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The object of my design

Summary
Grady Booch advocates an object oriented approach to computer software design. 
Object Oriented Design with Applications
In Grady Booch's book 'Object Oriented Design with Applications' his elegant description of the
Dietrich Dorner argues complex adaptive systems (CAS) are hard to understand and manage.  He provides examples of how this feature of these systems can have disastrous consequences for their human managers.  Dorner suggests this is due to CAS properties psychological impact on our otherwise successful mental strategic toolkit.  To prepare to more effectively manage CAS, Dorner recommends use of:
  • Effective iterative planning and
  • Practice with complex scenario simulations; tools which he reviews.   
problem of arbitrary complexity
of industrial software systems should resonate with any professional software developer. 

Booch asserts that the level of complexity, M. Mitchell Waldrop describes a vision of complexity via:
  • Rich interactions that allow a system to undergo spontaneous self-organization
  • Systems that are adaptive
  • More predictability than chaotic systems by bringing order and chaos into
  • Balance at the edge of chaos
exceeds the intellectual capacity of individual developers.  Booch concludes that developers must adopt disciplined ways to master complexity. 

Booch defines complexity, M. Mitchell Waldrop describes a vision of complexity via:
  • Rich interactions that allow a system to undergo spontaneous self-organization
  • Systems that are adaptive
  • More predictability than chaotic systems by bringing order and chaos into
  • Balance at the edge of chaos
as resulting from four elements:
  1. The complexity of the problem domain.  
  2. The difficulty of managing the development process. 
  3. The inherent flexibility of software. 
  4. The problem of characterizing the behavior of discrete systems. 
Booch reviews examples of complex systems where he lists:
  • The structure of a personal computer.  Booch points out the presence of hierarchies of elements that cooperate together to support the operation of the system. 
  • The structure of plants and animals.  Booch highlights the static hierarchy of multi-cellular plants and animals.  Booch argues that there are clear boundaries between the inside and outside of any given level.  Each functional unit is argued to have clear separation of concerns from the others that contribute to the whole animal or plant.  Booch admits the presence of many independent cooperating agents exhibiting complex behavior.  Booch points out that plants and animals show commonality across domains. 
  • The structure of matter. 
  • The structure of social institutions. 
From this review he highlights five attributes of his complex systems:
  1. Frequent presence of hierarchy.  Booch points out that there are often many hierarchies that can be identified in a complex system. 
  2. The primitive components of the system are relatively arbitrary. 
  3. Intra-component linkages are generally stronger than inter-component linkages. 
  4. Hierarchic systems are usually composed of only a few different kinds of subsystems in various combinations. 
  5. Complex systems that work have evolved from simpler systems that worked. 
Booch subsequently asserts that the hierarchies in a complex system are either 'part of' (object) or 'kind of' (class) hierarchies. 

Complex adaptive system
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. 
(CAS) theory
applies directly to some of the systems which Booch reviews in developing his classification of complexity.  Higher plants and animals are seen as
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-based
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. 
emergent
systems.  Social institutions are viewed as CAS systems composed of human agents.  However personal computers would have to be replaced by the
This page discusses the effect of the network on the agents participating in a complex adaptive system (CAS).  Small world and scale free networks are considered. 
network
of companies, and their staffs, who develop and use the personal computers which are instead viewed as
Tools and the businesses that produce them have evolved dramatically.  W Brian Arthur shows how this occurred.
tools


It is not surprising that Booch identifies a hierarchy in personal computers.  Mead and Conway's 'Introduction to VLSI Systems' highlights their gathering of key attributes of a VLSI is very large scale integration of silicon on a single chip.  Robert Noyce and Jack Kilby realized that all components of a circuit could be fashioned on one chip of semiconductor material removing the interconnection wiring constraint.   system from meetings with industry and academic participants and the subsequent enforcement of hierarchy and abstraction as approaches to limit complexity in the development of VLSI based computer systems. 

CAS theory asserts that the network of agents is sustained and controlled through
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 products
.  It's likely the system will collapse if these flows fail. 

CAS theory also highlights the existence of a shared
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 plan
.  This plan is now known to be inherited in evolved forms by all living systems.  The essential aspects have been maintained as
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
evolution
drove the emergence of different phenotypes is the system that results from the controlled expression of the genes.  It is typically represented by a prokaryotic cell or the body of a multi-cell animal or plant.  The point is that the genes provide the control surface and the abstract recipe that has been used to generate the cell.  .   Local state and
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. 
external signals
drives the selection of schemata by the agents.  Hence Booch's assertion of stronger intra-component linkages seems unjustified. 

Schematic plans do not contain Booch's 'part of' and 'kind of' relations.  Instead dependent on the specific
This page discusses the potential of the vast state space which supports the emergence of complex adaptive systems (CAS).  Kauffman describes the mechanism by which the system expands across the space. 
environmental state
of an agent its schematic plan will induce phenotypic responses that have proved evolutionarily beneficial.  It is that process which allows the
This page reviews the implications of reproduction initially generating a single child cell.  The mechanism and resulting strategic options are discussed. 
organism
(
This page reviews the inhibiting effect of the value delivery system on the expression of new phenotypic effects within an agent. 
and network
) to adapt as a whole even though there is no central coordination. 

Three of Booch's four elements of complexity (one, two and four) are general challenges of effective
This page introduces some problems that make it hard for a business to execute effectively. 
It then presents a theory of execution. 
It describes what the theory says must be done to execute effectively. 
It reviews General Electric's use of adaptive planning to support effective execution. 
Then it details the execution requirements. 
execution
.  Businesses, for example, operate as adaptive systems to cope with unpredictably shifting environments.  Instead Booch asserts the object oriented design process he details can identify the hierarchic essence.  CAS theory suggests his assertion will only be true if the problem and development domains have aligned schematic structures.  That is typically achieved by a
This page discusses the program strategy in a complex adaptive system (CAS).  Programs generate coherent end-to-end activity.  The mechanism is reviewed. 
program approach
and iterative development. 

The object and class foundation of object oriented design is its most significant weakness.  The
Carlo Rovelli resolves the paradox of time. 
Rovelli initially explains that low level physics does not include time:
  • A present that is common throughout the universe does not exist
  • Events are only partially ordered.  The present is localized
  • The difference between past and future is not foundational.  It occurs because of state that through our blurring appears particular to us
  • Time passes at different speeds dependent on where we are and how fast we travel
  • Time's rhythms are due to the gravitational field
  • Our quantized physics shows neither space nor time, just processes transforming physical variables. 
  • Fundamentally there is no time.  The basic equations evolve together with events, not things 
Then he explains how in a physical world without time its perception can emerge:
  • Our familiar time emerges
    • Our interaction with the world is partial, blurred, quantum indeterminate
    • The ignorance determines the existence of thermal time and entropy that quantifies our uncertainty
    • Directionality of time is real but perspectival.  The entropy of the world in relation to us increases with our thermal time.  The growth of entropy distinguishes past from future: resulting in traces and memories
    • Each human is a unified being because: we reflect the world, we formed an image of a unified entity by interacting with our kind, and because of the perspective of memory
    • The variable time: is one of the variables of the gravitational field.  With our scale we don't register quantum fluctuations, making space-time appear determined.  At our speed we don't perceive differences in time of different clocks, so we experience a single time: universal, uniform, ordered; which is helpful to our decisions

study of time
tells us that objects is a collection of: happenings, occurrences and processes; including emergent entities, as required by relativity, explains Rovelli.  But natural selection has improved our fitness by representing this perception, in our minds, as an unchanging thing, as explained by Pinker.  Dehaene explains the object modeling and construction process within the unconscious and conscious brain. 
are
This page reviews the implications of selection, variation and heredity in a complex adaptive system (CAS).  The mechanism and its emergence are discussed. 
natural selections
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
models
,
Computational theory of the mind and evolutionary psychology provide Steven Pinker with a framework on which to develop his psychological arguments about the mind and its relationship to the brain.  Humans captured a cognitive niche by natural selection 'building out' specialized aspects of their bodies and brains resulting in a system of mental organs we call the mind. 

He garnishes and defends the framework with findings from psychology regarding: The visual system - an example of natural selections solutions to the sensory challenges of inverse modeling of our environment; Intensions - where he highlights the challenges of hunter gatherers - making sense of the objects they perceive and predicting what they imply and natural selections powerful solutions; Emotions - which Pinker argues are essential to human prioritizing and decision making; Relationships - natural selection's strategies for coping with the most dangerous competitors, other people.  He helps us understand marriage, friendships and war. 

These conclusions allow him to understand the development and maintenance of higher callings: Art, Music, Literature, Humor, Religion, & Philosophy; and develop a position on the meaning of life. 

Complex adaptive system (CAS) modeling allows RSS to frame Pinker's arguments within humanity's current situation, induced by powerful evolved amplifiers: Globalization, Cliodynamics, The green revolution and resource bottlenecks; melding his powerful predictions of the drivers of human behavior with system wide constraints.  The implications are discussed. 

built into our minds
to represent the ever changing environment as a 'thing', but apparent object constancy supports Plato's mistakeHierarchy feels similarly natural to us.  Instead, adaptive system agents actually
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. 
emerge
from aggregations which reflect the state that can be represented in the enclosed agent and the plan that is shared by every agent in the system.  


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integrating quality appropriate for each market
 
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. 
Strategy
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This page uses an example to illustrate how:
  • 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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