This page describes the organizational forces that limit change. It explains how to overcome them when necessary.
This page uses an example to illustrate how:
This page uses the example of HP's printer organization freeing itself from its organizational constraints to sell a printer targeted at the IBM pc user.
The constraints are described.
The techniques to overcome them are implied.
Effective search techniques
SummaryThe page discusses the search dilemma. It describes how evolution solves the dilemma. It introduces some problems that impact non evolutionary searches and suggests some strategies for such situations.
IntroductionIt's fairly apparent that an armor plated racing car has a limited market. Its performance function is poor. However, validity of other cases of
This page looks at schematic structures and their uses. It discusses a number of examples:creative combinations of ideas and architectures are harder to judge intuitively. The assessment assumes a reasonable knowledge of the area. The knowledge of a technology domain is typically contained in the theory, practices and processes of a
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.
This page discusses the benefits of geographic clusters of agents and resources at the center of a complex adaptive system (CAS).geographic cluster of companies and academic institutions.
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.theory is particularly relevant to search. John Holland describes a dilemma - The set of possible structures to be included could be very large and the performance functions could involve many local maxima. How can one avoid a prolonged search? The search should go on while improvements are possible, but it must be possible to exploit possibilities for improved performance while the search proceeds, since unexplored possibilities may hold the key to optimal performance. The adaptive system must persistently test and incorporate structural properties associated with better performance. It is necessary for the useful properties to be identified if they are to be exploited.
John Holland's framework for representing complexity is outlined. Links to other key aspects of CAS theory discussed at the site are presented.
Holland showed that
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS). The mechanism and its emergence are discussed.evolution can optimize parallelized competitive activities that both explore and test, as long as there is a smooth correlated
This web page reviews opportunities to find and capture new niches based on studying fitness landscapes using complex adaptive system (CAS) theory.fitness landscape (Kauffman). Holland's
Plans change in complex adaptive systems (CAS) due to the action of genetic operations such as mutation, splitting and recombination. The nature of the operations is described.genetic operators leverage this evolutionary mechanism.
This page discusses the interdependence of perception and representation in a complex adaptive system (CAS). Hofstadter and Mitchell's research with Copycat is reviewed. The bridging of a node from a network of 'well known' percepts to a new representational instance is discussed as it occurs in biochemistry, in consciousness and abstractly.Perception and representation architectures are also designed to leverage discoveries into a parallel and prioritized search process.
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS). The mechanism and its emergence are discussed.Evolution can use
This page describes the consequences of the asymmetries caused by genotypic traits creating a phenotypic signal in males and selection activity in the female - sexual selection.sexual selection,
The impact of this asymmetry is to create a powerful alternative to natural selection with sexual selection's leverage of positive returns. The mechanisms are described.
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.emergence constraints, allelic, one of multiple alternative forms of a schematic sequence with the same address on a schematic string. matching, and
This page reviews the inhibiting effect of the value delivery system on the expression of new phenotypic effects within an agent.extended phenotypic alignment to help improve its success in recombination. Indeed to ensure some adaptability to changing circumstance epistatic alleles seem necessary. Understand the
Tools and the businesses that produce them have evolved dramatically. W Brian Arthur shows how this occurred.evolution of technologies and hence what is possible in your local
This web page reviews opportunities to find and capture new niches based on studying fitness landscapes using complex adaptive system (CAS) theory.environment.
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it. Samuel modeling is described as an approach.model based strategies the uncertainties 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. are higher. New areas take
Carlo Rovelli resolves the paradox of time.time to understand. How much time should an investigation spend looking outside its current area of expertise and how do you know if the search has been effective? In general the search dilemma shows itself in the difficulty of effectively allocating pure and applied research resources. Remaining within the area associated with the local research cluster risks over valuing phenotypic alignment. It seems best to ensure researchers can move around easily within the cluster, limiting these risks. At the same time novel search strategies should be included to capture newly evolved activities. The goal must be to avoid evolution promoting an alternate cluster with
Rovelli initially explains that low level physics does not include time:
This page reviews Christensen's disruption of a complex adaptive system (CAS). The mechanism is discussed with examples from biology and business.disruptive consequences.
The value of the proprietary research also depends on how effectively its conclusions can be integrated into the company's product development process.
Integrating workflow, in business processes define the movement of a case file between cooperating agents who when they possess the case perform the activities outlined in the flow to complete a process. The flows and their control have been automated with workflow servers moving the case to the agents. The flowchart style model of the automated flows does not typically reflect the true adaptive nature of the collaborative interactions of the agents. research into the enterprise messaging, are solutions that major businesses deploy to provide them with corporation wide e-mail services.
business appeared to provide us with a head start in satisfying a number of customer requirements. However, the architecture of the products was significantly different. It took time for all of us to understand the implications of the integration of these two architectures on each other. There were also subtle impacts on the offer,
The complex adaptive system (CAS) nature of a value delivery system is first introduced. It's a network of agents acting as relays.value delivery system, and
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.
First describes the dynamic nature of any complex adaptive system (CAS).markets that could be targeted. The uncertainties are significant. It is hard to be effective once the customers or channel decide they are part of a 'science experiment'.
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.
When two or more discrete architectures are being integrated as part of a product extension strategy, set very clear expectations about the limited market segments that can be initially targeted.
This page reviews the potential to benefit from strategy in a complex adaptive system (CAS). The challenges described by Dorner require a careful search of the proximate environment.Ensure that the product life-cycle allows for the need to re-factor the combined architecture and value delivery system to respond to market feedback. Hence iterative development is particularly attractive in these situations.