The problem of Fractal Wrongness

1. The problem of Fractal Wrongness • Great work built on weak foundation (science) • Hope I can learn and help (shake things up)

2. Fractal wrongness Emergence Risk

3. Fractal wrongness Emergence Risk Sustainability Autocatalysis Global !#%&!?!? Evolution Technology Markets, finance, … … Robustness Efficiency Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability … Much (most?) of what many (most?) believe is obviously false.

4. Fractal wrongness • Wrongness everywhere, every scale, “truthiness” • Involving anything “complex”… • Wild success: religion, politics, consumerism, … • Debates focused away from real issues… • Even if erased, sustainability challenges remain • Hopeless if it persists • I’ll set aside all of this for now, and “zoom in” on the world of PhD research/academic scientists • BTW, excellent case study in infectious hijacking

5. Fractal wrongness (PhDs only) Emergence Risk Sustainability Autocatalysis Global !#%&!?!? Evolution Technology Markets, finance, … … Robustness Efficiency Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability … Much (most?) of what many (most?) believe is obviously false.

6. …Extinction simulations have already been performed in a variety of network contexts, including the World Wide Web (WWW) [2], metabolic networks [26], protein networks [25],… [2] R. Albert, H. Jeong, and A.-L. Barabasi. Error and attack tolerance of complex networks. Nature, 406:378{382, 2000. [25] H. Jeong, S. P. Mason, A.-L. Barabasi, and Z. N. Oltvai. Lethality and centrality in protein networks. Nature, 411:41, 2001. [26] H. Jeong, B. Tombor, R. Albert, Z. N. Oltvai, and A.-L. Barabasi. The large-scale organization of metabolic networks. Nature, 407:651{654, 2000.

7. Fractal wrongness (PhDs only) • “Wrongness” everywhere, every scale • Involving “complexity” • Wild success: high impact journals, funding agencies, popular science, policy… • Debates shifted away from real issues… • Even if erased, huge challenges remain • Hopeless if it persists? • BTW, excellent case study in infectious hijacking

8. Fractal wrongness (PhDs only) • “Wrongness” everywhere, every scale • Involving “complexity” • Wild success: high impact journals, funding agencies, popular science, policy… • Debates shifted away from real issues… • Even if erased, huge challenges remain • Hopeless if it persists? • I’ll set this aside for now, and “zoom in” to a small spot (within science) with a solid foundation (but a huge spot in math and engineering)

9. Rigorous foundations Emergence Risk Autocatalysis Global !#%&!?!? Evolution Technology Markets, finance, … … Robustness Efficiency Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability …

10. A source of fractal wrongness we need to remove Large, thin, nonconvex

11. “solution sets” (a la Marder, Prinze, etc) large, thin, nonconvex Software Hardware Functional Digital Analog All systems

12. Layered language? Meaning Syntax Words Letters large, thin, nonconvex

13. Letters and words • 9 letters: adeginorz • 9!= 362,880 sequences of 9 letters • Only “organized” is a word • << (# words) << (# non-words) large thin Meaning Syntax Words Letters

14. Language? Almost all • grammatical sentences are meaningless • sequences of words are ungrammatical • sequences of letters are not words • collections of symbols are not letters Meaning Syntax Words Letters

15. Almost all • grammatical sentences are meaningless • sequences of words are ungrammatical • sequences of letters are not words • collections of symbols are not letters • large, thin, nonconvex • # of order parameters  • with  tuning, Watson beat humans at Jeopardy • What are the protocols and architecture? Meaning Syntax Words Letters

16. large thin 1 << # toys << # piles toys pile

17. large thin 1 << # toys << # piles emergence edge of chaos self-organized criticality scale-free ??? pile statistical physics random ensembles minimally tuned phase transitions bifurcations “order for free?”

18. large thin 1 << # toys << # piles nonconvex toys pile “order for free?”

19. Computer programs Software • Almost any computer language • Large # of working programs •  larger # of non-programs • “Nonconvex” = simple mashups of working programs don’t work • Need architecture to organize working programs • << (# programs) << (# non-programs) large thin Hardware Digital Analog

20. Computer programs Software Software is not an emergent property of hardware • morphodynamics and “top-down causality” ? • teleodynamics? Hardware Digital Analog

21. functional software Meaning Software Syntax Hardware All “code” large, thin, nonconvex Words Digital Letters Analog digital Analog

22. This paper aims to bridge progress in neuroscience involving sophisticated quantitative analysis of behavior, including the use of robust control, with other relevant conceptual and theoretical frameworks from systems engineering, systems biology, and mathematics. Trivial examples Doyle, Csete, Proc Nat AcadSci USA, JULY 25 2011

23. Universal “laws” (constraints) • Some constraints on bio & tech rise “bottom up” from physics/chemistry • Gravity • Speed of light • Energy • Carbon • Other small moieties (e.g. redox, …)… more later? • Emergence?

24. Physical laws as constraints on components • Gravity • Speed of light • Energy • Carbon • Other small moieties (e.g. redox, …)… more later Components

25. Biology & technology not emergent in any useful sense • Dissipation, friction • Liquids, gases, fluid dynamics • Phase transitions, homogeneous turbulence, etc • etc etc Huge gap Emergent phenomena? Components

26. Risk not emergent in any useful sense? Gap? Biology & technology not emergent in any useful sense Huge gap • Dissipation, friction • Liquids, gases, fluid dynamics • Phase transitions • etc etc Emergent phenomena? Components

27. Risk not emergent in any useful sense? Gap? Biology & technology not emergent in any useful sense Huge gap • Dissipation, friction • Liquids, gases, fluid dynamics • Phase transitions • etc etc Emergent phenomena? Components

28. Teleodynamics (Deacon) HUGE gap Top down causality (Juarrero) Morphodynamics (Deacon) Big gap Emergent phenomena? Components

29. Active Passive Turbulent Laminar Lumped  coherent story here, but haven’t figured out best way to explain… working on it… fragile Passive Distribute Laminar Lossless Classical Needs active Quantum ? robust Turbulent efficient wasteful

30. Emergence?

31. “New sciences” of “complexity” and “networks”? worse • Edge of chaos • Self-organized criticality • Scale-free “networks” • Creation “science” • Intelligent design • Financial engineering • Risk management • “Merchants of doubt” • … • Science as • Pure fashion • Ideology • Political • Evangelical • Nontech trumps tech

32. Theorem! z and p functions of enzyme complexity and amount Fragility simple enzyme complex enzyme Enzyme amount Savageaumics

33. Fragility hard limits • General • Rigorous • First principle simple complex ? Overhead, waste • Domain specific • Ad hoc • Phenomenological Plugging in domain details

34. Control Wiener Comms Bode Shannon robust control Kalman • Fundamental multiscale physics • Foundations, origins of • noise • dissipation • amplification • catalysis • General • Rigorous • First principle ? Carnot Boltzmann Heisenberg Physics

35. What I’m not going to talk much about • It’s true that most “really smart scientists” think almost everything in these talks is nonsense • Why they think this • Why they are wrong • Time (not space) is our problem, as usual • Don’t have enough time for what is true, so have to limit discussion of what isn’t • No one ever changes a made up mind (almost) • But here’s the overall landscape

36. Control Comms Wildly “successful” Complex networks Compute “New sciences” of complexity and networks edge of chaos, self-organized criticality, scale-free,… Stat physics Carnot Boltzmann Heisenberg Physics

37. Popular but wrong

38. Complex systems? Fragile Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … Even small amounts can create bewildering complexity

39. Complex systems? Robust Fragile Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence …

40. Complex systems? Robust complexity Resources Controlled Organized Structured Extreme Architected … Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence …

41. These words have lost much of their original meaning, and have become essentially meaningless synonyms • e.g. nonlinear ≠ not linear • Can we recover these words? • Idea: make up a new word to mean “I’m confused but don’t want to say that” • Then hopefully we can take these words back (e.g. nonlinear = not linear) Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … Fragile complexity

42. New words Fragile complexity Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … Emergulent Emergulence at the edge of chaocritiplexity

43. Alderson & Doyle, Contrasting Views of Complexity and Their Implications for Network-Centric Infrastructure, IEEE TRANS ON SMC, JULY 2010 Complex networks doesn’t work Stat physics “New sciences” of complexity and networks edge of chaos, self-organized criticality, scale-free,…

44. Complex systems? Control Comms Complex networks Compute Stat physics Carnot Boltzmann Heisenberg Jean Carlson, UCSB Physics Physics

45. Alderson &Doyle, Contrasting Views of Complexity and Their Implications for Network-Centric Infrastructure, IEEE TRANS ON SMC, JULY 2010 Complex networks Control Sandberg, Delvenne, & Doyle, On Lossless Approximations, the Fluctuation-Dissipation Theorem, and Limitations of Measurement, IEEE TRANS ON AC, FEBRUARY, 2011 Stat physics Carnot Boltzmann Heisenberg Physics

46. “The last 70 years of the 20th century will be viewed as the dark ages of theoretical physics.” (Carver Mead) Complex networks Fundamentals! From prediction to mechanism to control “orthophysics” Sandberg, Delvenne, & Doyle, On Lossless Approximations, the Fluctuation-Dissipation Theorem, and Limitations of Measurement, IEEE TRANS ON AC, FEBRUARY, 2011 Stat physics, fluids, QM Carnot Boltzmann Heisenberg Physics

47. Lumping requirements into simple groups dependable deployable discoverable distributable durable effective evolvable extensible failure transparent fault-tolerant fidelity flexible inspectable installable Integrity interchangeable interoperable learnable maintainable manageable mobile modifiable modular nomadic operable portable precision predictable producible recoverable relevant reliable repeatable reproducible resilient responsive safety scalable serviceable supportable securable stable survivable tailorable testable timely traceable upgradable accessible accountable accurate adaptable administrable auditable autonomy available compatible composable configurable correctness customizable debugable determinable demonstrable A systemcan beefficient using a resource or producing waste But can be inefficient for different resources or waste robust

48. Efficiency tradeoffs wasteful with resource 2 Achievable efficient with resource 2 Not Ideally wasteful with resource 1 efficient with resource 1

49. Efficiency tradeoffs wasteful humans digesting raw vegs chimps pronghorn? cattle efficient Not? wasteful efficient Distance running

50. Lumping requirements into simple groups dependable deployable discoverable distributable durable effective evolvable extensible failure transparent fault-tolerant fidelity flexible inspectable installable Integrity interchangeable interoperable learnable maintainable manageable mobile modifiable modular nomadic operable portable precision predictable producible recoverable relevant reliable repeatable reproducible resilient responsive safety scalable serviceable supportable securable stable survivable tailorable testable timely traceable upgradable accessible accountable accurate adaptable administrable auditable autonomy available compatible composable configurable correctness customizable debugable determinable demonstrable A system can be robust for a property and a perturbation But can be fragile for a different property or perturbation robust