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Introduction to Self Organisation

Introduction to Self Organisation. Competition. Content. Introduction Historic Perspective Related Subjects Basics Summary and Discussion. Historic Perspective. Economics Biology Sport War Computing Brains.

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Introduction to Self Organisation

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  1. Introduction to Self Organisation • Competition

  2. Content • Introduction • Historic Perspective • Related Subjects • Basics • Summary and Discussion

  3. Historic Perspective • Economics • Biology • Sport • War • Computing • Brains

  4. "For unto every one that hath shall be given, and he shall have abundance: but from him that hath not shall be taken away even that which he hath." (Matthew XXV:29, King James Version.) Saint Matthew and the Angel By Rembrandt The Matthew Effect

  5. The Invisible Hand Karl Marx (1818-1883) Economics I Adam Smith (1723-1790) The Wealth of Nations Das Kapital

  6. John Nash (1928- ) Thomas Schelling (1921- ) Economics II Milton Friedman (1912-2006)

  7. Herbert Spencer (1820-1903) Charles Darwin (1809-1882) Biology I Jean-Baptiste Lamarck (1744-1829) Survival of the fFittest

  8. Frog Spawn Mushroom Spores Bur Oak Biology II Competition Starts Early Semen Cormorants Nesting (aalscholvers)

  9. New York Marathon Sports Pierre de Coubertin (18631937) Aphrodisias

  10. Carl von Clausewitz (1780-1831) War Arms Race Sun Tzu (544 BC- 496 BC)

  11. John Holland (1929- ) Evolutionary Algorithms Computing DNA Computing

  12. Hierarchical Temporal Memory Adaptive Neural Networks Brains Jeff Hawkins Neural Network in Action

  13. Related Subjects • Auctions • Prediction Markets • Wisdom of Crowds

  14. Auctions • Auctions • English • Dutch • Chinese • French • Swiss • Swedish • English Closed • Partipants • Supply • Demand • Double • Bids • Open • Closed • Reservation Price

  15. English Dutch IPO Auctions I Sealed Bid Dutch

  16. Chinese Auctions II Swiss Swedish

  17. French William Vickrey (1914-1996) Auctions III Leon Walrass (1834-1910) Vickrey Auction

  18. Prediction markets • Information • gets aggregated • dynamically, continuously • to make predictions

  19. Prediction markets • Iowa’s Electronic Market IEM • Hollywood Stock Exchange • Newsfutures • Tradesports • Hedgestreet

  20. James Surowiecki (1960- ) The Wisdom of Crowds I Francis Galton’s Ox Weighing experiment Francis Galton (1822-1911)

  21. Thomas Bayes (1702-1761) Wisdom of Crowds II USS Scorpion (1958-1968) Jellybeans

  22. Basics • Independence • Information Cascade • Imitation • Decentralisation • Information • Working together in Competition • Emergenge • Demand and Supply • Evolution • Genetic Algorithm • DNA Computing • The Difference

  23. Information Cascade Imitation Herding Also Fund Managers Herd Scharfstein and Stein Independence Looking up Milgram, Bickman and Berkowitz

  24. The Urn Game Bubbles also take place if people know for sure what the value of a share is. If price rises above max value, students go in for a small ride and expect to go out again before the end of the game. Information Cascade Colin Camerer, Caltech Internet Bubble

  25. Washing Wheat Orchid and Bee Imitation Macaque on Koshima Island Blue Tit

  26. Decentralisation • Information • Working together in Competition John Nash (1929- ) Nash Equilibrium (Wikipedia) Schelling Point If each player has chosen a strategy and no player can benefit by changing his or her strategy while the other players keep theirs unchanged, then the current set of strategy choices and the corresponding payoffs constitute a Nash equilibrium.

  27. Aggregated Shared Information Tacit Personalised

  28. Working together in Competition Text

  29. Emergence • Demand and Supply • Evolution • Genetic Algorithms • DNA Computing

  30. Sellers had a minimum price. Buyers had a maximum price Both prices were hidden for others. Both wanted better deals. Market reached equilibrium. Vernon Smith (1927- ) Demand and Supply Classroom Experiment Price Emerges

  31. Life Emerges Variety- Speciation Evolution Selection Heredity

  32. Pseudo-code algorithm: • 1. Choose initial population • 2. Evaluate the fitness of each individual in the • population • 3. Repeat • 1. Select best-ranking individuals to reproduce • 2. Breed new generation through crossover and • mutation (genetic operations) and give birth • to offspring • 3. Evaluate the individual fitnesses of the • offspring • 4. Replace worst ranked part of population with • offspring • 4. Until termination John Holland (1929- ) TSP Example Application - Areas - Types Evolver Genetic Algorithm

  33. Genetic Representation Order of Cities: 354671829 Crossover "Greedy crossover selects the first city of one parent, compares the cities leaving that city in both parents, and chooses the closer one to extend the tour. If one city has already appeared in the tour, we choose the other city. If both cities have already appeared, we randomly select a non-selected city." Example: 354671829 and 346752189 give 345671829 or 354672189 • Mutation • "The basic idea of greedy-swap is to randomly select two cities from one chromosome and swap them if the new (swapped) tour length is shorter than the old one" • Example: 354671829 could become: • 384671529 TSP Example

  34. Application Areas • Materials Engineering • Mathematics and Algorithmics • Military and Law Enforcement • Molecular Biology • Pattern Recognition and Data Mining • Robotics • Routing and Scheduling • Systems Engineering • Acoustics • Aerospace Engineering • Astronomy and Astrophysics • Chemistry • Electrical Engineering • Financial Markets • Game Playing • Geophysics

  35. Application Types (Representation) • Set Membership (Binary) • Function Optimisation(Real Valued) • Database Mining (Tree-Based) • Construction Optimisation (Order Based)

  36. Adleman decided to implement the following algorithm for finding a Hamiltonian path through n vertices: I. Generate a set of random paths through the graph. II. For each path in this set do the following: A. If the path starts at the designated start vertex and ends at the designated end vertex, keep it. If not, remove it from the set. B. If the path passes through exactly n vertices, keep it. If not, remove it from the set. C. If the path passes through every intermediary vertex (all vertices except the start and end ones) in the graph, keep it. If not, remove it from the set. III. If the set is not empty, then there exists a Hamiltonian path. If it is empty, then there exists no Hamiltonian path Leonard Adleman (1945- ) DNA Computing Hamiltonian Path

  37. Path Forming Hamiltonian Path Problem Alderaan : 1a1b Bespin : 2a2b Dagobath : 3a3b Hoth : 4a4b Execution Hamiltonian Path Problem

  38. II A Polymerase Chain Reaction PCR Machine II C II B Execution II Text Affinity Separation

  39. The Difference • Perspective • Heuristics • Measuring Sticks and ToolBoxes • Interpretations • Predictive Models • Diversity • Put Diversity to Work

  40. Sum of Fifteen Magic Square Perspective IThree games Tic Tac Toe

  41. Realistic Random Perspective II Base 10 Gray Coding 1 to 1 Mapping

  42. Messuring Sticks Cards Ladders Trees Interpretations Projection Lumping Predictive Models Traveling Salesman Problem Opt 2 Heuristic

  43. Diversity • Small Groups • Why Society needs Dissent • Diversity Trumps Ability • Put Diversity to Work

  44. Social psychologist Clark McCauley's three conditions under which groupthink occurs: * Directive leadership * Homogeneity of members' social background and ideology. * Isolation of the group from outside sources of information and analysis. Text Small Groups Vote More Talk Less Bay of Pigs Challanger

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