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Modeling th e world

Modeling th e world. a possible parsing of informatics. Health-. By Erik Stolterman. Towards problem solving Beyond computing Into the natural and social Synthesis of information technology Two -dimensional science? Empirical , computer-aided study of organization. Geo-.

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Modeling th e world

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  1. Modeling the world

  2. a possible parsing of informatics Health- By Erik Stolterman • Towards problem solving • Beyond computing • Into the natural and social • Synthesis of information technology • Two-dimensional science? • Empirical, computer-aided study of organization Geo- HCID Security Data Mining Bio- Data & Search Social Informatics Complex Systems Music- Chem-

  3. Formalizing Knowledge • Lord Kelvin's dictum • “When you can measure what you are speaking of and express it in numbers you know that on which you are discoursing. But if you cannot measure it and express it in numbers. your knowledge is of a very meagre and unsatisfactory kind.” • 1824-1907 • Absolute scale of temperature, underwater telegraph cables, thermodynamics • Physics: • The first science to construct precise, rigorous formal theories of the world. • relating the operation of rules upon symbols to the law-like behavior of the World. • Aristotle (384-322 BC) was first to relate symbols more explicitly to the external world and to successively clarify the nature of the symbol-world relation.

  4. Understanding Nature with Symbols • Aristotle (384-322 BC) • First to relate symbols more explicitly to the external world and to successively clarify the nature of the symbol-world relation. • Student of Plato, educated Alexander the Great • First to consider specific observable factors which determine motion. • In Physics • He recognized (mathematical) rules which could describe the relation between an object's weight, the medium's density and the consequent rate of motion (fall): • (1) for freely falling or freely rising bodies, speed is proportional to the density of the medium. • (2) in forced motion, speed is proportional to the force applied and inversely proportional to the mass of the body moved • First time that observable quantities had been expressed in symbolic (numerical) form allowing the results of observations to be used in calculations • The nature of causation • http://classics.mit.edu/Aristotle/physics.html Raphael’s “Plato and Aristotle” = modeling

  5. Abstracting Relations • Galileo (1564-1642) • Progressive dissociation of the symbols from objects • The interrelationships among signs themselves studied quite apart from the relations among the objects they represent • Previously, symbols were still generally regarded as inherent properties of the referent objects themselves • Aristotle’s Physics postulated certain primary qualities/elements such as “Fire”. Galileo regards "primary" properties as only those that can be mathematically quantified, such as size, shape and motion. • Dialogosoprai due massimisistemi del mondo: a fabulous read • Newton (1643-1727) • Extends process of abstraction • Distinguishes between symbols • Arising from observation • represent initial conditions • Arising from symbol relations • representing laws which govern the subsequent motion.

  6. Heinrich Hertz (1857-1894) • Some facts about Hertz • First to broadcast and receive radio waves • Established that light is a form of electromagnetic radiation. • His name is associated with the SI unit for frequency • Principles of Mechanics (1894) • Goal was to purge physics of mystical, undefined, unmeasured entities • such as force (which one can infer but not measure) • Physical theories to be based only on measurable quantities • the results of measurements are symbols. • Physical theory becomes about building relationships among observationally-derived symbols: models • what Hertz called "images." Cf. Kepler: "Just as the eye was made to see colors, and the ear to hear sounds, so the human mind was made to understand, not whatever you please, but quantity,

  7. Model Logical Consequence of Model Symbols (Images) Initial Conditions Observed Result Measure Measure Encoding Physical Laws World2 World1 Using computation to model the world: Hertzianmodeling paradigm “The most direct and in a sense the most important problem which our conscious knowledge of nature should enable us to solve is the anticipation of future events, so that we may arrange our present affairs in accordance with such anticipation”. (Hertz, 1894) Predicted Result ???? Formal Rules (syntax) (Pragmatics) (Semantics)

  8. The Antikythera Mechanism • 2,000-year-old astronomical calculator • bronze mechanical analog computer • discovered more than 100 years ago in a Roman shipwreck, was used by ancient Greeks to display astronomical cycles. • built around the end of the second century BC to calculate astronomical positions • With imaging and high-resolution X-ray tomography to study how it worked. • complicated arrangement of at least 30 precision, hand-cut bronze gears housed inside a wooden case covered in inscriptions. • technically more complex than any known device for at least a millennium afterwards.

  9. Other mechanical models AbbasibnFirnas (9th century) Anno 1712 Late 18th, early 19th Anno 1780

  10. Some thoughts on the notion of models Are all “models” equally acceptable/useful? No! William Ockham (c. 1285–1349): “entianon suntmultiplicandapraeternecessitatem” - Loosely paraphrased as “make no unnecessary assumptions”, or “of two competing theories: simplest is often best” - Explanatory “power” (cf. discussion on “beauty” - Generality Example: model of lightning? “Thor gets mad.” Karl Popper (1902-1994): notion of falsifiability - model/theories/assertions can not be be confirmed by any number of empirical tests (Blackbox..?) - but information gained when falsified - = requirement for scientic models - tremendously important in science Both issues matter in social and political sciences: - existing intuitive notions complex, fail - falsifiability: logistical/moral problems Popper (1972) Objective Knowledge

  11. Mental hygiene Why People Believe Weird ThingsMichael Schermer http://www.skywise711.com/Skeptic/WPBWT/index.html#1

  12. Is The Mathematics Language Of Nature http://pithemovie.com

  13. 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

  14. 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

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

  16. 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

  17. F&M Santogold [Santogold, Fujiya & Miyagi, Bjork, Lykke Li ,…] Buyer 1 [1, 1, 0, 0, 0,…] Buyer 2 [1, 0, 0, 0, 0,…] Tracking Consumer Data • Records stored as vectors • CD Purchases • [Santogold, Fujiya & Miyagi, Lykke Li, John Legend, Gang Gang Dance…] • Pages you read • [Information, Library of Babel, Blogs, Informatics, Black Box, Obama, …..] • Vector is a representation of consumer • Grouping consumers according to similarity is a model of users • Clustering • Used for all sorts of models! Angle: Consumer Similarity

  18. Tracking people http://informatics.indiana.edu/jbollen/PLosONEmap André Skupin Borner/Ketan Highly recommended: http://www.scimaps.org/

  19. Probabilistisc 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

  20. ant-inspired robots • Becker et al Rules • 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.

  21. becker et al experiments

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

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

  24. Next Lecture Assignment 1: Due on October 21, 2009 – Quadrants 2 and 3 - Send to jbollen@indiana.edu, PDF file - Subject: I501 – Assignment 1 – YOUR NAME - attachment: your report (5 pages) * introduction: describe problem, early observations and your general approach to solving problem * methodology: general principles of your methodology, explain why/how, data collection, overview of method, details on tools used * Results: describe the results of your investigations, analysis, graphs, tables * Results discussion: draw conclusions from your analysis, discuss what they imply, formulate model of blackbox * Conclusion: short summary of the conclusion that you draw from your results. Suggestion for future generations.

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