Optimizing complex systems represents a challenge. Traditional approaches to complex systems development either ignore the issue or optimize subsystems. Some approaches might even iterate through a number of architectures to identify the best one. This paper investigates an alternative approach, namely architecting the complex system to optimize the interactions between the subsystems at design time. The paper uses the interactions in the sex life of males and females (the system) as a case study and shows that better (more pleasurable) results can be achieved by optimizing the system for the interaction at the interface than for the individual (subsystem) experience. The paper then provides diverse examples where systems were or could have been optimized for interactions if seen from the holistic perspective. These instances include weapons systems, logistics systems, the Apollo program, the human cardiovascular system, an online classroom, the INCOSE Australia chapter and a library. The paper concludes with recommendations for further research

1. The Sixth National Conference
INCOSE_IL 2011
• ‫חשיבה מערכתית ומידול מערכתי‬
• Applying holistic thinking to
improving your sex life
• Dr Joseph Kasser
• National University of Singapore
1 March 2011 Applying holistic thinking / 1.21

2. Topics
• Systems approach to solving problems
• The common vision of the solution
– CONOPS
• Aggregating functions
• Complexity and its reduction
• Optimizing your sex life
• Optimizing systems for interactions
• Examples
• Generic model
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3. Systems approach to problem
formulation
Observe
Research
Formulate
hypothesis
Test
hypothesis
Refuted Supported
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4. Systems approach to CONOPS
formulation
Research
Formulate
CONOPS (9)
CONOPS
No
Complete?
Yes
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5. Example: CONOPS of a system
A B C D
E F G H
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6. N2 chart representation
A B C D E F G
A
B
inputs – vertical squares
C
D
E
F
G
outputs – horizontal squares
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7. N2 chart representation
A
B
C
inputs – vertical squares
D
E
F
G
H
outputs – horizontal squares
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8. N2 chart representation
Output
A o o o o
o B o o o o
o C o o o
o o o D o o
o o E o o Input
o o o F o o
o o o o G o
o o o o H
8 March 2011 Applying holistic thinking / 1.21

9. N2 chart representation
A o o o o
o B o o o o
o C o o o
o o o D o o
o o E o o
o o o F o o
o o o o G o
o o o o H
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10. Aggregated (synthesized) subsystems
Subsystems within
subsystem
A
A o o
o BCD o o
BCD EFG
o o EFG
o o H
H
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11. Alternative subsystem grouping-1
A o o o o
o B o o o o
o C o o o
o o o D o o
o o E o o
o o o F o o
o o o o G o
o o o o H
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12. Alternative subsystem grouping-2
A o o o o
o B o o o o
o C o o o
o o o D o o
o o E o o
o o o F o o
o o o o G o
o o o o H
12 March 2011 Applying holistic thinking / 1.21

13. Alternative subsystem grouping-3
A o o o o
o B o o o o
o C o o o
o o o D o o
o o E o o
o o o F o o
o o o o G o
o o o o H
13 March 2011 Applying holistic thinking / 1.21

14. Alternative subsystem grouping-4
A o o o o
o B o o o o
o C o o o
o o o D o o
o o E o o
o o o F o o
o o o o G o
o o o o H
14 March 2011 Applying holistic thinking / 1.21

15. Alternative subsystem grouping-5
A o o o o
o B o o o o
o C o o o
o o o D o o
o o E o o
o o o F o o
o o o o G o
o o o o H
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16. Which aggregation option to
choose?
• How to make choice?
• Literature suggests
– Minimum coupling
– Maximum cohesion
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17. Types of coupling-1
• Content coupling (high): one module modifies or relies
on the internal workings of another module
– e.g. accessing local data of another module
• Common coupling: two modules share the same global
data
– e.g. a global variable
• External coupling: two modules share an externally
imposed data format, communication protocol, or device
interface.
• Control coupling : one module controls the logic of
another, by passing it information on what to do
– e.g. passing a what-to-do flag
17 March 2011 Applying holistic thinking / 1.21

18. Types of coupling-2
• Stamp coupling (Data-structured coupling): modules
share a composite data structure and use only a part of
it, possibly a different part
– e.g. passing a whole record to a function which only needs one field
• Data coupling: modules share data through, for
example, parameters.
• Message coupling (low) : Modules are not dependent
on each other, instead they use a public interface to
exchange parameter-less messages.
• No coupling: modules do not communicate at all with
one another.
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19. Types of cohesion*
*Ian Sommervile 1998
• 1. Coincidental: elements have no relationship
• 2. Logical: elements performing similar functions
• 3. Temporal: elements that are activated at a single time
• 4. Procedural: elements make up a single control
sequence.
• 5. Communicational: elements that operate on the
same input data or produce the same output data.
• 6. Sequential: The output from one element in the
component serves as input for some other element.
• 7. Functional: Each element is necessary for the
execution of a single higher level function.
19 March 2011 Applying holistic thinking / 1.21

20. Factors for Complexity*
• A large number of
members or subsystems
– size, scale
• Strong interactions
between the subsystems
• Combination of the above
* Allison, 2004
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21. Reducing complexity
• Weaken interactions between subsystems
– Minimize coupling
• Use a small number of subsystems at any
level in the hierarchy
– Maximize cohesion
• Configure subsystems for the maximum
degree of homeostasis
21 March 2011 Applying holistic thinking / 1.21

22. Approaches to the optimization
problem
• Subsystem-centric approaches
– (min) coupling and (max) cohesion
• System-centric approach
– Maximum functional cohesion
– Message coupling desirable
– Small number of subsystems
– Homeostatic subsystems
• At HKMF Layer 2+
22 March 2011 Applying holistic thinking / 1.21

23. Systems vs. Holistic
• Systems engineering
– An activity that deals with parts and their interactions as a whole
(Kasser and Hitchins, 2009)
• Systems approach
– An approach to problem solving that views any problem as a part of a
bigger system, and in developing a solution, sees that solution being
achieved through the interaction of system elements*, such that the
properties of the whole are beyond the properties of the individual parts
(Halligan, 2010).
• Holistic approach
– an approach that optimises the system for the interactions between
the subsystems* at design time, rather than an approach that
optimizes the subsystems after the subsystem boundaries have been
determined.
*Bold text by this author.
23 March 2011 Applying holistic thinking / 1.21

24. Improving your sex life
• System (of systems?) problem
• Definition of problem - issues
• Subsystem optimization
– Male experience
– Female experience
• System optimization
– Mutual experience
• Quality or quantity?
– Define ‘quality’ and ‘quantity’
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25. System optimization alternatives
• Optimize subsystem
– (male) experience
• Optimize system
– (mutual) experience
– Approach chosen
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26. Research issue
• Standard Type V holistic thinking approach
• Research the issues
• Generic thinking
– Literature review of domain
– Access lessons learned by others
• Neglected but critical step in current process
• Gain an understanding of the situation
– Do some prototyping
• Interface issues
• Cause and effect
– Time delays
• Feedback
– Negative and positive
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27. The system
• Environment
• Interactions via multiple
interfaces
• Mental
– Verbal
– Behavioral
• Consideration
• Respect
• Physical
– Tactile
• Other
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28. Results
Bragging again
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29. Complex system optimization
problem
• How do you optimize a complex system?
• Subsystem
– less than optimal
• Interactions
– We do so, but sometimes don’t think of it in
that way
– Needs further research
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30. Examples
• Weapons systems
• The Royal Air Force (RAF) Battle of Britain Air Defence
System (RAFBADS)
• Logistics systems
• The Apollo Program
• The MIR space station
• The human cardiovascular system
• A distance learning classroom
• The Library
• Forming the International Council on Systems
Engineering (INCOSE) Australia chapter
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31. Generic model
• The N2 chart is only a start
• The number of subsystems is small, if the interface
between the major subsystems is complicated enough it
can be considered as a subsystem
– This facilitates understanding of the system
• The cohesion of each subsystem is maximized
– The subsystems are designed for homeostasis
• The coupling is minimized
• The system is optimized for interaction at the interfaces
– Interaction is interface dependent
• Requires thinking, tools do not yet exist
31 March 2011 Applying holistic thinking / 1.21

32. Summary
• Systems approach to solving problems
• The common vision of the solution
– CONOPS
• Aggregating functions
• Complexity and its reduction
• Optimizing your sex life
• Optimizing systems for interactions
• Examples
• Generic model
32 March 2011 Applying holistic thinking / 1.21

33. Questions and comments
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