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
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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).
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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.
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
becker et al experiments
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
Luc Steels et al: ant algorithms
http://www.youtube.com/watch?v=93LwvuxDbfU
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
Adaptive information systems
Swarm Smarts. 78. Scientific American March 2000. ERIC BONABEAU
Johan Bollen (1994): adaptive hypertext systems
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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,…]
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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/
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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?
I501 – Introduction to Informatics
[email protected]://informatics.indiana.edu/jbollen/I501
Informatics and computing
Lecture 11 – Fall 2009
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
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