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.
Chess illustrates key aspects of strategic thinkingChess matches are heavily documented and analyzed, and
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.each theory advanced about a
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.move sequence is typically played out in later games that test the theory practically. Much of the logic 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.complex adaptive systems is visible.
John Holland's framework for representing complexity is outlined. Links to other key aspects of CAS theory discussed at the site are presented.
I would recommend any competitive strategist to validate his theories at low cost on the chess board.
Much of the 2000 tech. bubble could have been avoided had the networking industry paid more attention to matching
Agents can manage uncertainty by limiting their commitments of resources until the environment contains signals strongly correlated with the required scenario. This page explains how agents can use Shewhart cycles and SWOT processes to do this.pre-conditions to levels of commitment instead of adopting strategic intent justified by positive returns, W. Brian Arthur's conception of how high tech products have positive economic feedback as they deploy. Classical products such as foods have negative returns to scale since they take increasing amounts of land, and distribution infrastructure to support getting them to market. High tech products typically become easier to produce or gain from platform and network effects of being connected together overcoming the negative effects of scale.
Alekhine's classic win against Marshall Baden-Baden May 1925 with a Pawn storm attacking Marshall's Queen and King was never in doubt. My schema for his win is: weak square complex + opponents castling strategy +
Agents can manage uncertainty by limiting their commitments of resources until the environment contains signals strongly correlated with the required scenario. This page explains how agents can use Shewhart cycles and SWOT processes to do this.commitments=pre-conditions + Pawn storm;
Alekhine's joint use of weak square complexes and
This page discusses the benefits of bringing agents and resources to the dynamically best connected region of a complex adaptive system (CAS).centralization demonstrates the building of strategic advantage based on deep understanding 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.environment and creating weaknesses in the opponent's position. His balancing of commitment to pre-conditions managed 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.
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.
while enabling powerful attacks.
Since chess is a far simpler system than life it provides a model environment for understanding competitive advantage. While it does not model unbalanced situations like the
This page reviews Christensen's disruption of a complex adaptive system (CAS). The mechanism is discussed with examples from biology and business.innovator's dilemma it can apply in battles between established competitors. Often the root causes of success are simpler to identify than in the business scenario.
Even with the highly documented nature of the major games sophisticated theories that rationally explain the situation in the center of a chess board did not appear until Steinitz described his positional strategies, a logic extended and complemented by Nimzowitsch's "My System" and his demonstration of the strategies implied in "Chess Praxis." The full 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.
nature of chess strategy is demonstrated in "Tal Botvinnik 1960".