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Pareto-based Science: Basic Principles—and Beyond

Pareto-based Science: Basic Principles—and Beyond. Bill McKelvey ----- Adelphi Conference: Social Entrepreneurship, System Thinking & Complexity 2008. Order, Chaos, Emergence. 2 nd critical value: Edge of Chaos. 1 st critical value: Edge of Order. Emergence. Initial condition. Order.

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Pareto-based Science: Basic Principles—and Beyond

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  1. Pareto-based Science:Basic Principles—and Beyond Bill McKelvey ----- Adelphi Conference:Social Entrepreneurship, System Thinking & Complexity2008

  2. Order, Chaos, Emergence 2nd critical value:Edge ofChaos 1st critical value:Edge ofOrder Emergence Initial condition Order

  3. Order, Chaos, Emergence Chaos 2nd critical value:Edge ofChaos CatastropheTheory &AttractorBasins 1st critical value:Edge ofOrder Fractals Emergence Region of Emergence Initial condition PowerLaws Order ScaleFreeTheories

  4. The Romanesco broccolo power law 1000 Frequency (log scale) 300 300 Frequency 80 80 9 9 1 1 Size (log scale Size (florets) From Fractal to Power Law A power law is a relationship in which one quantity A is proportional to another B taken to some power n; that is, A~Bn

  5. Italian Income Distribution Only the Straight line is a Power Law Distribution Minimum amounts of: 1. Social background,2. Education,3. Personality type,4. Technical ability,5. Communication skills6. Motivation,7. Right place-right time,8. Willing to take risks

  6. Self-Organised Criticality: The Sand-Pile Model (Bak & Chen, 1992) Log of frequency of avalanches Log of size of avalanches

  7. Paretian World GaussianWorld Power law Inverse Slope LogofEventFrequency Mean Log of Event Size Mosquitoes Elephants

  8. Some 1st Principles of Pareto-based Science • Principle #1: Given Connectivity, R/Fs Dominate • Principle #2: Tension Exacerbates Connectivity Effects • 1st Critical Value; Tension in a Teapot; Bose-Einstein Condensate • Fishnets; power grids • Fear & Greed in the Stock Market  loss of heterogeneity  market collapse • Business problems  more connections via phone, meetings, Internet, etc. • Supply/demand-based tension  hub & spoke airports  connectivity of storm effects • Principle #3: Connectivity Exacerbates Tension Effects • Mini-ice age  migration  conflicts & black plague • LTCM; Increasing connectivity of losses & liabilities  sub-prime meltdown • Traffic jams  more traffic on other roads  more tension • Connectivity  contagion bursts  pandemics • Rioters with cell phones  more trouble for the police • Principle #4: The Law of Large Numbers Finds Rank/Frequency and Not Normal Distributions • A. Connectivity Replaces i.i.d. • B. Pareto Rank/Frequencies Replace Normal Distributions

  9. Paretian World GaussianWorld Inverse Power Law Slope LogofEventFrequency Mean Log of Event Size Mosquitoes Elephants • Principle #5: Rank/Frequencies Pareto-based Methods • What is Common to Both? DNA, RNA, Genes, Organelles, Cells, Organelles, Blood • What is Different? Different Ecologies Adaptation and Species Differences

  10. Pareto-based Method Implications • 1: Need to Develop Methods for Studying Emergence • 2: Studying Extremes at N = 1: “Talking Pigs” • 3: Likelihood of Overlapping i.i.d.& Idiosyncratic Micro-niches at Upper-left--i. e., Anderson’s Long Tail • 4:Vertical Slices Progressing toward Smaller Samples to N = 1 • 5: Horizontal Scalability DynamicsFigure • 6:Bak’s Self-organized Criticality--Research how Butterfly-events Do or Don’t Scale up from Left to Right • 7. Power laws as the “Diagonal” in Gini Coef. Methods • 8:Power laws as Indicators of Efficaciously Adaptive Self-organization • 9. Methods aimed at Better Indicating/Locating i.i.d vs. Connectivity Effects at intra- and inter-firm, industry and economy levels of analysis

  11. Improving N = 1 Methods Multiple Observers • Hermeneutics • Principle of Charity • Coherence Theory • Abduction • Needed Improvements • Few cases; same biased observer? No! • Few cases + few diverse observers… Yes! • When Induction doesn’t lead to Deduction… • Scalability sensitivity to butterfly events & levers • Extreme statistics • PL slope as criterion variable MODEL N = 100s to 1000s

  12. Paretian World GaussianWorld Power law Inverse Slope LogofEventFrequency Mean Log of Event Size Ma&PaorTesco • 9: EcoSystem Research • 10. Industry and Firm Structures • Iansiti & Levien: Software ecosystem • Ishikawa: 2-digit SIC-code industries • Power laws in “empty” categories • Other distributions in “full” categories • Transition economies in Eastern Europe • Power law evidence of self-organization dynamics EcoSystem Wal-Mart

  13. Quick Examples of Missing the Initiating Events • FBI • Filling in the Dots • 52 Clues Known in Advance • Behind on the Patterns • Enron • Creative Accounting; Complicit CPA • People Knew; Memos were Sent • Behind on the Patterns • NASA • Challenger and Columbia Disasters • All Sorts of Clues about “Almost” Failures • Behind on the Patterns • Doctor in UK • Murdered over 250 patients (they think 280+) • Prescribed drugs; murdered patients; kept drugs for his “habit” • What he was doing was “known” before he killed the 1st person!

  14. Well Performing Economies U.S./Japan line India/China line

  15. Not So Hot Economies

  16. Is UK Broken? U.S. Bangladesh Mexico UK India Bulgaria

  17. Originals in Red; Next in White; Newest in Black Czech Rep. Germany UK Spain Hungary Cyprus Malta

  18. Microsoft’s Software Ecosystem M. Iansiti & R. Levien 2004. Strategy as Ecology. Harvard Business Review, 2004, pp. 68–78.

  19. Software Power Law Distribution

  20. 2002 1992 Per Bak’s “Avalanche” research dates back to 1987!

  21. Sand Grains of Irregular Shape • Some Kind of Connectivity • Critical Slope • Avalanches; Heteroscedasticity • Pareto Distribution; Power Law • Unstable Means; (nearly) Infinite Variance • Widened Confidence Intervals • Independence Among Data Points • Approximating marbles (rounded) • Linearity • Homoscedasticity • Normal Distribution • Stable Mean; Finite Variance • Narrowed Confidence Intervals

  22. M & M Science

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