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Think complexity
 

Think complexity

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Think complexity

Think complexity

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    Think complexity Think complexity Presentation Transcript

    • ThinkComplexityAllen B. DowneyProf of Computer ScienceOlin CollegeGoogle Visiting Scientist2009-10tinyurl.com/tc0523
    • The Olin Challenge● Re-invent the CS curriculum for an innovative eng program.● Easy: engineering major + CS minor.● Hard: breadth, depth, coherence and rigor in 5 classes. NOPE!
    • One solutionDowney and Stein, "Designing a small-footprint curriculum incomputer science," FIE 2006.
    • One problem● Who forgot to pack the data structures?● My adviser is not mad, just very, very disappointed.
    • Whats wrong with data structures?● No context.● No motivation.● Just one damn sort algorithm after another.
    • Meanwhile... complexity!
    • v0.1 (2005)● Taught Computational Modeling.● Started with popular non- fiction.● Ended with "the ultra-secret point of this class."
    • v0.5 (2008) ● More Python. ● More data structures. ● More philosophy of science. ● Ended with "the ultra-secret point of this class."
    • v0.9 (2011)● Wrote the book.● Taught the class.● Students worked on case studies.● Recruited a program committee.● Edited like mad.● Published!
    • And finally... ● ...wrote Chapter 1: The ultra-secret point of this class.
    • My thesis We are in the middle of a quiet revolution in science. ● What we mean by "science." ● Whats considered a good theory. ● Whats considered a good model.
    • The complexity revolutionEquation-based → simulation-basedAnalysis → computationContinuous → discreteLinear → non-linearDeterministic → stochasticAbstract → detailedOne, two → manyHomogeneous → composite
    • Celestial mechanicsWhy are planetary orbitselliptical? ● Universal Gravitation. ● Differential equation. ● QED!Most people find this kindof explanation satisfying.
    • Racial segregationSchellings agent-based model. ● Agents are happy with a few similar neighbors. ● Otherwise they move. Spot the accessibility issue with this image!
    • Satisfying? On the one hand: ● Appeal to universal natural law, ● mathematical virtuosity, ● predictive power, ● and Proofiness™! On the other: ● Highly abstract (not realistic), ● No math, just playing with computers. ● What kind of work do these models do?Newton is not impressed.
    • What do models do?● Predict, Explain, Design.
    • Schellings model● Counterexample to the assumption that a racist outcome implies racist agents.● Example of an emergent property.
    • Six degreesStanley Milgram: two of the most (in)famous experiments: ● Obedience to Authority, ● Small World experiment.
    • Too many models?Watts and Strogatz: Small world graphSocial networks are small because they include both strongly-connected clusters and "weak ties" that connect clusters.Barabási and Albert: Scale free networkSocial networks are small because they include nodes withhigh degree that act as hubs, and those hubs grow, over time,due to preferential attachment.
    • Thomas KuhnMost famous for Structure of Scientific Revolutions.Later "Objectivity, Value Judgment, and Theory Choice." Ultimately responsible for this sort of thing.
    • Getting to PhilosophyQuoth xkcd: If you take any [Wikipedia] article, click onthe first link [...], and then repeat, you will eventually endup at “Philosophy”
    • Philosophy of ScienceTopics in ThinkComplexity: ● Theory choice. ● Demarcation problem and falsifiability. ● Realism and instrumentalism. ● Holism and reductionism.
    • Back to...... the ultra-secret point of this book.
    • New kind of scienceEquation-based → simulation-basedAnalysis → computationContinuous → discreteLinear → non-linearDeterministic → stochasticAbstract → detailedOne, two → manyHomogeneous → composite
    • New kind of model?Predictive → explanatory.Realism → instrumentalism.Reductionism → holism.
    • New kind of engineeringCentralized→ decentralized.Isolation → interaction.One-to-many → many-to-many.Top-down → bottom-up.Analysis → computation.Design → search.
    • New kind of thinking → many-valued logic.Aristotelian logicFrequentist probability → Bayesianism.Objective → subjective.Physical law → theory → model.Determinism → indeterminism.
    • Read it! ● Complexity science. ● Philosophy of science. ● Analysis of algorithms? ● Intermediate Python? ● Case studies!
    • Case studies ● Ant trails. ● Slime molds. ● Distribution of wealth in Sugarscape. ● Epidemiology in social networks. ● Knots in Wikipedia. ● The norms game. ● Evolution of virtual creatures.You can write one, too: thinkcomplex.com/case_studies/
    • Whats next?Think Python ● OReilly, July 2012Think Sync?Think Linear?
    • Free BooksMore free books at Green Tea Press.Read, contribute, translate,modify, copy, distribute.
    • Data StructuresComplexity lends itself to data structures. ● Graphs and graph algorithms. ● Cellular automata and matrices. ● Agent-based models and ... everything.