From swarming to collaborative filtering.

1. From swarming to collaborative filtering. http://www.csml.ucl.ac.uk/images/Netflix_Prize.jpg

2. Informatics: a possible parsing Health- HCID Security Geo- Data Mining Bio- Data & Search Social Informatics Complex Systems • towards problem solving • beyond computing • into the natural and social • synthesis of information technology Music- Chem-

3. b b b a a a b a b b a a b a b Psilophyta/Psilotum Let’s Observe Nature! What do you see? • Plants typically branch out • How can we model that? • Observe the distinct parts • Color them • Assign symbols • Build Model • Initial State: b • b -> a • a -> ab • Doesn’t quite Work! a b

4. Complex systems approach: looking at nature • A complex system is any system featuring a large number of interacting components (agents, processes, etc.) whose aggregate activity is nonlinear • not derivable from the summations of the activity of individual components • Network identity: Components form aggregate structures or functions that requires more explanatory devices than those used to explain the components • Genetic networks, Immune networks, Neural networks, Social insect colonies, Social networks, Distributed Knowledge Systems, Ecological networks • Bottom-up Methodology • Collections of simple units interacting to form a more complex hole • Study of Simple Rules that Produce Complex Behavior • Discovery of Global Patterns of behavior

5. b b b a a a b a b b a a b Psilophyta/Psilotum a b What about our plant? • An Accuratemodel requires • Varying angles • Varying stem lengths • Randomness • The Fibonacci Model is similar • Sneezewort: a b

6. Fibonacci Numbers! • Rewritingproduction rules • Initial State: A • A -> B • B -> AB • n=0 : A • n=1 : B • n=2 : AB • n=3 : BAB • n=4 : ABBAB • n=5 : BABABBAB • n=6 : ABBABBABABBAB • n=7 : BABABBABABBABBABABBAB • The length of the string is the Fibonacci Sequence • 1 1 2 3 5 8 13 21 34 55 89 ... • Fibonacci numbers in Nature • Livio (2003) The Golden Ratio: The Story of PHI, the World's Most Astonishing Number

7. Another example: flocking in nature • Flocking occurs when large groups of animals of the same species form aggregates that behave like a coherent, single entity • Herds, flocks, schools, swarms, humans • Properties: • Collectiveflight, migration, foraging, “drafting” • Coherence: aggregate has its own distinguishable system behavior and form • Adaptive: behavior of aggregate responds and adapts to external events (predators) • Coordination: behavior of individuals seems to be indicative of central controlor symbolic/long-range communication, but isn’t

8. How to model flocking behavior? • Describing properties of aggregate behavior will only go so far: • Study shapes of aggregate • Situations in which it occurs • Dynamics, features of behavior • Biologists fixing radios? • Lessons from complex systems: • Complex systems behavior: not derivable from the summations of the activity of individual components • Network identity: Components form aggregate structures or functions that requires more explanatory devices than those used to explain the components ~ emergence • Bottom-up Methodology: • Collections of simple units interacting to form a more complex hole • Study of Simple Rules that Produce Complex Behavior Parrish(2002) – Self-organized fish schools

9. Models of flocking behavior • Boids: Craig Reynolds “Flocks, Herds and schools”, SIGGRAPH 21(4),1987 • Visual model of bird flocks • Lack of centralized control • Lack of symbolic communication • General approach: Local computation, i.e. each individual maximizes: • Collision avoidance: steer away from impact • Velocity matching: match speed of neighboring birds • Flock centering: steer towards perceived flock center • Flock behavior = emerges from interactions of large groups of such construed individuals

10. Ant trails: emergent organizaton driven by communication • Problem: optimize location and extraction of food source • Lack of centralized control • Lack of symbolic communication • General modeling approach: • Local computation leads to higher order emergent computation • Walk algorithm probabilistic, but biased by pheromone concentraion • Ants leave pheromone trail when food is found • Pheromone evaporates with time • Find shortest path • Note: • ~ greedy algorithm: hill-climbing on trail strength leads to adaptive, collective behavior • Approaches to address traveling salesman problem: BIOS group: S. Kaufmann (Santa Fe), see also M. Dorigo(2006) Ant Colony Optimization-IEEE Computational Intelligence Magazine for overview

11. Probabilistic cleaning: ants • Very simple rules for colony clean up • Pick dead ant. if a dead ant is found pick it up (with probability inversely proportional to the quantity of dead ants in vicinity) and wander. • Drop dead ant. If dead ants are found, drop ant (with probability proportional to the quantity of dead ants in vicinity) and wander. See Also: J. L. Deneubourg, S. Goss, N. Franks, A. Sendova-Franks, C. Detrain, L. Chretien. “The Dynamics of Collective Sorting Robot-Like Ants and Ant-Like Robots”. From Animals to Animats: Proc. of the 1st Int. Conf. on Simulation of Adaptive Behaviour. 356-363 (1990). Figure by Marco Dorigo in Real ants inspire ant algorithms

12. Ant-inspired robots • Rules (Becker et al, 1994) • Move: with no sensor activated move in straight line • Obstacle avoidance: if obstacle is found, turn with a random angle to avoid it and move. • Pick up and drop: Robots can pick up a number of objects (up to 3) • If shovel contains 3 or more objects, sensor is activated and objects are dropped. Robot backs up, chooses new angle and moves. • Results in clustering • Theprobabilityofdroppingitemsincreaseswithquantityofitems in vicinity Figure from R Beckers, OE Holland, and JL Deneubourg [1994]. “From local actions to global tasks: Stigmergy and collective robotics”. In Artificial Life IV.

13. becker et al experiments

14. Luc Steels et al: ant algorithms http://www.youtube.com/watch?v=93LwvuxDbfU

15. Adaptive information systems Swarm Smarts. 78. Scientific American March 2000. ERIC BONABEAU Johan Bollen (1994): adaptive hypertext systems

16. Shameboy Plastic Operator [Shameboy, Plastic Operator, Figurine,…] Buyer 1 [1, 1, 0, 0, 0,…] Buyer 2 [1, 0, 0, 0, 0,…] Recommender systems: general principles • People ~ n-dimensional vectors • Person = { CD/book purchases, DVDs rented, …} • Vector is a representation of consumer. Entries can be weighted (TFIDF etc) • “Vector Space Model” • Calculate similarity of users: • Correlation of user vectors • Cosine similarity • Group consumers according to similarity: clustering • Similar users: discrepancies in vectors are recommendations • Used for all sorts of applications • Similar problem to “bad of words” • Multiple user personalities? • Orthogonality? • Same = better?? Angle: Consumer Similarity

17. Tracking scientists (they are people too!) http://informatics.indiana.edu/jbollen/PLosONEmap André Skupin Borner/Ketan (2004) PNAS 101(1) Highly recommended: http://www.scimaps.org/

18. We’re all ants now? • User vectors: • Represent individual trail/exploration in n-dimension information space • Recommender systems: • bias probabilistic exploration paths of users based on others’ actions • Higher probability of following existing trails • Analogy: • Set of user vectors + recommender system ~ ant trails • Solving traveling salesman in n dimensions? ;-) • Modeling fads, hypes, flashcrowds in cyberspace, self-fulfilling prophecies, but also long tail effects, more optimized exploration of information space? • Which features of recommender systems promote either of the above? • Cf. youtube.com: “other users are watching” vs. batch-processed recommendations documents recommender interface

19. Readings: Questions: - Atlantic (2009) “Is google making us stupid”: As a scientist how would you falsify Carr’s theory that “google is changing the way we think”? Has google changed the way you think? (notions of sampling, plagiarism, etc) - Bettencourt (2008), PNAS: The proposed model results in a scenario in which cities undergo cycles of expansion followed by crisis as a result of the exhaustion of resources. Cycle length shortening with each generation. Speculate: where does this process “break”? What’s a way out?

20. Next week readings Gouth (2009) Training for Peer Review. Science Signaling 2 (85), tr2. [DOI: 10.1126/scisignal.285tr2] MONASTERSKY (2005) The number that is devouring science. Chronicle of higher education, Section: Research & Publishing Volume 52, Issue 8, Page A12 Eysenbach G, 2006 Citation Advantage of Open Access Articles. PLoSBiol 4(5): e157. doi:10.1371/journal.pbio.0040157 Lance Fortnow (2009) Time for Computer Science to Grow Up. Communications of the ACM, august, 52(8)doi:10.1145/1536616.1536631