SystemSystemSystemSystemConceptsConceptsConceptsConcepts
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Complexity ReductionA Pragmatic, Semantic Approach
Empowering Collaborative Intelligence
Joseph J SimpsonMary J Simpson
S t C t LLCSystem Concepts LLChttp://www.systemsconcept.org
August 17th, 2011
© 2011 Joseph J Simpson, Mary J Simpson
SystemSystemSystemSystemConceptsConceptsConceptsConcepts
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Overview
Introduction and DefinitionsScientific Foundations• Scientific Method (Abduction Induction Deduction)Scientific Method (Abduction, Induction, Deduction)• Types of Truth• Types of Systems• Types of Complexityyp p y
Historical Systems Engineering (SE) Practices• SE Method, N Squared Chart (N2C)• SE Method Design Structure Matrix (DSM)SE Method, Design Structure Matrix (DSM)• SE Method, Interpretive Structural Modeling (ISM)
Recent Developments• Abstract Relation Type (ART)• Abstract Relation Type (ART)• ART for N2C• ART for DSM• Concept Cube SMConcept Cube
Summary and Questions© 2011 Joseph J Simpson, Mary J Simpson
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Introduction
• Primary goal is to enable group learning• Learning and discovery reduce cognitive
complexity• Groups are collections of intelligent agents both
human and non-human• Some systems engineering (SE) techniques
support structured group learning for humans• Other SE techniques include the use of intelligent
tagents• Evolutionary programming is used to add intelligent
t t l i l SE t h iagents to classical SE techniques
© 2011 Joseph J Simpson, Mary J Simpson
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Scientific
Foundations
© 2011 Joseph J Simpson, Mary J Simpson
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Scientific Method
InferenceReduces uncertainty and develops clear objective views of the world
AbductionGenerates theories, conjectures, hypotheses and explanations that have not yet been verified by induction or deduction
InductionGenerates conclusions based on observations or experimentalGenerates conclusions based on observations or experimental data
DeductionDeductionProcess of formal, mathematical reasoning that produces a conclusion based on a set of assumptions that are given as true
© 2011 Joseph J Simpson, Mary J Simpson
C.S. Peirce, Four Types of Inference SystemSystemSystemSystemConceptsConceptsConceptsConcepts
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“Every perceptual judgment shades into an abductive inference or hypothesis, and further elucidation of the meaning must involve phases corresponding to deduction and induction.”*p p g
P l
Deduction
Perceptual Judgment Abduction
Induction
‘Is more basic than’
© 2011 Joseph J Simpson, Mary J SimpsonFigure adapted from Chapter 2 Universal Priors to Science, A Science of Generic Design, Managing Complexity through Systems Design, John N Warfield, 2003.
Is more basic than
*Manley Thompson, The Pragmatic Philosophy of C S. Peirce, 1953, page 250.
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Peirce, Scope of Inference Structure
Inference
Perceptual J d t Ampliative DeductiveJudgment Ampliative Deductive
Inductive Abductive Syllogistic Non-syllogistic
Crude Quantita-tive Qualitative
‘Is a sub-category of’
© 2011 Joseph J Simpson, Mary J SimpsonFigure adapted from Chapter 2 Universal Priors to Science, A Science of Generic Design, Managing Complexity through Systems Design, John N Warfield, 2003.
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Scientific Method & Model Building, AD Hall
C
Theory
PredictionsC
C C
C CC
Deduction
P P′InductionModify
Confirm, Verify
CompareData,
ObservationsData,
Observations~
Error in Model,Theory
ConfidenceLimits of Model
or Theory
C = ConceptsWhere
© 2004 Joseph J Simpson, Mary J SimpsonFigure adapted from Chapter 4 The Basis of Systems Methodology in Scientific Method, Metasystems Methodology, Arthur D. Hall, 1989.
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Scientific Method-Evolutionary Computation, Fogel
Model Hypothesis
General Solution
Deductive ManipulationHypothesis SolutionManipulation
Inductive Generalization
Deductive Specialization
Measured Real World
Estimated Real World
Independent Verification
© 2011 Joseph J Simpson, Mary J SimpsonFigure adapted from Chapter 1 Defining Artificial Intelligence, Evolutionary
Computation, Toward a New Philosophy of Machine Intelligence, David B. Fogel, 2006.
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Types of Truth
Three types of statements (or sentences) are considered in this discussion: formal, factual and value.
FormalTruth of formal sentences depends only on the form of the logical connectives in the sentence.
FactualTruth of factual sentences depends on the state of the real world, and how well the synthetic statement describes the real world.
ValueTruth of a value sentence is determined in a number of waysTruth of a value sentence is determined in a number of ways, including the methods applied to formal and factual sentences.Value sentence truth expands to ethics, culture, & prediction.
© 2011 Joseph J Simpson, Mary J Simpson
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Types of Truth
Formal Factual ValueExamples(a)(b)
A is BA is not B
John is a man.John is not a man.
John is a good man.John ought to pay five dollars.
(c) If A then B All swans are white. John values gold.
These sentences tell us:
What is possible or impossible
What is true or false in the world.
What ought orought not to be.
us:
Evidence relevant to their truth:
Meaning of words, esp.“is”, “not”, “and”, “if then”.
Meanings and observations.
Meanings, observations and value/ethical systems.
Known by experience?
No Yes Some types yes,some types no.
Are they certain?
Yes No No
Required to A proof is necessary & Observational data No one kind of argument;
© 2011 Joseph J Simpson, Mary J Simpson Adapted from “Metasystems Methodology” A.D. Hall. III
Required to validate inferences:
A proof is necessary &sufficient.
Observational data and predictive success.
No one kind of argument; many desired things.
Types of SystemsSystemSystemSystemSystem
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II. UnorganizedComplexity
(aggregates)
Statistical Analysis Word Frequency Agent Behavior
ss
(aggregates) Agent Behavior
ando
mne
s
III. OrganizedComplexity(systems)
Logic Rules Language Structure Operational Rules
Ra (systems) Operational Rules
“too complex for analysis; too organized for statistics”
I. Organizedsimplicity(machines)
Complexity
(machines)
Adapted from “Introduction to General Systems Thinking” G.M. Weinberg© 2011 Joseph J Simpson, Mary J Simpson
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Types of Complexity
Complexity is defined as the measure of the difficulty, effort and/or resources required for one system to effectively observe, communicate and/or interoperate with, another , p ,system.
Range of Complexity Types
C iti /Cognitive/Perceptual ComputationalBehavioral Organic
[Everything Else]
© 2011 Joseph J Simpson, Mary J Simpson
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Types of Complexity
Cognitive Complexity“… is that sensation experienced in the human mind when, in observing or considering a system frustration arises from lackobserving or considering a system, frustration arises from lack of comprehension of what is being explored.” [Warfield]
Relativistic View of ComplexityRelativistic View of Complexity“… the complexity of a given system is always determined by some other system with which the given system operates.” [Casti]
Computational ComplexityComputational complexity is generally associated with well defined algorithmic problems and the efficient solutions for these stated algorithmic problems.g p
© 2011 Joseph J Simpson, Mary J Simpson
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Observations re. Types of Complexity
HumanCognitive Complexity
ComputerComputational Complexity
Communication in common context (Law 1)
Communication with common syntax (Symbols)
Can communicate with nonsense symbols (Law 2)
Cannot communicate with nonsense symbols
Limited by short-term memory Limited by computation time andLimited by short-term memory recall
Limited by computation time and memory size
Three types of language Single language component:components:
• Prose• Mathematics
Graphics
• Symbols (0,1) (binary logic)
• Graphics
© 2011 Joseph J Simpson, Mary J Simpson
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Historical
Systems Engineering (SE)
PracticesPractices
© 2011 Joseph J Simpson, Mary J Simpson
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SE Method: N-Squared Charts
Initially developed as a systems analysis and interface communication tool for software design and development.Proved to be well suited for the grouping of alternative system• Proved to be well suited for the grouping of alternative system configurations
• Mostly used to communicate system structurey y
• Based on analysis by human experts• Analyze and define interfaces• Present dependency, interdependency and sequence • Evaluate clustering and parallelism• Address interrelationships inherently without biasAddress interrelationships inherently without bias
• Recognize interface patterns and signatures (functionally-bound blocks, nodes, supported nodes, disjoints, etc.) thru use of human pattern recognition
© 2011 Joseph J Simpson, Mary J Simpson
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SE Method: N-Squared Charts
Function1
F1
F2
F1
F3
F1
F4
F1
F5
F1
F6
FeedForward
Functions, elements are placed on the diagonal
F2 F3 F4 F5 F6
F1
F2
Function2
F2
F3
F2
F4
F2
F5
F2
F6 Feed Forward i i di t d t F1
F3
F2
F3
Function3
F3
F4
F3
F5
F3
F6
F1 F2 F3 Function F4 F4
is indicated to the right and
downFeed-Back is indicated
F4 F4 F4 4 F5 F6
F1
F5
F2
F5
F3
F5
F4
F5
Function5
F5
F6
to the left and up
F1
F6
F2
F6
F3
F6
F4
F6
F5
F6
Function6 Feed-Back
Interactions or interdependencies between functions, elements are indicated in the remaining squares
© 2011 Joseph J Simpson, Mary J Simpson
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SE Method: N-Squared Charts
F1
F2 Critical Element or Function
F3
F4
F5
F6Tightly related
Simple Flow (no feedback)
Control Loops
F7
F8
Tightly related functional group
(a ‘natural’ module)
Nodal Point
Loops
F9
F10
Nodal Point
F11Complex
Interactions
Adapted from “The N2 Chart, R. Lano, Copyright 1977, TRW Inc.© 2011 Joseph J Simpson, Mary J Simpson
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SE Method: N-Squared Charts
Nine Functions and Twenty-Three Nodes Five Functions and
Eight NodesF1
F2
F3
F4
Where:FA = F1FB = F2, F3 AND F44
F5
F6
F7
FC = F5FD = F6, F7 AND F8FE = F9
F9
F8
FA
FB
FD
FC
FB
© 2011 Joseph J Simpson, Mary J Simpson
FE
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SE Method: Design Structure Matrix (DSM)
First developed to address the reduction of computational complexity in sets of linear equations.
• Recognized as a powerful graphical tool to communicate large amounts of detailed information to groups of diverse individuals
• Developed to facilitate the solution of large-scale systems problems p g y pusing graphical communication techniques coupled with a set of computational rules and techniques
• Designed to identify system clusters and the highest value systemDesigned to identify system clusters and the highest value system configurations
• Has developed into a number of different, varying techniques each ith i t d tiwith unique syntax and semantics
Identification of feasible, alternative system configurations has proven highly complex and remains the highesthas proven highly complex, and remains the highest barrier to effective implementation
© 2011 Joseph J Simpson, Mary J Simpson
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SE Method: Directional DSM
Item1
Items are placed on the diagonal Rows with no ‘local’ interaction
are allowed
Item2 “Feed-Back” is minimized
X
Item3
Item4
“Feed Forward” is maximized
Direction differs from
Direction differs from
one technique to another
X X
X
Item5
differs from one technique
to another
Output
InputX
X
Item6X X X XX
© 2011 Joseph J Simpson, Mary J Simpson
Interactions or interdependencies between items are indicated in the remaining squares
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SE Method: Balanced DSM
Items are placed on the diagonal Rows with no ‘local’ interaction
are allowedItem1
Item2
Li k
X XX
Links are
Item3
Item4
Links are balancedXX
X X XXbalanced
Item5X
X X
X
XX
Item6X X
© 2011 Joseph J Simpson, Mary J Simpson
Interactions or interdependencies between items are indicated in the remaining squares
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SE Method: Interpretive Structural Modeling
Developed by John N Warfield, to address the reduction of cognitive complexity found in socio-technical systems. Warfield’s science encompasses:p
Behavioral pathologies of individuals, groups, organizations
Logic and language (formal logic, mathematics of structure, og c a d a guage ( o a og c, at e at cs o st uctu e,structural modeling, and languages)
Collaborative abduction (generation of structural hypotheses; bd i h h i i i )group abduction or hypothesis generation in a group process)
Adds intelligent computer-based agent to process
Applies to ideas and concept development
© 2011 Joseph J Simpson, Mary J Simpson
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SE Method: ISM
Logical Structure of the Problem(s)
Computer determines logically feasible set of concepts
Human experts reason, and
make a decision
p
make a decision
Humans focus on the remaining logically feasible concepts in the problem space to solve the problem(s)concepts in the problem space to solve the problem(s)
© 2011 Joseph J Simpson, Mary J Simpson
Problem and/or Set of Problems
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SE Method: ISM, Law of Triadic Compatibility
Quantifies the limitations of short-term memory as they relate to human decision makingThe human mind can recall and operate with seven concepts:The human mind can recall and operate with seven concepts:
• Three elements• The four combinations associated with three elements
{1,2,3}
The human mind is compatible with the need
{1,2} {1,3} {2,3}p
to explore interactions among a set of three elements
1 2 3elements
Capacity cannot be presumed for a set that both has four
© 2011 Joseph J Simpson, Mary J Simpson
Capacity cannot be presumed for a set that both has four members, and for which those members interact
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SE Method: ISM, Principle of Division by Threes
Iterative division of a concept as a means of analysis is mind compatible if each division produces at most three components, thereby creating a ‘tree’ withp , y g• One element at the top• At most three elements at the second level• At most nine elements at the third level• And so on…
Fi t l lFirst level
Second level
© 2011 Joseph J Simpson, Mary J Simpson
Third level
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Recent
Developments
© 2011 Joseph J Simpson, Mary J Simpson
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Abstract Relation Type (ART)
Based on the Abstract Data Type concept• Similar structure
• Similar behavior
• Programming language independent
Abstract Relation Types• Focused on global system organizing relationship
• Similar structure
• Similar semantics
• Specific system type independent
Used as basic components of a pattern language
© 2011 Joseph J Simpson, Mary J Simpson
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Abstract Relation Type (ART)
Has three main parts:
Prose Description (text, words)• Formal pattern• Informal prose
G hi R t tiFormal Prose
Graphic Representation(directed graphs)
• Must have formal graphsInformal Prose
g p• Can also have informal graphs
Mathematics & ComputerGraphs Math
Representation• Math equations
C t d
© 2011 Joseph J Simpson, Mary J Simpson
• Computer codes• One or both
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Abstract Relation Type, Setting the Context
Focus on system organizing relationship• Natural language relationship (meta-language)
Relationship typesRelationship types• Definitive• Competitive• InfluenceInfluence• Temporal• Spatial• Mathematical
• Mathematical relation (object language)Relation types
Universal• Universal• Reflexive• Transitive• Symmetric
© 2011 Joseph J Simpson, Mary J Simpson
• Symmetric• Hybrid
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Abstract Relation Type (ART) Example
Marking Space
Represents system structure
Value Space
Represents system value configuration(s)Represents system value configuration(s)
Outcome Space
Represents aggregation of system structure and system value(s)
Evolutionary algorithms are used to search the Outcome Space to find the best-fit solution
© 2011 Joseph J Simpson, Mary J Simpson
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Abstract Relation Type (ART)
Outcome Space (OS1)
. .
Marking Space (MS1)
. . ..
.
Value Space (VS1)
Abstract Relation Type (ART) ≡ Ƒ [ MS, OS ]
© 2011 Joseph J Simpson, Mary J Simpson
Outcome Space (OS) ≡ Ƒ [ VS1, VS2, …VSn, VSn+1, … ]
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Abstract Relation Type (ART), Example
A B C D E FA 0 0 0 0 0 1 0 1 2 3 4 50 1 2 3 4 5
Marking SpaceValue Matrix
Number Field
BCD
00
0
00
0
10
0
01
0
010
01
1
00
0
11
1
22
2
33
3
421
21
1
00
0
11
1
22
2
33
3
421
21
1
reflects the same properties as the
organizing relationshipxD
EF
00
0
00
0
000
1110
001
00
00
0
11
5
243
1432
321
210
00
11
5
243
1432
321
21
000
00
20
00
05
0
Outcome SpaceRepresents the System Structure [for each
object through , a one in the cell shows
A F Reflects the l ti l0
00
00
0
20
0
03
0
020
01
1
o e t e ce s o slocation of an interface] =
relative value of the system
structure
© 2011 Joseph J Simpson, Mary J Simpson
000
00
143
00
00
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ART for N2C
1
1
1
1
1
1
0 0 0 0 0
00 0 0 0
1
0
0
0
0
0 0 00 0 0
1
1
1
1
1
1
0 0 0 0 0
00 0 0 0
1
0
0
0
0
0 0 00 0 0
1
1
1
1
1
1
0 0 0 0 0
00 0 0 0
1
0
0
0
0
0 0 00 0 0
No Obvious Pattern;
Unordered1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Unordered
1
11
1 0 0 0 0
0 0 0 0
0
0 0 0
0 01
11
1 0 0 0 0
0 0 0 0
0
0 0 0
0 01
11
1 0 0 0 0
0 0 0 0
0
0 0 0
0 0
1
1
1 1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1 1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1 1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
11
11
1
0
0
00
0 0
0
0
0
0 0
0
0
0
0 0
000
01
1
11
11
1
0
0
00
0 0
0
0
0
0 0
0
0
0
0 0
000
01
1
11
11
1
0
0
00
0 0
0
0
0
0 0
0
0
0
0 0
000
0
1 11 00 00 0 01 11 00 00 0 01 11 00 00 0 0
1
1
1
11
1
1
1 0
0
0
0
0
0
0 0
0 0
0
0 0
0
0
0
0
0
0
1
1
1
11
1
1
1 0
0
0
0
0
0
0 0
0 0
0
0 0
0
0
0
0
0
0
1
1
1
11
1
1
1 0
0
0
0
0
0
0 0
0 0
0
0 0
0
0
0
0
0
0
Ordered;
© 2011 Joseph J Simpson, Mary J Simpson
1 1
1 1
0
0
0 0
0
0
0 0
0
0
0
0
0
0
1 1
1 1
0
0
0 0
0
0
0 0
0
0
0
0
0
0
1 1
1 1
0
0
0 0
0
0
0 0
0
0
0
0
0
0
Ordered;Obvious Patterns
SystemSystemSystemSystemConceptsConceptsConceptsConcepts
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ART for N2C, Evolutionary Algorithm Applied
2 61 0 1 2 3 4 5
1 70 1 2 3 4 5 6
0 81 2 3 4 5 6 7
5 34 3 2 1 0 1 2
4 43 2 1 0 1 2 3
3 52 1 0 1 2 3 4
1
1
11
1
0 0 0 0 0
00 0 0 0
0
00
1
1
11
1
0 0 0 0 0
00 0 0 0
0
00
1
1
11
1
0 0 0 0 0
00 0 0 0
0
00
Unordered Marking Space; No Obvious
6 25 4 3 2 1 0 1
7 16 5 4 3 2 1 0
5 34 3 2 1 0 1 21
1
1
1
1
1
1
1
1
00 0 0 0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
00 0 0 0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
00 0 0 0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Space; No Obvious Pattern
8 07 6 5 4 3 2 11
1
1 1
1
1
1
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1 1
1
1
1
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1 1
1
1
1
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
11
1
1
1
0 0 0 0
0 0 0 0
1
0
0
0
00
0
0
0 0
0
0
0 0
0
0 0
0
000 1
1
11
1
1
1
0 0 0 0
0 0 0 0
1
0
0
0
00
0
0
0 0
0
0
0 0
0
0 0
0
000 1
1
11
1
1
1
0 0 0 0
0 0 0 0
1
0
0
0
00
0
0
0 0
0
0
0 0
0
0 0
0
000
1
1
1
1
11
11
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0 0
0
0
0
0
0
0
0
0
1
1
1
1
11
11
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0 0
0
0
0
0
0
0
0
0
1
1
1
1
11
11
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0 0
0
0 0
0
0
0
0
0
0
0
0
Ordered Marking Space;
© 2011 Joseph J Simpson, Mary J Simpson
1 1 00 0 00 0 0 1 1 00 0 00 0 0 1 1 00 0 00 0 0Obvious Patterns
SystemSystemSystemSystemConceptsConceptsConceptsConcepts
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ART for DSM
• System structure is separated from system value
• Marking space is based on Warfield’s Mathematics of Structure – augmented Boolean mathematics
• Value space is based on Hitchins’ Automated N S d hSquared chart
• Evolutionary algorithm ART method generates did t l ti t ti ll d i th t fcandidate solutions automatically, reducing the cost of
human systems analysis and creating solutions not seen by the human analystseen by the human analyst
© 2011 Joseph J Simpson, Mary J Simpson
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ART for DSM
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© 2011 Joseph J Simpson, Mary J Simpson
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SystemSystemSystemSystemConceptsConceptsConceptsConcepts
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SE Method: Concept Cube SM
A Generic Concept Cube Approach• Designed to organize any given concept so that human
experts can recognize and evaluate a conceptexperts can recognize and evaluate a concept• Adapts basic aspects of Formal Concept Analysis• Incorporates Warfield’s “Law of Triadic Compatibility”Incorporates Warfield s Law of Triadic Compatibility
(LTC) and “Principle of Division by Threes” (PDT)• Main concept assigned as Level 0 (L0)• Three sub-concepts assigned as Level 1 (L1)• Standard numbering scheme (level, parent, local):
L0 > C0 0L0 -> C0.0L1 -> SC1.1; SC1.2; SC1.3L2 -> SC2.1.1; SC2.1.2; SC2.1.3
SC2.2.1; SC2.2.2; SC2.2.3SC2.3.1; SC2.3.2; SC2.3.3
© 2011 Joseph J Simpson, Mary J Simpson
The Concept
SystemSystemSystemSystemConceptsConceptsConceptsConcepts
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The Concept Cube SM
Sub-Concept
1.1
The Concept Cube SM
Sub-Concept
1.2
Sub-Concept
1.3Highest Level of
Abstraction Level 1
Sub-Concept
2.1.1
Sub-Concept
2.3.1
Abstraction - Level 1
Abstraction Level 2
Sub-Concept
2.2.1
Sub-Concept
2.1.2
Sub-Concept
2.1.3
Sub-Concept
2.3.2
Sub-Concept
2.3.3
Sub-Concept
2.2.2
Sub-Concept
2.2.3Sub-Concept
1.3 CubeSub-Concept
1.1 Cube
Sub-Concept 1.2 Cube © 2011 Joseph J Simpson, Mary J Simpson
© 2011 Joseph J Simpson, Mary J Simpson
SystemSystemSystemSystemConceptsConceptsConceptsConcepts
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Concept Cube Example, The Asset Cube
Specification
ProgramType
The Asset Cube
The System
SpProg
Action
Exposure
Eff figur
atio
n
Valu
e
Prot
ectio
n
Process
nmentProduct
Mechanism
torActor
Effect
Con
fi
The Threat
The TargetExposure Type
Pr
Environm
Requirements
Function
Architectur
Cos
t
Sced
ule
Tech
nica
l
Storage
ocessingTransmission
C s
MecVector
Event
Pre-Event
Post-Event Imm
edia
te
Nea
r-Te
rm
Long
-Ter
m
Configuration
tureProce
Integrity
Confidentiality
Availability Tech
nolo
gy
Polic
y, P
ract
ices
Hum
an F
acto
rs
ent
The System Cube
The Threat Cube
Action Effect
V l P t ti
Specification Program
© 2011 Joseph J Simpson, Mary J Simpson
y
The Target Cube[Information Assurance]
Value Protection
SystemSystemSystemSystemConceptsConceptsConceptsConcepts
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Example, The Asset Cube – Level 1 Interfaces
ThreatThreat-Target
InterfaceSystem-Threat
Interface
SystemTarget
Target-SystemInterface
© 2011 Joseph J Simpson, Mary J Simpson
SystemSystemSystemSystemConceptsConceptsConceptsConcepts
SM
Example, The Asset Cube – Level 2 Interfaces
Threat Interfaces
System Interfaces
Expo.
Exposure-Action
Effect-Exposure
Type
Type-Specification
Program-Type
EffectAction
Action-Effect
ProgramSpec.
Specification-Program
Config.
Configuration- Protection-
Protect.Value
gValue
ProtectionConfiguration
V lTarget
© 2011 Joseph J Simpson, Mary J Simpson
Value-Protection
gInterfaces
SystemSystemSystemSystemConceptsConceptsConceptsConcepts
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Summary and
Questions
© 2011 Joseph J Simpson, Mary J Simpson
SystemSystemSystemSystemConceptsConceptsConceptsConcepts
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Summary
ART approach facilitates reduction of cognitive complexity • Encodes typical systems engineering techniques in clearly defined
processes and patternsp p• Supports the application of computing resources to evaluate
alternative system representations• Separates system structure from system value and system semantics• Separates system structure from system value and system semantics,
& supports detailed communication of both the system organizing structural relationship, and system interface value relationships
• Connects these relationships to mathematical relations that are the• Connects these relationships to mathematical relations that are the basis for the computation and analytical portions of the cognitive complexity reduction
• Contributes to greater relative density of information transfer due to its• Contributes to greater relative density of information transfer due to its use of a formal approach to prose, mathematics, and structural graphics
E l i di ti h ART l ti t ti t h i ill
© 2011 Joseph J Simpson, Mary J Simpson
Early indications show ART evolutionary computation techniques will directly reduce computational complexity associated with the solution of complex system problems
SystemSystemSystemSystemConceptsConceptsConceptsConcepts
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Discussion
• Questions?
• Comments?
© 2011 Joseph J Simpson, Mary J Simpson