John Doyle 道陽 Jean-Lou Chameau Professor Control and Dynamical Systems, EE, & BioE tech 1 # Ca Universal laws and architectures: Theory and lessons from.

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John Doyle 道陽 Jean-Lou Chameau Professor Control and Dynamical Systems, EE, & BioE tech 1 # Ca 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, Synesthesia, spacecraft, statistical mechanics and zombies

Neuroscience + People care +Live demos! 1.experiments 2.data 3.theory 4.universals

Length (meters) Fragility too fragile complex No tradeoff expensive fragile costly fragile cheap robust Survive Multiply thin small thick big Laws Slow Flexible vision eye vision Act slow delay Fast Inflexible VOR fast Slow Flexible Fast Inflexible Impossible (law) 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, Synesthesia, spacecraft, statistical mechanics Slow Flexible Fast Inflexible Impossible (law) Architecture General Special

Which blue line is longer?

Slow Flexible vision eye vision Act slow delay Fast Inflexible VOR fast 3D + motion color vision SlowestSlowest See Marge Livingstone

This is pretty good. Stare at the intersection

control feedback RNA mRNA RNAp Transcription Other Control Gene Amino Acids Proteins Ribosomes Other Control Translation Metabolism Products Signal transduction ATP DNA New gene Fast Costly Slow Cheap Flexible Inflexible General Special HGT DNA repair Mutation DNA replication Transcription Translation Metabolism Signal… Layered Architecture

Slow Fast Flexible Inflexible General Special Apps OS HW Dig. Lump. Distrib. OS HW Dig. Lump. Distrib. Digital Lump. Distrib. Lumped Distrib. HGT DNA repair Mutation DNA replication Transcription Translation Metabolism Signal SenseMotor Prefrontal Fast Learn Reflex Evolve vision VOR

Control, OR Comms Compute Physics Shannon Bode Turing Gödel Einstein Heisenberg Carnot Boltzmann Theory? Deep, but fragmented, incoherent, incomplete Nash Von Neumann

Control, OR Compute Bode Turing Delay and risk are most important Worst-case (“risk”) Time complexity (delay) Worst-case (“risk”) Delay severely degrades robust performance Computation for control Off-line design On-line implementation Learning and adaptation

Control, OR CommunicateCompute Physics Shannon Bode Turing Einstein Heisenberg Carnot Boltzmann Delay and risk are most important Delay and risk are least important

Communicate Physics Shannon Einstein Heisenberg Carnot Boltzmann Dominates “high impact science” literature Average case (risk neutral) Random ensembles Asymptotic (infinite delay) Space complexity

Control, OR CommunicateCompute Physics Shannon Bode Turing Delay and risk are most important Delay and risk are least important New progress!

.01 1 delay sec/m AαAα C 20  m.2  m 2 s/m.008 s/m.1 Axon size and speed area.1110 diam(  m)

.01 1 delay sec/m AαAα C.1 AβAβ Aγ AδAδ area.1110 diam(  m)

delay sec/m myelinated unmyelinated area.1110 diam(  m) Axon size and speed

area delay sec/m diam(  m).1 10  m.1  m 1  m myelinated myelin

delay sec/m area.1110 diam(  m) Retinal ganglion axons? Other myelinated Why such extreme diversity in delay and size? VOR Why no diversity in VOR?

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 Fractal wrongness

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. Doyle, Csete, Proc Nat Acad Sci USA, JULY Trivial examples

“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? Fragile Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … Robust

Complex systems? Resources Controlled Organized Structured Extreme Architected … Robust complexity 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 Emergulent Emergulence at the edge of chaocritiplexity Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence …

Things we won’t get to Attention/Awareness (Graziano) Compassion Consciousness Free will Me vs We Us vs Them

Things we won’t get to

Things we won’t get to Good/Evil

Things we won’t get to