Presentation from May 10, 2005 Dinner Meeting

8/9/2019 Presentation from May 10, 2005 Dinner Meeting

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Systems and Software Consortium | 2214 Rock Hill Road, Herndon, VA 20170-4227

Phone: (703)742-8877 | FAX: (703)742-7200

www.systemsandsoftware.org

Practical Applications

of Complexity Theory

May 10, 2005

Sarah Sheard


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An expanded presentation of the symposium paper

Practical Applications of Complexity Theory

for Systems Engineers

To be given by at INCOSE

July 12, 2005, 4:30 pm

Rochester, NY


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Topics

System development paradoxes Chaos theory

Complexity theory

Complex adaptive systems

Suggestions for systems engineers

References


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Paradoxes with system development

Everyone wants system enterprise architecting, but no onereally follows through on it. The drawings hardly end up

driving anything.

Any really big systems needing to be designed are obsolete

by the time they get funded (even complicated analyses are

obsolete by the time they are complete). We try to stabilize requirements so we can build something,

but requirements creep remains the number one problem, and

its usually the customer who makes the changes.

We are building systems of systems, but no one lets out

contracts for systems of systems, they let out contracts forsystems or elements of systems. They fund, and produce

requirements for, only the pieces.


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Paradoxes with system development

Revenge effects:...the very steps we take to solve a problemcome back and bite us. For example, those who clean their

kitchens the most end up having the most antiseptic-resistant

bacteria. (Reference: Tenner)

Cause and effect become complicated...you find a problem

and look for its cause...then analyze it, and it has manycauses...find the reasons for them, and pretty soon you have

to solve everything in the world.

Any change you try to make engenders resistance that tries to

undo all your good work, and often successfully (e.g.,

technology insertion). You set up an assessment method to check how well some-

one does something in general, but pretty soon everyone

starts preparing for the test, and only the test.


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Paradoxes

Can these paradoxes be resolved by lookingthrough a different view, that of complexity?


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Chaos theory: Precursor to Complexity

Order exists within apparent randomness, e.g. drips of a faucet, wildlife

population

Simple systems can cause complex behavior

all they need is nonlinearity

Small nonlinearity factor in wildlife

population equation leads to steady state

Larger nonlinearity leads to boom/bust

oscillation

As nonlinear factor increases, cycle

doubles again until becoming chaotic

This is reproduced in many experimental

and mathematical domains[e.g., x(next) = a x (1-x) ]

Nonlinearity factor


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Chaos Theory conclusions

Simple equations do notimply

simple behavior

Strange attractors and fractals

describe nature better than linear

equations

Starting close together does not

imply ending close together...butterfly effect

End points diverge from nearly

indistinguishable starting points

The universe does not work

mostly like a predictable,controllable machine

Yet we need to predict and control

our projects and programs


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Complexity Theory

Chaos Theory

describes one

dimension

What happens at

the transition fromorder to chaos?

Many systems

adapt to live at the

edge of chaos!

Goals:

Source: http://www.theory.org/fracdyn/

neurodyn/langton-bifurcation.html

Characterize complex adaptive systems

Look at what we engineer in terms of complex adaptive systems


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Edge of Chaos

Order :Too littlecommunication

Chaos:Too unstable

Complex Adaptive systemsadapt toward the Edge of Chaos


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Critical point between Chaos and Order

Power law of events at the critical point When a sand pile is at critical angle, [number of grains falling

when one is added] follows a power law

So does number of earthquakes, etc.

Catastrophes are part of critical state

Source: http://www.theory.org/fracdyn/

neurodyn/langton-bifurcation.html


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Complexity Theory

Complexity theory is based on relationships, emergence,patterns and iterations

Complexity theory maintains that the universe is full of

systems (such as weather systems, immune systems,

and social systems) that are complex and constantly

adapting to their environment*

Complexity Theory examines the implications of such

interacting systems

*Peter Fryer, http://www.trojanmice.com/articles/complexadaptivesystems.htm


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Complex Adaptive Systems

1. Order is emergent, not predetermined (flock not building)2. Systems history is irreversible

3. Systems future is often unpredictable

Self-organizing

Includes agents: semi-autonomous building blocks following rules,seeking to optimize something by evolving over time (e.g. flora and

fauna in an ecosystem)

Fitness of the agent evolves in a complex manner (fitness

landscape is complex)

System as a whole (e.g. ecosystem) becomes more fit as itbecomes more complex (more connected, more intelligent)


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Scale of Systems

Ordinary Systems*(can be satisfactorily

treated as autonomous)

Systems of Systems*

(have a system integrator)

Complex Systems*

(adaptive, homeostatic)

*Taxonomy and terms by Michael L. Kuras and Brian E. White of Mitre Corp.

Ball BearingsOne Software Subroutine

MREs and Bullets

...

LSI Chip

Hoover Dam

Boeing 747

Space Shuttle

Windows Operating

System

...

Air Traffic Control

Ballistic Missile Defense

Homeland Security

Human Civilization

Less Complex,

Deterministic

More Complex,

Stochastic

MRE = Meals ready to eat.

LSI = large-scale integration


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Complex adaptive systems today

The world of systems is far more like biological complexadaptive systems than like clockwork mechanical

systems. Think of the larger system your system fits into

as biological, with a homeostatic mechanism...resisting

your changes... e.g. government and bureaucracies.

You are always only changing out pieces of ongoingliving systems, never actually creating a new system.

Hence the design (enterprise architecture) is always

ongoing, at best describing a living, changing reality,

never a true predictive plan. Your system changes its environment just by existing.


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Implications for paradoxes (1)

Think of the larger system you fit into as a biological, with homeostatic

mechanism...resisting your changes... e.g. government, bureaucracies

Any change you try to make engenders resistance that tries to

undo all your good work, and often successfully (e.g.,

technology insertion). Note homeostasis, and plan for it.

Cause and effect become complicated...you find a problem andlook for its cause...then analyze it and it has many causes...find

the reasons for them, and pretty soon you have to solve

everything in the world. Understand that cause and effect are

first-order, mechanistic, linear concepts that dont make so

much sense in complex systems. Try instead to understand

causal loops and system feedback.


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You are always only changing out pieces of ongoing living systems,

never actually creating a new system, hence enterprise architecture isalways ongoing, at best describing a living, changing reality, never a true

predictive plan

We are building systems of systems, but no one lets out contracts

for systems of systems, they let out contracts for systems or

elements of systems. They fund, and produce requirements for,only the pieces.

Everyone wants system enterprise architecting, but no one really

follows through on it. The drawings hardly end up driving

anything.

Consider these as simple facts. The architecture of a system-of-systems is just the as-is description of an organism at one time.

The system will adapt on its own, not according to someones plan.


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Your system changes its environment just by existing.

Any really big systems needing to be designed are obsolete

by the time they get funded (even complicated analyses are

obsolete by the time they are complete)

Consider different kinds of analyses and design, such as

genetic algorithm analyses, real options valuation, and set-

based design approaches. See references by McConnell,

Shisko, and Kennedy.

McConnell: Emergence: Applying the Principles using Genetic

Algorithms to derive [production deployment] schedules: A

near-optimal solution was found within minutes as against days.


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Your system changes its environment just by existing. We try to stabilize requirements so we can build something,

but requirements creep remains the number one problem,

and its usually the customer who makes the changes.

Revenge effects:...the very steps we take to solve a problem

come back and bite us. For example, those who clean theirkitchens the most end up having the most antiseptic-resistant

bacteria.

You set up an assessment method to check how well some-

one does something in general, but pretty soon everyone

starts preparing for the test, and only the test.Start thinking of systems as complex adaptive systems. See

suggestions in symposium paper.


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Suggestions for Systems Engineers

Understand the problemspace better Ask more questions

Understand the

environment

Make better mental models

Make specific

improvements in: Modeling and simulation

Design

Risk identification and

management

Change management

Help management Embrace change Predict trends

Change program

management

Reconsider the distribution

of enterprise control

Focus research Look for data on

experientially derived

heuristics

Evolve SE principles basedon complexity theory


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So Where Are We Now?

The significant problems we have cannot be solved atthe same level of thinking with which we created them

- Albert Einstein


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References and Recommended Reading

Fryer, Peter. What are Complex Adaptive Systems? htttp://www.trojanmice.com/articles/

complexadaptivesystems.htm .Gleick, James. Chaos: The Making of a New Science. New York: Viking, 1987.

Kennedy, Michael N. Product Development in the Lean Enterprise: Why Toyotas System is FourTimes More Productive and How You Can Implement It. Richmond, Virginia: The Oaklea Press,2003.

Kuras, M.L. and Brian E. White,Engineering Enterprises using Complex Systems Engineering, Mitre

report MP 05B 0000003, 2005

Langtons Complexity at http://www.theory.org/fracdyn/neurodyn/langton-bifurcation.html.

Lewin, Roger. Complexity: Life at the Edge of Chaos. New York: Collier Books, 1992.

McConnell, George R. Emergence: Applying the Principles using Genetic Algorithms to deriveSchedules. Proceedings of INCOSE. Las Vegas, Nevada, 2003.

Sanders, T. Irene. Strategic Thinking and the New Science: Planning in the Midst of Chaos,Complexity, and Change. New York: The Free Press, 1998.

Senge, Peter. The Fifth Discipline: The Art andP

ractice of the Learning Organization. Doubleday,1990.

Shishko, Robert, Donald H. Ebbeler, and George Fox. NASA Technology Assessment Using RealOptions Valuation, Systems Engineering7(1), 2004, 1-12.

Tenner, Edward . Why Things Bite Back: Technology and the Revenge of Unintended Consequences.Knopf, 1997

Waldrop, Michael. Complexity: The Emerging Science at the Edge of Order and Chaos. New York:

Touchstone, 1992.

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Presentation from May 10, 2005 Dinner Meeting

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