From swarming to collaborative filtering

I501 – Introduction to Informatics

[email protected]://informatics.indiana.edu/jbollen/I501

Informatics and computing

Lecture 11 – Fall 2009

From swarming to collaborative filtering.

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





Informatics:

a possible parsing

Complex Systems

Data & Search

Data Mining

HCID

Social Informatics

Security

Bio-

Chem-

Geo-

Music-

Health-

towards problem solving beyond computing into the natural and social synthesis of information technology





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!

Psilophyta/Psilotum

bab

bb

b

b

bb b

aa

aa

aaa





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





What about our plant?

An Accurate model requires Varying angles Varying stem lengths Randomness

The Fibonacci Model is similar Sneezewort:

Psilophyta/Psilotum

bab

bb

b

b

bb b

aa

aa

aaa





Fibonacci Numbers!

Rewriting production 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





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: Collective flight, 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 control or symbolic/long-range communication, but isn’t





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





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





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





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.

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).





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 The probability of dropping items increases with quantity of

items 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.





becker et al experiments





Luc Steels et al: ant algorithms

http://www.youtube.com/watch?v=93LwvuxDbfU





Adaptive information systems

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

Johan Bollen (1994): adaptive hypertext systems





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??

Shameboy

Plastic Operator

Angle: Consumer Similarity

[Shameboy, Plastic Operator, Figurine,…]

Buyer 1 [1, 1, 0, 0, 0,…]

Buyer 2 [1, 0, 0, 0, 0,…]





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/





documents

interface

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

recommender





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?





Next week readings

1. Gouth (2009) Training for Peer Review. Science Signaling 2 (85), tr2. [DOI: 10.1126/scisignal.285tr2]

2. MONASTERSKY (2005) The number that is devouring science. Chronicle of higher education, Section: Research & Publishing Volume 52, Issue 8, Page A12

3. Eysenbach G, 2006 Citation Advantage of Open Access Articles. PLoS Biol 4(5): e157. doi:10.1371/journal.pbio.0040157

4. Lance Fortnow (2009) Time for Computer Science to Grow Up. Communications of the ACM, august, 52(8) doi:10.1145/1536616.1536631

Documents

From swarming to collaborative filtering