  In eukaryotic cells signalling is used extensively.  A signal interacts with the exposed region of a receptor molecule inducing it to change shape to an activated form.  Chains of enzymes interact with the activated receptor relaying, amplifying and responding to the signal to change the state of the cell.  Many of the signalling pathways pass through the nuclear membrane and interact with the DNA to change its state.  Enzymes sensitive to the changes induced in the DNA then start to operate generating actions including sending further signals.  Cell signalling is reviewed by Helmreich.  Signalling is a fundamental aspect of CAS theory and is discussed from the abstract CAS perspective in signals and sensors.  In AWF the eukaryotic signalling architecture has been abstracted in a codelet based implementation.  To be credible signals must be hard to fake.  To be effective they must be easily detected by the target recipient.  To be efficient they are low cost to produce and destroy. 
  nuclear, a membrane bounded module in a eukaryotic cell where the DNA is encapsulated and operated on. 
  schemata to initiate deployment of further active complexes into the local environment. 
  
• With the influence of
  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. 
  
  schematic plans, 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. 
  
  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 and
  This page describes the Smiley infrastructure that supports the associative binding of schematic strings to codelets defined in the Meta file and Slipnet. 
  The infrastructure supporting the associations is introduced. 
  The role of Jeff Hawkins neocortical attributes is discussed. 
  Relevant Slipnet configurations are included. 
  The codelets and supporting functions are included. 
  
  associative representations of the specification and state promoted by
  This page reviews the catalytic impact of infrastructure on the expression of phenotypic effects by an agent.  The infrastructure reduces the cost the agent must pay to perform the selected action.  The catalysis is enhanced by positive returns. 
  
  structural amplifiers CAS agents emerge and perform the testing of a computer program. 
• The
  This web frame includes a set of presentations about complex adaptive systems (CAS)
  
  presentation frame provides an alternative serial format for viewing the ideas discussed at RSS. 
  

• The
  We are products of complexity, but our evolution has focused our understanding on the situation of hunter gatherers on the African savanna.  As humanity has become more powerful we can significantly impact the systems we depend on.  But we struggle to comprehend them.  So this web frame explores significant real world complex adaptive systems (CAS):
  

  • Assumptions of randomness & equilibrium allowed the wealthy & powerful to expand the size and leverage of stock markets, by placing at risk the insurance and retirement savings of the working class.  The assumptions are wrong but remain entrenched. 
    
  • The US nation was built from two divergent political views of: Jefferson and Hamilton.  It also reflects the development of competing ancient ideas of Epicurus and Cyril.  But the collapse of Bretton Woods forced Wall Street into a position of power, while the middle and working class were abandoned by the elites.  Housing financed with cash from oil and derivative transactions helped hide the shift. 
    
  • Most US health care is still operating the way cars built in the 1940s did.  Geisinger is an example of better solution.  But transforming the whole network is a challenge.  And public health investment has proved far more beneficial.  
  • Helping our children learn to be effective adults is part of our humanity, but we have created a robust but deeply flawed education system.  Better alternatives have emerged.  
  • Spoken language, reading and writing emerged allowing our good ideas to become a second genetic material. 
    
  • The emergence of the global economy in the 1600s and its subsequent development; 

  It explains how the examples relate to each other, why we all have trouble effectively comprehending these systems and explains how our inexperience with CAS can lead to catastrophe.  It outlines the items we see as key to the system and why. 
  
  
  Example systems,
  
• This page introduces a series of asymmetries which encourage different strategic approaches.   
  The differences found in business, sexual selection, gamete structure, as well as in chess encourage escalations in the interactions. 
  And yet the systems including these asymmetries can be quite stable. 
  
  Asymmetries and
  
• In this page we:
  

  • Introduce some current problems of complexity for humanity
    
  • Explain how to use the site. 

  There are two main starting points:
  

  1. The example systems frame and
     
  2. The presentation frame. 
     
  3. And then there is how we use the site. 

  The site uses lots of click through.  That's so that you can see the underlying principles that are contributing to the system being discussed.  We hope that as you internalize and reflect on the principles the system should appear in a new light.  At this site clicking is good!
  
  Opportunities frames
  

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