Simple, complicated or complex

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To develop foresight, one has to acknowledge and understand three different types of systems - simple, complicated and complex. This presentation will discuss an overall structure for the different types of systems and their characteristics together with examples. Paul will discuss the apparent trend towards more complex systems and why this trend may be true. He will provide some insight in how to think about complex systems.
Paul Schumann is a futurist, creative thinker, advisor and writer. He is a proponent and practitioner of collaborative approaches.
He has been a technologist and technology manager in the semiconductor industry (IBM), internal entrepreneur (IBM), cultural change agent (IBM), and consultant (Technology Futures and Glocal Vantage). With 50 years of professional experience, Paul is still excited about learning, and sharing what he is learning.
He is a blogger, writer of numerous articles and book chapters, and coauthor of two books (Innovate! and Superconductivity). Paul has been blogging since 2002 and as of this writing has posted 679 blogs on Insights-Foresight (http://insights-foresight.blogspot.com/ ).
Paul is a fan of web 2.0 technologies and has applied them to his own work, and to create market intelligence systems for clients. He is expecting to see their application in democracy. His interests also include media ecology and complexity.
He is the founder, past president and past member of the board of the Central Texas Chapter of the World Future Society. Paul was a member of the advisory boards of the Marketing Research Association, the Associated Chemistry Teachers of Texas and ACC’s Center for Community-based and Nonprofit Organizations. He is on the editorial board of On the Horizon journal, and was involved with Texas Forums and Extreme Democracy.

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Simple, complicated or complex

  1. 1. Paul Schumann CTEX WFS August 21, 2012
  2. 2. 1, 2, a Few, Many 1 2 A Few Many Newton Poincare Gauss Complicated Criticality & ChaosSimplicity Organized Complexity Disorganized Complexity Paul Schumann 2
  3. 3. The Present“It makes me happy. To be at the beginning again, knowing almost nothing… The ordinary-sized stuff which is our lives, the things people write poetry about – clouds – daffodils – waterfalls,,,these things are full of mystery, as mysterious to us as the heavens were to the Greeks…It’s the best possible time to be alive, when almost everything you thought you knew is wrong.”Tom Stoppard, Arcadia Paul Schumann 3
  4. 4. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 4
  5. 5. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 5
  6. 6. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 6
  7. 7. Examples Three Body Problem The Logistic Map: x t + 1 = R x t ( 1 – x t ) Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks R=2 R=4 Evolution x 0 = 0.99 x 0 = 0.2 Economics x0 = 0.200000000 01 Paul Schumann 7
  8. 8. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 8
  9. 9. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 9
  10. 10. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics N = a M -2 Paul Schumann 10
  11. 11. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Criticality Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Gaussian Markets Social Networks Evolution Economics Paul Schumann 11
  12. 12. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 12
  13. 13. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Termite Cathedral Paul Schumann 13
  14. 14. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 14
  15. 15. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics European Starlings Paul Schumann 15
  16. 16. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 16
  17. 17. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 17
  18. 18. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 18
  19. 19. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 19
  20. 20. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 20
  21. 21. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 21
  22. 22. ModelingMathematical Massively Parallel Derived  Simulation Analytical  Describe underlying Conditional mechanisms Equation: x = ½ a t2  Rule based agents in an Predictive environment  Exploration  Easier to add random or probabilistic events  Flexible & intuitive Paul Schumann 22
  23. 23. P=a C -2.1Examples 2007: corr=0.98 2008: corr=0.94 Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 23
  24. 24. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 24
  25. 25. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 25
  26. 26. Examples Three Body Problem Weather Logistic Graph Earthquakes/Hour Glass Forrest Fires Termite Castles Slime Mold Flocking/Schooling/Herding Body Rhythms Nature’s True Shapes Biological Growth Ecological Systems Markets Social Networks Evolution Economics Paul Schumann 26
  27. 27. Examples Three Body Problem “…the wealth of nations is driven by Weather productive knowledge. Individuals are Logistic Graph limited in the things they can effectively Earthquakes/Hour Glass know and use in production so the only Forrest Fires way a society can hold more knowledge is Termite Castles by distributing different chunks of Slime Mold knowledge to different people. To use the Flocking/Schooling/Herding knowledge, these chunks need to be re- Body Rhythms aggregated by connecting people through Nature’s True Shapes organizations and markets. The complex Biological Growth web of products and markets is the other Ecological Systems side of the coin of the accumulating Markets productive knowledge.” Social Networks Evolution Atlas of Economic Complexity Economics Paul Schumann 27
  28. 28. Examples Dispersed interaction —The economy has interaction between many dispersed, heterogeneous, agents. The action of any given agent depends upon the anticipated actions of other agents and on the aggregate state of the Three Body Problem economy. No global controller —Controls are provided by mechanisms of Weather competition and coordination between agents. Economic actions are mediated Logistic Graph by legal institutions, assigned roles, and shifting associations. No global entity Earthquakes/Hour Glass controls interactions. Traditionally, a fictitious auctioneer has appeared in some mathematical analyses of general equilibrium models, although nobody Forrest Fires claimed any descriptive accuracy for such models. Traditionally, many Termite Castles mainstream models have imposed constraints, such as requiring that budgets Slime Mold be balanced, and such constraints are avoided in complexity economics. Cross-cutting hierarchical organization —The economy has many levels Flocking/Schooling/Herding of organization and interaction. Units at any given level behaviors, actions, Body Rhythms strategies, products typically serve as "building blocks" for constructing units at Nature’s True Shapes the next higher level. The overall organization is more than hierarchical, with many sorts of tangling interactions (associations, channels of communication) Biological Growth across levels. Ecological Systems Ongoing adaptation —Behaviors, actions, strategies, and products are Markets revised frequently as the individual agents accumulate experience[8]. Novelty niches —Such niches are associated with new markets, new Social Networks technologies, new behaviors, and new institutions. The very act of filling a Evolution niche may provide new niches. The result is ongoing novelty. Economics Out-of-equilibrium dynamics —Because new niches, new potentials, new possibilities, are continually created, the economy functions without attaining any optimum or global equilibrium. Improvements occur regularly. Paul Schumann 28
  29. 29. Complexity, Emergence & Fractals The behavior of a complex system in dynamic Complexity equilibrium is chaotic, in non-equilibrium is critical Emergence is the way Equilibrium Non- complex systems and patterns equilibrium arise out of a multiplicity of relatively simple interactions Chaos Agents Criticality A fractal is an object or quantity that displays self- similarity on all scales. Some complex systems can Emergence Agents appear simple or complicated at some scales Adaptive Fractals Paul Schumann 29
  30. 30. Why Is Complexity Important? Ubiquitous Trans disciplinary “…the next century (21st) will be the century of complexity.” – Hawking “…the overarching challenge of our age will be managing modern complexity” – Beer “I am convinced that the nations and people who master the new sciences of complexity will become the economic, cultural and political superpowers of the next century (21st).” – Pagels “Complexity has created a bridge or a merger of quantitative and qualitative explanations of life.” - Zimmerman Paul Schumann 30
  31. 31. The Future Complexity More systems More interconnectedness in the systems The reach of a system is growing The number of people/things in a system is increasing The speed of interaction is increasing Therefore: More complexity  More chaos  More criticality  More emergence Paul Schumann 31
  32. 32. Paul SchumannPaul Schumann, PO Box 161475, Austin, TX 78716512.632.6586paschumann2009@gmail.comhttp://insights-foresight.blogspot.com/https://sites.google.com/a/schumann2020.com/paul- schumann/homehttp://www.twitter.com/innovant2003Want to go further? Contact me… Paul Schumann 32
  33. 33. Resources Complexity: A Guided Tour, Melanie Mitchell, Oxford, 2009 Simplexity, Jeffrey Kluger, Hyperion, 2008 The Black Swan: The Impact of the Highly Improbable, Nassim Nicholas Taleb, Random House, 2007 Adventures in Modeling: Exploring Complex, Dynamic Systems with StarLogo, Vanessa Stevens Colella, Eric Klopfer & Mitchel Resnick, Teachers College press, 2001 Emergence: The Connected Lives of Ants, Brains, and Software, Steven Johnson, Scribner,2001 Ubiquity: Why Catastrophes Happen, Mark Buchanan, Three Rivers Press, 2000 Turtles, Termites and Traffic Jams: Explorations in Massively Parallel Microworlds, Mitchel Resnick, MIT, 1997 Chaos Gaia Eros, Ralph Abraham, Harper, 1994 Tao of Chaos: Merging East and West, Katya Walter, Kairos Center, 1994 Complexity: The Emerging Science at the Edge of Order and Chaos, M. Mitchell Waldrop, Touchstone, 1992 Exploring Complexity: An Introduction, Gregoire Nicolis & Ilya Prigogine, Freeman, 1989 Chaos: Making a New A Science, James Gleick, Penguin, 1987 Godel, Escher, Bach: An Eternal Golden Braid, Douglas Hofstadter, Vintage, 1980Go to Insights and Foresight Blog (http://insights-foresight.blogspot.com/) and search forcomplexity. All the resources listed above have blog entries and or links. Paul Schumann 33
  34. 34. This work is licensed under the Creative Commons Attribution license. You may distribute, remix, tweak, and build upon thiswork, even commercially, as long as you credit me for the original creation as Paul Schumann. Paul Schumann 34

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