1. 06-04-09 The Interesting In Between: Why Complexity Exists Scott E Page University of Michigan Santa Fe Institute scottepage@gmail.com

2. 06-04-09 Background Reading

3. 06-04-09 Outline Attributes Properties The Interesting In Between Why Complexity Conclusions

4. 06-04-09 Complex Adaptive Systems: Attributes

5. 06-04-09 Complex Adaptive Systems Networks Source: MIT

6. 06-04-09 Complex Adaptive Systems Networks Adaptation Source: Exploring Nature

7. 06-04-09 Complex Adaptive Systems Networks Adaptation Interactions Source: Uptodate.com

8. 06-04-09 Complex Adaptive Systems Networks Adaptation Interactions Diversity Source: Scientific American

9. 06-04-09 Complex Adaptive Systems: Properties

10. 06-04-09 Complex ≠ Complicated

11. 06-04-09 Complex ≠ Equilibrium (w/ Shocks)

12. 06-04-09 Complex ≠ Chaos Source: Andrew Russell

13. 06-04-09 Complex ≠ Difficult Source: Biology-direct

14. 06-04-09 Complex = Dancing Landscapes Source: Chris Lucas

15. 06-04-09 Epi-Phenomena Emergence Structures and Levels Source: Boortz.com

16. 06-04-09 Diffusion Limited Aggregation Start with a seed on a plane. Create drunken walkers who start from a random location and walk in random directions until touching the seed, at which point the walkers become immobilized. Witten and Sander (1981)

17. 06-04-09 Diffusion Limited Aggregation seed walker

18. 06-04-09 Diffusion Limited Aggregation

19. 06-04-09 Conway’s Game of Life X5 76 4 1 23 8 Cell has eight neighbors Cell can be alive Cell can be dead Dead cell with 3 neighbors comes to life Live cell with 2,3 stays alive

20. 06-04-09 Examples X

21. 06-04-09 Bigger Space

22. 06-04-09 A New Kind of Science - Wolfram Binary state objects arranged in a line using simple rules can create - “perfect’’ randomness - chaos - patterns - computation

23. 06-04-09 Epi-Phenomena Emergence Structures and Levels Emergent Functionalities Source: Biology-direct

24. 06-04-09 Wolfram’s 256 Automata N X

25. 06-04-09 Rule 90 N X 2 8 16 64 Sum = 90

26. 06-04-09 Rule 90 N X 2 8 16 64 Sum = 90

27. 06-04-09 Four Classes of Behavior

28. 06-04-09 Emergent Computation Source: U of Indiana

29. 06-04-09 Epi-Phenomena Emergence Structures and Levels Emergent Functionalities Innovation http://media-2.web.britannica.com/eb-media/54/4054- 004-F5EB3891.jpg

30. 06-04-09 Epi-Phenomena Emergence Structures and Levels Emergent Functionalities Innovation Large Events

31. 06-04-09 A Long Tailed Distribution

32. 06-04-09 A Long Tailed Distribution cities size words citations web hits book sales phone calls earthquakes moon craters wars net worth family names

33. 06-04-09 Large Events Source: www2002

34. 06-04-09 Per Bak’s Sandpile sand table floor

35. 06-04-09 Per Bak’s Sandpile sand table floor

36. 06-04-09 Self Organized Criticality Systems may self organize into critical states. If so “events” may not be normally distributed. They may instead have long tails. Small events could have enormous consequences.

37. 06-04-09 Epi-Phenomena Emergence Structures and Levels Emergent Functionalities Innovation Large Events Robustness Source: NBC

38. 06-04-09 “Imagine how difficult physics would be in electrons could think.” -Murray Gell-Mann

39. 06-04-09 Robustness: The World of Thinking (or adapting) Electrons

40. 06-04-09 The Langton Graph

41. 06-04-09 The Langton Graph

42. 06-04-09 A Thought Play A Simple Model of Forest Fires & Bank Failures

43. 06-04-09 The Bank Model Banks choose to make a risky loan each period with probability p

44. 06-04-09 The Bank Model Banks choose to make a risky loan each period with probability p Risky loans fail with probability q but have a higher yield

45. 06-04-09 The Bank Model Banks choose to make a risky loan each period with probability p Risky loans fail with probability q but have a higher yield Failures spread to neighboring banks only if those banks have a risky loan outstanding

46. 06-04-09 The Forest Fire Model Trees choose to make a grow each period with probability p Trees get hit by lightening with probability q Fire spreads to neighboring locations only if those locations have a tree

47. 06-04-09 Example Period 1: 00R00R000RR0R Period 2: R0R00R00RRRRR

48. 06-04-09 Example Period 1: 00R00R000RR0R Period 2: R0R00R00RRRRR Period 3: R0R00R00FRRRR Period 4: R0R00R00FFFFFF Period 5: R0R00R00000000

49. 06-04-09 Key Insight Revisited Bank managers should be smarter than trees!

50. 06-04-09 Forest Fire Model Results Yield increases in p up to a point and then falls off rather dramatically Physicists call this a ``phase transition’’

51. 06-04-09 Poised at the ‘edge of chaos’ rate of risky loans p* yield

52. 06-04-09 Smarter Banks Let each bank learn (using a standard learning rule from psychology called Hebbian learning) whether or not to make risk loans.

53. 06-04-09 Emergent Robustness rate of risky loans p* yield

54. 06-04-09 Emergence of Firewalls 11101101110111100111

55. 06-04-09 The Interesting In Between

56. 06-04-09 The Barn Mutation/Adaptation Network Big Area Real Novelty InteractionDiversity

57. 06-04-09 Tuning Complexity

58. 06-04-09

59. Rates of Adaptation/Learning 0: - rule aggregation 11: - rational expectations

60. 06-04-09 Interdependencies 0: - decision theory 11: - mangle

61. 06-04-09 Network/Connectedness Mathematically tractable models: N = 2 -game theory N = Infinity -averaging - random mixing

62. 06-04-09 Rock, Paper, Scissors Rock: All DToxic E Coli Paper:TFTResistant E Coli Scissors:All CSensitive E Coli

63. 06-04-09 Rock, Paper, Scissors Rock: All DToxic E Coli Paper:TFTResistant E Coli Scissors:All CSensitive E Coli Simulations: we get “ stone soup” but diversity on a lattice or a line

64. 06-04-09 Rock, Paper, Scissors Rock: All DToxic E Coli Paper:TFTResistant E Coli Scissors:All CSensitive E Coli Real Experiments: we get one type E Coli in a flask but diversity on a slide Kerr, Riley, Feldman, Bohannan (Nature 2002)

65. 06-04-09 alone lattice network soup complexity complexity

66. 06-04-09 Diversity 0: - representative agent model 11: - statistical averaging

67. 06-04-09 A complex adaptive system requires the right amount of “interplay” between our agents.

68. 06-04-09 The Small Barn Mutation Network Equilibrium InteractionDiversity

69. 06-04-09 The Big Barn Mutation Network Lack of Structure: Limiting Distributions Interaction Diversity

70. 06-04-09 The Interesting in Between Mutation Network Complex InteractionDiversity

71. 06-04-09 Why Complexity?

72. 06-04-09 Emergent Complexity A lurking theory of homeocomplexus: learning rates, interaction effects, networks, and diversity adjust to maintain complexity.

73. 06-04-09 Enlarging the Barn If the barn is small, the system is often both stable predictable. These two properties create an opportunity for faster adaptation – this can mean a new action (diversity), a new connection, or a new interdependency.

74. 06-04-09 Shrinking the Barn If the barn is big, the system tends to be either a mangle or random. Either state creates an incentive for simple strategies, fewer connections, less diversity, and reducing interdependencies.

75. 06-04-09 Dali’s Barn Mutation Network Big Area Real Novelty Interaction Diversity

76. 06-04-09 Conclusions

77. 06-04-09 Big Successes Crowds and Panics Spatial Segregation Residential Location Transportation Networks Internet Structure Scaling Laws

78. 06-04-09 We Have No Choice Global Warming Global Financial Markets Disease Transmission Transportation Internet Terrorism Networks Crime

79. 06-04-09 Normal Science Step 1: Construct a Model Step 2: Produce Hypotheses Step 3: Test the Hypotheses

80. 06-04-09 All New Non Analytic Ensembles of Models Interactions of disciplines in same model