From swarming to collaborative filtering.

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From swarming to collaborative filtering. http:// www.csml.ucl.ac.uk/images/Netflix_Prize.jpg. Informatics: a possible parsing. Health-. HCID. Security. Geo-. Data Mining. Bio-. Data & Search. Social Informatics. Complex Systems. towards problem solving beyond computing - PowerPoint PPT Presentation

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From swarming to collaborative filtering.

http://www.csml.ucl.ac.uk/images/Netflix_Prize.jpg

I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 20091Informatics:a possible parsingComplex SystemsData & SearchData MiningHCIDSocial InformaticsSecurityBio-Chem-Geo-Music-Health- towards problem solving beyond computing into the natural and social synthesis of information technology

I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 2009Lets Observe Nature!What do you see?Plants typically branch outHow can we model that?Observe the distinct partsColor themAssign symbolsBuild ModelInitial State: bb -> aa -> abDoesnt quite Work!

Psilophyta/Psilotum babbbbbbbbaaaaaaa

I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 20093

Complex systems approach: looking at natureA complex system is any system featuring a large number of interacting components (agents, processes, etc.) whose aggregate activity is nonlinearnot derivable from the summations of the activity of individual componentsNetwork identity: Components form aggregate structures or functions that requires more explanatory devices than those used to explain the componentsGenetic networks, Immune networks, Neural networks, Social insect colonies, Social networks, Distributed Knowledge Systems, Ecological networksBottom-up Methodology Collections of simple units interacting to form a more complex holeStudy of Simple Rules that Produce Complex BehaviorDiscovery of Global Patterns of behaviorI501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 20094What about our plant?An Accurate model requiresVarying anglesVarying stem lengthsRandomness

The Fibonacci Model is similarSneezewort:

Psilophyta/Psilotum babbbbbbbbaaaaaaa

I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 20095Fibonacci Numbers!Rewriting production rulesInitial State: AA -> BB -> ABn=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 Sequence1 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

I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 20096Another example: flocking in natureFlocking occurs when large groups of animals of the same species form aggregates that behave like a coherent, single entityHerds, flocks, schools, swarms, humansProperties:Collective flight, migration, foraging, draftingCoherence: aggregate has its own distinguishable system behavior and formAdaptive: behavior of aggregate responds and adapts to external events (predators)Coordination: behavior of individuals seems to be indicative of central control or symbolic/long-range communication, but isnt

I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 2009How to model flocking behavior?Describing properties of aggregate behavior will only go so far:Study shapes of aggregateSituations in which it occursDynamics, features of behaviorBiologists fixing radios?Lessons from complex systems:Complex systems behavior: not derivable from the summations of the activity of individual componentsNetwork identity: Components form aggregate structures or functions that requires more explanatory devices than those used to explain the components ~ emergenceBottom-up Methodology:Collections of simple units interacting to form a more complex holeStudy of Simple Rules that Produce Complex Behavior

Parrish(2002) Self-organized fish schoolsI501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 2009Models of flocking behaviorBoids: Craig Reynolds Flocks, Herds and schools, SIGGRAPH 21(4),1987Visual model of bird flocksLack of centralized controlLack of symbolic communicationGeneral approach: Local computation, i.e. each individual maximizes:Collision avoidance: steer away from impactVelocity matching: match speed of neighboring birdsFlock centering: steer towards perceived flock centerFlock behavior = emerges from interactions of large groups of such construed individuals

I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 2009Ant trails: emergent organizaton driven by communication Problem: optimize location and extraction of food sourceLack of centralized controlLack of symbolic communicationGeneral modeling approach:Local computation leads to higher order emergent computation Walk algorithm probabilistic, but biased by pheromone concentraionAnts leave pheromone trail when food is foundPheromone evaporates with timeFind shortest pathNote:~ greedy algorithm: hill-climbing on trail strength leads to adaptive, collective behaviorApproaches 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

I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 2009Probabilistic cleaning: antsVery simple rules for colony clean upPick 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. Figure by Marco Dorigo in Real ants inspire ant algorithms

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).I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 200911Ant-inspired robotsRules (Becker et al, 1994)Move: with no sensor activated move in straight lineObstacle 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 clusteringThe probability of dropping items increases with quantity of items in vicinityFigure from R Beckers, OE Holland, and JL Deneubourg [1994]. From local actions to global tasks: Stigmergy and collective robotics. In Artificial Life IV.

I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 200912becker et al experiments

I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 200913Luc Steels et al: ant algorithmshttp://www.youtube.com/watch?v=93LwvuxDbfU

I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 2009Adaptive information systems

Swarm Smarts. 78. Scientific American March 2000. ERIC BONABEAU

Johan Bollen (1994): adaptive hypertext systemsI501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 2009Recommender systems: general principlesPeople ~ n-dimensional vectorsPerson = { CD/book purchases, DVDs rented, }Vector is a representation of consumer. Entries can be weighted (TFIDF etc)Vector Space ModelCalculate similarity of users:Correlation of user vectorsCosine similarityGroup consumers according to similarity: clusteringSimilar users: discrepancies in vectors are recommendationsUsed for all sorts of applicationsSimilar problem to bad of wordsMultiple user personalities?Orthogonality?Same = better??ShameboyPlastic OperatorAngle: Consumer Similarity[Shameboy, Plastic Operator, Figurine,]Buyer 1 [1, 1, 0, 0, 0,]Buyer 2 [1, 0, 0, 0, 0,]I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 200916Tracking scientists (they are people too!)http://informatics.indiana.edu/jbollen/PLosONEmap

Andr Skupin

Borner/Ketan (2004)PNAS 101(1)Highly recommended:http://www.scimaps.org/I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 200917documentsinterfaceWere all ants now?User vectors:Represent individual trail/exploration in n-dimension information spaceRecommender systems:bias probabilistic exploration paths of users based on others actionsHigher probability of following existing trailsAnalogy:Set of user vectors + recommender system ~ ant trailsSolving 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

recommenderI501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 2009Readings:Questions:

- Atlantic (2009) Is google making us stupid:As a scientist how would you falsify Carrs 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? Whats a way out?

I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 2009Next week readingsGouth (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 A12Eysenbach G, 2006 Citation Advantage of Open Access Articles. PLoS Biol 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:10.1145/1536616.1536631I501 Introduction to Informaticsjbollen@indiana.eduhttp://informatics.indiana.edu/jbollen/I501

Informatics and computingLecture 11 Fall 2009

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