Two Vectors Towards a
Grand Unified Theory of
Systems Engineering
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Dr. George Friedman
Associate Director, Systems Architecting and Engineering Program
University of Southern California
Center for Systems and Software Engineering (CSSE)
Convocation
2006
Selected Quotes:
From 45+ years as a working guy:
“I don’t have time to drain the swamp when I’m up to my
ass in alligators”
“Paralysis though analysis”
“We don’t have time to do it right, but we always seem to
have the time to do it again”
“The data is not available and we don’t have time to collect it”
(but even when it is available, it’s used incorrectly)
“We have neither the time or budget to test thoroughly”
Then, as 3rd president of INCOSE:
“What happened to the ‘systems’ perspective? The SE
process family is stovepiped!”
Why GUTSE?
Presently, Systems Engineering has weaknesses, including:
Multi-stakeholder, multi-criteria --> incoherent, political decisions
Advanced technologies, new configurations, environments
--> deterministic design and modeling in uncertain world
Excessive decomposition-->incomplete FFDs, block diagrams,’walls’
Insufficient academic recognition and “intellectual content”
--> education vs training; core expertise not captured
Two vectors for above: Decision Theory and Graph Theory
Decision Theory
NSF: “Design is merely a series of decisions”
--> “Decision-based design” thrust
--> apply the body of decision theory research to engineering
GJF: Expand and quantify risk management
--> balances performance, cost and schedule triad
--> acknowledges probabilistic nature of technology, environment
--> meta-decisions on utilities and rationality converge consensus
--> decision diagrams provide team wide visibility
--> encourages testing; even imperfect tests
(responds to schedule, budget, quality issues of testing)
Graph Theory
Human cognition ~7 dimensions but Modeling needs 104 +
Decomposition and re-integration is treacherous
Leads to suboptimization and suppression of interactions
Path to complexity management: Multi-D math modeling
Let the primary complexity driver provide tools as well
Constraint Theory employs bipartite graph metamodels
GJ Friedman, Constraint Theory, Multi-D Math Model Mgt, Springer 2005
Addresses the “well-posed” problems of consistency, computability
The “BNS” is the kernel of constraint; at the U{circuits}
Easy properties of the bipartite graph --> well-posed conclusions
Bipartite Graphs
for
Right Brain Rigor
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DECISION THEORY
Tree-like
: Decisions
: Events
CONSTRAINT THEORY
Circuits and Trees
:
:
Relations
Variables
HOW GUTSY IS GUTSE?
Compared to the central reductionist dogma of science,
which “believes” that intelligence can be explained from biology,
biology from chemistry, chemistry from physics, and physics from
cosmology,
GUTSE should be easier because:
we can (or should) choose from mature technologies, far from the
frontiers of science, such as 4-force unification, the origin of life, the
evolution of intelligence, chaos…
GUTSE may be harder because:
it must characterize and integrate human behavior regarding the interpersonal dynamics, goal-setting, design organization, operations, manmachine interactions, system acceptance and politics.
Conclusions
Decision Theory and Graph Theory provide two research
vectors that strengthen many of Systems Engineering’s
present weaknesses, including software integration.
Additionally, they are based on previous rigorous work
and provide intellectual content to systems engineering,
assisting its academic recognition and – hopefully – its
intergenerational capture of expertise.
Warning: be careful not to overdo the detail and rigor
Impacts on Research World
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Completed PhD
Constraint
Theory
Decision
Theory
W. McCumber, UH
F. Serrano, MIT
R. Pulanna, USC
M. Weiler, USC
In-Process PhD
J. Bartolomei, MIT
J. Simpson, UMR
T. Henkle, USC
Also: Design Sheet at Rockwell Science and Boeing
T. Jackson at SJSU on Decision analysis for risk