Simplicity on the Other Side
of Complexity
An Introduction to Complexity
Science, Management & Health Care
Complexity Lens Reflection
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We are finely tuned “complex adaptive
systems,” especially when we are working at
our highest intelligence & purpose.
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Describe a time or experience when a collaborative
effort created or encouraged something surprising. It
should be something you are proud to have been a
part of… a difference that made a difference. It can
be a very small, subtle thing. It could be from your
current workplace or a past effort of any kind.
See the Workbook Handout
Heart Rate Dynamics
Blood Cell Dynamics
EEG Dynamics
Looking For Success In The
Wrong Places
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Substantial gains in performance - 40% have been documented in productivity,
quality, value.
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“What matters is managers’ point of view.”
“…confronting how we think about work,
organizations, and the people in them.”
Pfeffer, The Human Equation
Tom Petzinger
Wall Street Journal
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“Even as it was toppled from unassailability in
science, Newtonian mechanics remained firmly
lodged as the mental model of management, from
the first stirrings of the industrial revolution right
through the advent of modern-day M.B.A.
studies.”
As biologists and other pioneers began to realize,
it could not explain the self renewing processes of
life.
Scientific Origins
Before Complexity
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Scientists believed the future was knowable given enough
data points
Dissecting discrete parts would reveal how everything -- the
whole system -- works
Phenomena can be reduced to simple cause & effect
relationships
The role of scientists, technology, & leaders was to predict
and control the future
Increasing levels of control over nature would improve our
quality of life
Newton & the Machine Metaphor
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In science
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the search for the basic building blocks
In management
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The whole is no more or no less than the sum of parts, so
focus on the parts (e.g. functions, disciplines)
Organizations and people are implicitly viewed as
machines (or machine parts)
Roots Of Complexity
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Santa Fe Institute
Physics-chaos theory
Math-fractal geometry
Meteorology-butterfly effect
Biology-complex adaptive systems
From Physics Envy To Biology Envy
Surprising Convergence of Disciplines
Chemistry
Psychology
Physics
Computer
Science
Mathematics
Biology
Meteorology
Ecology
Sociology
Economics
Surprising Convergence:
We Stand on the Shoulders of Giants
Chemistry
Physiology
Ary Goldberger, Cardiac Research
Physics
David Bohm, Wholeness
& the Implicate Order
Robert Alexrod, Complexity of Cooperation
Complex Adaptive
Systems
Meteorology
Edward Lorenz, The Butterfly Effect
Philosophy
Ken Wilbur, Integral Science & Religion
Ecology
Sociology
Ilya Prigogine, Order Out of Chaos
James Lovelock, Gaia Hypothesis
Physics-Ecology
Fritjof Capra, Web of Life
((( Murray Gell-Mann )))
The Quark & the Jaguar
Socio-Biology
E.O. Wilson Consilience
((( Stuart Kaufmann )))
At Home in the Universe
Computer Science
((( John Holland )))
Emergence
Christopher Langton
((( Brian Arthur )))
Increasing Returns
Genetics
R.C. Lewontin, Biology as Ideology
Mathematics
Mandlebrot, Fractals
More Giants
Complexity applied to organizations
Strategy/Leadership
Ralph Stacey
Market Strategy
Leadership
Kevin Kelly
Gareth Morgan
Management
Leadership
Brenda Zimmerman
Meg Wheatley
Strategy
S. Brown & K. Eisenhardt
Sustainability
Paul Hawken/James Moore
Complex
Adaptive Systems
Planning
Henry Mintzberg
Etienne Wegner
Jeffery Goldstein
Mass Customizing
Org Development
Martha Rogers
David Cooperrider
Roger Lewin/Birute Regine
Everett Rogers
Learning
Management
Org Dynamics
Innovation
People Practices
Jeffery Pfeffer
Knowledge
Ikujiro Nonaka
Inspiration from
Complex Adaptive Systems
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Definition: A collection of individual agents,
who have the freedom to act in unpredictable
ways, and whose actions are interconnected
such that one agent’s actions changes the
context for other agents.
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Examples: termite colonies, stock markets, the
Internet, gardens, human beings, groups of people
Defining
Complex Responsive Systems
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Alternative CAS definition by Ralph Stacey:
CASs consist of a network of agents that
interact with each other according to a set of
rules that require them to examine and
respond to each other’s behavior to improve
their behavior and thus the behavior of the
system they comprise.
Interdependent Attributes
Natural
Emergence &
Creativity
Adaptable
Elements
Order w/o
Central
Control
Not Predicable
in Detail
Simple
Rules
Embedded
Systems
Co-Evolution
Non-Linearity
Attributes of Complex Adaptive Systems
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Elements of the system change themselves (they adapt)
Complex behaviors can emerge from a few simple rules that are
applied locally
Emergence of novelty & creativity is a natural state
Order emerges without central control
Non-linearity: small changes can have BIG effects
Systems are embedded in systems & their interdependency
matters
Not predictable in detail: forecasting is an inexact, yet boundable,
art
Co-evolution of life proceeds through constant tension & balance
Living Systems Are Non-Linear
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Not predictable in long-term
Future not just unknown but unknowable
Small events may trigger huge effects
Huge efforts may have negligible effects
Examples Of Non-Linearity
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Rosa Parks’ refusal to yield her seat
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Weather, hurricanes
 A statement
Greenspan
or word used by Alan
Stacey Diagram
Know When Your Challenges Are In the Zone of Complexity
Chaotic
Close to
Seek Patterns
Simple
Plan, control
Close to
Certainty
Far from
When Complexity Practices Are Useful
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When you are frustrated with current and
past approaches
When challenges are wicked and messy
When you want to start something new
When there is little agreement or certainty
about how to respond *
* See the Zone of Complexity in Ralph Stacey’s diagram
Nine Interdependent Principles
Complexity Lens
Good Enough
Vision
Chunking
Clockware/
Swarmware
Tune To
The Edge
Competition/
Cooperation
Shadow System
Seek
Paradox
Multiple Actions
Seeing Through A Complexity Lens
Simple Rules in Practice
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Living systems follow “simple rules”
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Craig Reynolds’ “Boids” simulation uses minimum
rules of interaction
Gareth Morgan’s “min specs”
Simple rules include “Must do’s” or “Never do’s”
Example: Reynolds’ Steering Rules
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Maintain a minimum distance from other
boids and objects
Match speed of neighboring boids
Move toward the center of mass of flockmates in your area
Complex “flocking” emerges!
The 15% Principle
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W. Edwards Deming suggested that everyone -- from the
CEO to the front line worker -- has influence over 15% of
their system. The other 85% is beyond their discretionary
control.
Recognize that you have 15% discretionary influence… it
may sound small but you can use it to make a difference
that makes a difference.
Marry 15% principle with Multiple Actions At The
Fringes – Let Direction Emerge
Tune Your Place To The Edge
Close to
Chaotic
Simple
Close to
Certainty
Far from
“Farmers don’t grow crops. They create the
conditions for crops to grow.”
Gareth Morgan