Simplicity on the Other Side of Complexity

1. Simplicity on the Other Side of Complexity An Introduction to Complexity Science, Management & Health Care

2. We are finely tuned “complex adaptive systems,” especially when we are working at our highest intelligence & purpose. 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 Complexity Lens Reflection

3. Heart Rate Dynamics

4. Blood Cell Dynamics

5. EEG Dynamics

6. Substantial gains in performance - 40% - have been documented in productivity, quality, value. “What matters is managers’ point of view.” “…confronting how we think about work, organizations, and the people in them.” Pfeffer, The Human Equation Looking For Success In The Wrong Places

7. Tom PetzingerWall Street Journal • “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.

8. Scientific Origins

9. Before Complexity • 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

10. Newton & the Machine Metaphor • In science • the search for the basic building blocks • In management • 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)

11. Roots Of Complexity • Santa Fe Institute • Physics-chaos theory • Math-fractal geometry • Meteorology-butterfly effect • Biology-complex adaptive systems

12. From Physics Envy To Biology Envy

13. Surprising Convergence of Disciplines Chemistry Computer Science Biology Mathematics Psychology Sociology Physics Economics Meteorology Ecology

14. Surprising Convergence:We Stand on the Shoulders of Giants Chemistry Ilya Prigogine, Order Out of Chaos Sociology Robert Alexrod, Complexity of Cooperation Physiology Ary Goldberger, Cardiac Research Complex Adaptive Systems ((( Murray Gell-Mann ))) The Quark & the Jaguar ((( Stuart Kaufmann ))) At Home in the Universe ((( John Holland ))) Emergence ((( Brian Arthur ))) Increasing Returns Physics-Ecology Fritjof Capra, Web of Life Physics David Bohm, Wholeness & the Implicate Order Socio-Biology E.O. Wilson Consilience Meteorology Edward Lorenz, The Butterfly Effect Computer Science Christopher Langton Philosophy Ken Wilbur, Integral Science & Religion Genetics R.C. Lewontin, Biology as Ideology Ecology James Lovelock, Gaia Hypothesis Mathematics Mandlebrot, Fractals

15. More Giants Complexity applied to organizations Strategy/Leadership Ralph Stacey Market Strategy Kevin Kelly Leadership Gareth Morgan Complex Adaptive Systems Management Brenda Zimmerman Leadership Meg Wheatley Strategy S. Brown & K. Eisenhardt Innovation Everett Rogers Sustainability Paul Hawken/James Moore Planning Henry Mintzberg Management Jeffery Goldstein Learning Etienne Wegner Mass Customizing Martha Rogers Org Development David Cooperrider Knowledge Ikujiro Nonaka Org Dynamics Roger Lewin/Birute Regine People Practices Jeffery Pfeffer

16. 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. Examples: termite colonies, stock markets, the Internet, gardens, human beings, groups of people Inspiration from Complex Adaptive Systems

17. 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. DefiningComplex Responsive Systems

18. Interdependent Attributes Adaptable Elements Natural Emergence & Creativity Simple Rules Order w/o Central Control Embedded Systems Co-Evolution Not Predicable in Detail Non-Linearity

19. 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 Attributes of Complex Adaptive Systems

20. Living Systems Are Non-Linear • Not predictable in long-term • Future not just unknown but unknowable • Small events may trigger huge effects • Huge efforts may have negligible effects

21. Examples Of Non-Linearity • Rosa Parks’ refusal to yield her seat • Weather, hurricanes • A statement or word used by Alan Greenspan

22. Stacey DiagramKnow When Your Challenges Are In the Zone of Complexity Far from Chaotic Seek Patterns Agreement Zone of Complexity Simple Plan, control Close to Far from Certainty Close to

23. 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 When Complexity Practices Are Useful

24. Nine Interdependent Principles Good Enough Vision Clockware/ Swarmware Complexity Lens Chunking Tune To The Edge Competition/ Cooperation Seek Paradox Shadow System Multiple Actions

25. Seeing Through A Complexity Lens

26. Simple Rules in Practice • Living systems follow “simple rules” • Craig Reynolds’ “Boids” simulation uses minimum rules of interaction • Gareth Morgan’s “min specs” • Simple rules include “Must do’s” or “Never do’s”

27. Example: Reynolds’ Steering Rules • Maintain a minimum distance from other boids and objects • Match speed of neighboring boids • Move toward the center of mass of flock-mates in your area • Complex “flocking” emerges!

28. 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 The 15% Principle

29. Tune Your Place To The Edge Far from Chaotic Info Flow Diversity Anxiety Agreement Simple Close to Far from Certainty Close to

30. “Farmers don’t grow crops. They create the conditions for crops to grow.” Gareth Morgan