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Universal laws and architectures : Theory and lessons from brains, bugs, nets, grids, docs, planes, fire, fashion, art, turbulence, music, buildings, cities , earthquakes, bodies, running, throwing , S y n e s t h e s i a , spacecraft, statistical mechanics. and zombies.
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Universal laws • and architectures: • Theory and lessonsfrombrains, bugs,nets, grids, • docs, planes, fire, fashion, • art, turbulence, music, buildings, • cities, earthquakes, bodies, running,throwing, • Synesthesia, spacecraft, statistical mechanics and zombies John Doyle 道陽 Jean-Lou Chameau Professor Control and Dynamical Systems, EE, & BioE 1 # Ca tech
experiments data theory universals • Neuroscience + People care +Live demos!
vision fast slow Slow Act 8 delay vision Fragility eye Universal laws VOR 4 Fast 1 Slow 10 fragile Flexible too fragile Inflexible thin small 2 Laws fragile Impossible (law) complex 1 Fast Survive No tradeoff .05 .1 1 .2 .5 Length (meters) 0 thick big 10 robust -1 0 1 10 10 10 Flexible Inflexible expensive cheap costly Multiply
Universal laws • and architectures: • Theory and lessonsfrombrains, bugs,nets, grids, • docs, planes, fire, fashion, • art, turbulence, music, buildings, • cities, earthquakes, bodies, running,throwing, • Synesthesia, spacecraft, statistical mechanics Slow Architecture Impossible (law) Fast Flexible Inflexible Special General
Slowest color vision 3D + motion vision eye fast slow Act delay Slow vision VOR Fast See Marge Livingstone Inflexible Flexible
Stare at the intersection This is pretty good.
Layered Architecture Gene RNA RNAp Transcription mRNA Amino Acids Slow Cheap Other Control • HGT • DNA repair • Mutation • DNA replication • Transcription • Translation • Metabolism • Signal… Ribosomes DNA Translation New gene Proteins Other Control Metabolism Products Fast Costly ATP Inflexible Flexible Signal transduction control feedback Special General
vision Prefrontal Learn Slow Motor Fast Sense HGT DNA repair Mutation DNA replication Transcription Translation Metabolism Signal Apps OS HW Dig. Lump. Distrib. OS HW Dig. Lump. Distrib. Digital Lump. Distrib. Lumped Distrib. Distrib. Evolve VOR Reflex Fast Inflexible Flexible Special General
Compute Comms Gödel Shannon Turing Von Neumann Theory? Deep, but fragmented, incoherent, incomplete Nash Carnot Boltzmann Bode Heisenberg Physics Control, OR Einstein
Compute • Worst-case (“risk”) • Time complexity (delay) Turing Delay and risk are most important • Computation for control • Off-line design • On-line implementation • Learning and adaptation Bode • Worst-case (“risk”) • Delay severely degrades robust performance Control, OR
Compute Communicate Turing Shannon Delay and risk are most important Delay and risk are least important Carnot Bode Boltzmann Control, OR Heisenberg Physics Einstein
Communicate • Space complexity Shannon Dominates “high impact science” literature • Average case (risk neutral) • Random ensembles • Asymptotic (infinite delay) Carnot Boltzmann Heisenberg Physics Einstein
Compute Communicate Turing Shannon Delay and risk are most important Delay and risk are least important New progress! Bode Control, OR Physics
.2 m 2 s/m Axon size and speed 1 C delay sec/m .1 20 m Aα .01 .008 s/m diam(m) .1 1 10 area .01 1 100
1 C delay sec/m Aγ .1 Aδ Aβ Aα .01 diam(m) .1 1 10 area .01 1 100
Axon size and speed unmyelinated 1 delay sec/m .1 myelinated .01 diam(m) .1 1 10 area .01 1 100
.1 m 1 delay sec/m 10 m .1 myelinated myelin 1 m .01 diam(m) .1 1 10 area .01 1 100
Why such extreme diversity in delay and size? Other myelinated 1 delay sec/m Why no diversity in VOR? Retinal ganglion axons? .1 VOR .01 diam(m) .1 1 10 area .01 1 100
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
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
“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
Complex systems? Fragile Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … Even small amounts can create bewildering complexity
Complex systems? Robust Fragile Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence …
Complex systems? Robust complexity Resources Controlled Organized Structured Extreme Architected … Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence …
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
New words Fragile complexity Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … Emergulent Emergulence at the edge of chaocritiplexity
Things we won’t get to Attention/Awareness (Graziano) Compassion Consciousness Free will Me vs We Us vs Them
