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Finding simplicity in complex systems Luís M. A. Bettencourt

Finding simplicity in complex systems Luís M. A. Bettencourt Theoretical Division, Los Alamos National Laboratory Santa Fe institute. lmbettencourt@gmail.com.

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Finding simplicity in complex systems Luís M. A. Bettencourt

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  1. Finding simplicity in complex systems Luís M. A. Bettencourt Theoretical Division, Los Alamos National Laboratory Santa Fe institute lmbettencourt@gmail.com

  2. Each piece, or part, of the whole nature is always an approximation to the complete truth, or the complete truth so far as we know it. In fact, everything we know is only some kind of approximation. • The Feynman Lectures on Physics (1964)

  3. Taming Complex Systems • Measuring new and better things • genome, pathogens, medicine, ecosystems, • science & technology, computer networks, • nations, cities, organizations, individuals. • Big Data • data are enormous, varied, growing exponentially • So what? • do we have better public health? • policy? • science and technology? • more efficient markets?

  4. Why (not)? The problem: familiarity, observation vs. experiments, drowning in data, entanglement, speed, scope • The answer [this talk]: • most things don’t matter! • but • some things matter a lot: know what’s important! • measure • infer mechanism • act fast • act systematically • iterate approximately!

  5. news amanda knox

  6. contagion processes the early history

  7. source: wikipedia Hand bill from the New York City Board of Health, 1832

  8. These miserable outcasts called that "fumigating" us, and the term was a tame one indeed. They fumigated us to guard themselves against the cholera, though we hailed from no infected port. We had left the cholera far behind us all the time. However, they must keep epidemics away somehow or other, and fumigation is cheaper than soap. RANT, RANT, RANT Mark Twain 1867 Life photo archive /Google Images.

  9. The data soho london (1854)

  10. Prockter, A. photo image, Feb. 23, 2006. 7th September, 1855

  11. Cady Staley and Geo. S. Pierson, The Separate System of Sewerage, Its Theory and Construction (New York: D. Van Nostrand, Co., 1899), p. 33.

  12. “Whenever you can, count.” Francis Galton

  13. the nature of branching processes b=2 Nk= bk Nk-1 k ~ time, space,... b is some complicated function b>1 supercritical blows up b=1 critical = threshold b<1 subcritical fizzles out in epidemics b = contact rate x infectious time Most complex systems operate fast and extensively by triggering and regulating cascades

  14. Motor Control

  15. Epileptic Seizure Generalized 3 Hz spike and wave discharges in a child with childhood absence epilepsy source: wikipedia

  16. HIV vs the human immune system information arms races

  17. Highly Active Antiretroviral Therapy (HAAT) or “drug cocktail” anti-retroviral drugs give the immune system a hand by reducing b for the virus

  18. information cascades heuristics of decision making under uncertainty Bikhchandani, S., Hirshleifer, D., and Welch, I. (1992) when in doubt imitate Bettencourt (2002-3) arXiv:cond-mat/0212267 arXiv:cond-mat/0304321 • Proof: • no a priori predictability of new trends is possible • [early predictability very unreliable] • network visibility (number of incoming-links) leads to the emergence and reinforcement of • (just as dumb) market makers

  19. The spread of scientific ideasUSA, Japan,USSR The power of a good idea Quantitative modeling of the spread of ideas from epidemiological models Bettencourt et al Physica A (2006), Scientometrics (2008), PNAS (2011)

  20. much of what we do in society makes sense in light of information transmission increase contact rate & cities meetings phd programs postdocs search engines increase lifetime of information data storage written documents libraries & archives databases b = contact rate xlifetime of information

  21. It’s all about b [networks] • don’t need to know it • measure it! • but what about uncertainty? • predict and measure it! • what about understanding, control? • act, predict and measure it!

  22. Marburg Hemorrhagic Fever Uige, Angola 2005 89% death rate

  23. observation model prediction inferred mechanism Bettencourt (2006) in Mathematical and Statistical Estimation Approaches in Epidemiology Springer

  24. good idea! measure predict measure anomaly? iterate

  25. swine flu pandemic Mexico 2009 shut down Mexico City 20 million people but with mixed results world pandemic

  26. Faster than the speed oflife b=1.3 for influenza (b<3) infectious period ~ days information technology ~ fraction of second Neo dodging bullets The matrix

  27. Computers are now being used to generate news stories about company earnings results or economic statistics as they are released. This almost instantaneous information is fed directly into other computers, which trade on the news. City trusts computers to keep up with the news Aline van Duyn, Financial Times, April 16 2007 03:00

  28. Taming complexity is a wicked problem • but often it is enough to know what is critical • [failure modes, instabilities, cascades] • it is easier to be simple-minded but fast* • than slow and complex • * but you’ve got to learn • action mechanisms need to be further developed

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