1. Complexity John Paul Gonzales Santa Fe Institute // Project GUTS / Supercomputing Challenge Betsy Frederick Silicon Desert Consulting // Project GUTS / Supercomputing Challenge betsy.frederick@gmail.com Scientifically Connected Communities 31 May 2011 and 20 June 2011

2. Complexity: Agenda/Outline “Sunday Morning Thoughts” History Definitions via history and student examples Fields “Monday Morning Ideas” Paradigm Shift in Science Implications for Science Education – Computational Thinking Classroom Tools

3. Definitions Complexity …No Easy Definition. Yet.

4. Definitions “ An intelligence which, at a given instant knew all the forces acting in nature and the position of every object in the universe – if endowed with a brain sufficiently vast to make all necessary calculations – could describe with a single formula the motions of the largest astronomical bodies and those of the smallest atoms. To such an intelligence, nothing would be uncertain; the future, like the past, would be an open book. “A Philosophical essay on Probabilities,” 1795 -Pierre-Simon Laplace

5. Definitions Age of Determinism

6. Definitions Age of Determinism

7. Definitions Flickr.com/practicalowl

8. Definitions

9. →

10. …But Does It Work?

11. Does it Work? Yes: http://www.grc.nasa.gov/

12. Does it Work? Yes: =

13. But what about flocking? Does it Work? No. Flickr.com/FininEden

14. “Whole is greater than the sum of its parts.”

15. “Whole is greater than the sum of its parts” → Reynolds, 1986

16. Complexity: (My Definition) “The study of (large) systems made up of simple parts that, when following simple rules, produce, collective, unpredictable, and emergent behavior.”

17. “Whole is greater than the sum of its parts.”

18. “Have to play the game to see the outcome.” Let’s look at Boids.

19. What opportunities does a model like this offer us? Inquiry - What if? Science talk Experiments Students can build their own models based on examples.

20. Complex Systems Computer models are used by scientists to understand complex systems and possibly prevent (or study interventions for) daunting problems such as epidemics and other problems that define the 21 st century. Climate change, loss of biodiversity, energy consumption and virulent disease affect us all (Emmott et al., 2006). (Irene Lee)

21. http://www.nsf.gov/news/mmg/media/images/crowded_world1_h.jpg Emergency Egress Model: Think of school emergency exit plans.

22. Melanie Mitchell asks, How … do insect colonies, composed of thousands to millions of individual insects, collectively make decisions and accomplish complex tasks that seem to require the communication and processing of colony ‐ wide information? How does the immune system, composed of trillions of cells and molecular components circulating in the body, collectively recognize patterns of infection and other organism ‐ wide conditions, and collectively decide how to mount an appropriate response?

23. One good illustration of this is the process of task allocation in ant colonies. In an ant colony, ants take on different specialized tasks, such as foraging for food, nest maintenance, patrolling the nest, and refuse ‐ sorting. Ants do not always stick to the same task; instead they often switch tasks as needed, depending on the current state of their environment. Each ant has a limited view of the global nest environment, limited contact with other ants, and no central “controller” issuing commands as to what task to pursue. How do ants decide what task to take on at a given time so that the colony as a whole will have an optimal allocation of workers to various tasks, given that the optimal allocation continually changes?

24. Monday Morning

25. Why do I want to do this?

26. It’s powerful.

27. Why do I want to do this? It’s intuitive. Vs. Lotka-Volterra Competition Model

28. Why do I want to do this? It’s intuitive. Vs. Lotka-Volterra Competition Model Wolves/Sheep ABM Find this example in the NetLogo Library

29. Complex Systems are sometimes called Complex Adaptive Systems → Many agents following simple rules Leaderless Emergent, self-organizing behavior Dynamic environment Difficult to predict

30. Time for one more example?

31. A “Times Change” Observation

32. Adding in Roman Numbers: I, V, X, L, C, D, M IV, IX, XL, XC CD, CM…

33. Adding in Roman Numbers (http://turner.faculty.swau.edu/mathematics/materialslibrary/roman/) CCCLXI + DCCCXLV CCCLXVIII + DCCCXXXV DCCCCCCLXXXXXVVIIII DCCCCCCLLXIIII DCCCCCCCXIIII DCCXIIII MCCCXIIII MCCXIV

34. OR: 369 + 845 = 1214

36. References/Readings Epstein, Joshua. “Why Model?”Lecture, 2008. http://www.mit.edu/~scienceprogram/Materials/Monday%20Materials/WhyModel.pdf Johnson, George. “All Science is Computer Science” New York Times, 2001. http://www.cs.iastate.edu/all-science-is- cs.html Mitchell, Melanie. Complexity, A Guided Tour. Portland University Press, 2009. Mitchell, Melanie. What is Computation? Bilolgical Computation. ACM Ubiquity Symposium. http://web.cecs.pdx.edu/~mm/BiologicalComputation.pdf http://web.cecs.pdx.edu/~mm/BiologicalComputation.pdf Netogo can be downloaded from Northwestern University Supercomputing Challenge: http://challenge.nm.orghttp://challenge.nm.org Project GUTS: http://projectguts.org

37. A “Real World” Example Wolves and Sheep

38. “Have to play the game to see the outcome.”

39. Iteration Randomness/Chance Evolution over Time

40. →