Layering and physics• Rethink “everything” emphasizing layering as

the key concept (admittedly procrustean)• Connecting layered architectures with “layering”

(called coarse graining) in multiscale physics• Look for persistent sources of confusion• Highlight needs for clearer explanation of what

we already know• New theory is also needed for multiscale

physics, and progress is encouraging

1

PassiveLossless

• We’ve also been focusing on this theory. • Note that logically, the Venn diagram on the right holds • Reconciling this apparent contradiction is the challenge • Fluctuation-dissipation is first essential theorem

ActivePassive

PassiveLossless

• Classical statistical physics “explains” only this (badly).

Active

Passive

Lossless

• It would appear logically that the diagram on the left is equivalent to the Venn diagram below• So there is actually a nontrivial result here• As opposed to “what is SW” which is just pedagogical

ActivePassive

PassiveLossless

Active

Passive

Lossless

Lossless

Passive

Active

Finite time horizon

Infinite time

horizon

• Note that without active control, there is nothing that corresponds to what we call “cause”• As in, the “algorithm caused the robot to turn right”• So explaining to scientists that “algorithm caused” is what we mean by “cause”• While at the same time, SW only existing embodied in HW

Passive

PassiveLossless

Passive

Lossless

Lossless

Passive

Caution

• This is “deep” background• As is, not accessible or useful• Need deep experts to rethink how we

explain things we already know• There are edges of this that are research,

but the immediate need is pedagogical • Elements should go in immediate papers• Longer term issues are mixed in here

5

Big big picture• I want to ultimately argue that there are

essentially two flavors of “complexity” (and many subflavors, but deferring that for now…)

• The origins are physics vs engineering (or disorganized vs organized)

• Both have been successes in some respects and failures in other

• A key distinction is the role of “architecture”• Expanding on themes started in Alderson and

Doyle 2010

Systematic error/confusion in “new sciences”• The main idea is “emergent complexity from minimal tuned

random ensembles”• Architecture = graph topology• Dominates science and misapplication is main source of

errors• Big success story is the “modern synthesis” (not normally

thought of this way) in evolutionary biology• In physics, a standard recipe, vetted, refined, honed

– widely adopted in PhysRev, NatPhys, etc– allows great rhetorical scope– applicable everywhere (wrongly, and nowhere

correctly)• Ancillary errors from

– bad statistics, – logical errors (e.g. flipping if and only if), – emphasis on patterns (particularly superficial)

Systematic error/confusion in biology• The primary error is the same

– “emergent complexity, minimal tuned, random”– has dominated in the “modern synthesis”– evolution = small, random mutation plus selection– essential in Davrolis EvoArch

• New alternatives are radically different (better) – “Natural genetic engineering”– Savageau, Shapiro, Gerhard & Kirschner, Mattick… – Claim: Needs architecture/layering to make coherent sense

of collection of facts – Contrast with attempts to just tweak the old version

• No detail here, big a topic on its own, more elsewhere

Systematic error/confusion elsewhere

• What systems engineers know is poorly explained*• Available statistical tools are inadequate and don’t

reflect state of the art (from 50 years ago)• “Correct” theories are fragmented and incoherent• Even what constitutes “correct theory” is poorly

explained, conventional philosophy is weak• Notions of explanation, causality, mechanism,

emergence, etc etc are murky and incoherent• Multiscale and layered systems not explained

* engineers apparently have a long tradition of secrecy

SoftwareHardware

Apps

OS

Libs, IPC

kernel

DigitalAnalog

ActivePassive

ClassicalQuantum

LumpedDistributePassive

Lossless

• Start with this cartoon• Probably badly done as is• Believe this is important, but• Needs clear explanation• But of things • We thoroughly understand now• Except at the very bottom

SoftwareHardware

Apps

OS

Libs, IPC

kernel

DigitalAnalog

ActivePassive

ClassicalQuantum

LumpedDistributePassive

Lossless

• Need coherent view of layering• Turing focus on analog and up.• Physics has a coherent, consistent view that varies from confused to wildly wrong • Must ultimately redo physics all the way down• For now, understand it’s limitations• Clearly explain what we already know

Issues

SoftwareHardware

Apps

OS

Libs, IPC

kernel

DigitalAnalog

ActivePassive

ClassicalQuantum

LumpedDistributePassive

Lossless

Of course, a consequence of good

layering is that you can only indirectly know what is going

on below the layer in question. (This does

recurse…) Makes reverse engineering

challenging.

SoftwareHardware

AppsOS Libs, IPC

kernel

DigitalAnalog Active

Passive

ClassicalQuantum

LumpedDistributeWhat are

the right cartoons?

Software

Hardware

AppsOS

Libs, IPCkernel

DigitalAnalog

ActivePassive

?

?

Modularity of digital hardware

What are the right cartoons?

Software

Hardware

AppsOS

Libs, IPCkernel

DigitalAnalog

ActivePassive

Layers up here

are very different

from layers down here

This needs clearer exposition

Software

Hardware

AppsOS

Libs, IPCkernel

DigitalAnalog

Layers here are “stacked” and nonintersecting, a more familiar kind of modularity

Whereas• SW is X of HW• Digital is X of Analog

What is “X”?State, organization, large/thin…???

Need better nomenclature

Software

Hardware

AppsOS

Libs, IPCkernel

DigitalAnalog

ActivePassive

Laye

rs h

ere

from layers here

are very different

Drawn a different way

I’d be thrilled with a coherent explanation of this. (Sloman and VMs is a start.)

Software

Hardware

AppsOS

DigitalAnalog

New idea: Turing style?

Maybe start from here with Turing’s 3 step research:1. hard limits, (un)decidability

using standard model (TM)2. Universal architecture

achieving hard limits (UTM)3. Practical implementation in

digital electronics

Maybe start from here with Turing’s 3 step research:1. hard limits, (un)decidability

using standard model (TM)2. Universal architecture

achieving hard limits (UTM)3. Practical implementation in

digital electronics

Essentials:0. Model1. Universal laws2. Universal architecture3. Practical implementation

Software

Hardware

DigitalAnalog

Software

Hardware

Apps

OSLibs, IPC

kernel

Digital

Analog

Active

Passive

Laye

rs h

ere

from layers hereare very different

• Can this be explained by differences in the nature of scope?

• In applications, scope is named, logical, functional, semantic, …

• In hardware/resources, scope is addressed, physical,

• OS kernel is the “waist” between the two

Important questions

ActivePassive

ClassicalQuantum

LumpedDistributePassive

Lossless

The essence of multiscale

physics

PassiveLossless

• We’ve also been focusing on this theory. • Note that logically, the Venn diagram on the right holds • Reconciling this apparent contradiction is the challenge • Fluctuation-dissipation is first essential theorem

ActivePassive

PassiveLossless

• Classical statistical physics “explains” only this (badly).

Active

Passive

Lossless

Repeat for emphasis:• These two diagrams express logical relations that are superficially contradictory• Theory is needed to reconcile this• Standard StatPhys story is at best murky, at worst wrong• Our approach is working and should fix this, but is just a baby step (so far)

ActivePassive

PassiveLossless

Active

Passive

Lossless

• These two pictures illustrate the essential challenge• Not sure how to draw them to highlight this…

ActivePassive

PassiveLossless

Active

Passive

Lossless

PassiveLossless

… and underscore the difference with the physics view

PassiveLossless

Note:In our theory, “highly organized” and extreme nonlinearity play an essential role in active devices, and hence in life and technology.

ActivePassive

PassiveLossless

In physics, even mild nonlinearity is synonymous with chaos, while “highly organized” and active devices are not treated at all.

ActivePassive

PassiveLossless

In physics, even mild nonlinearity is synonymous with chaos, while “highly organized” and active devices are not treated at all.

“emergent, far from equilibrium, Prigogine, etc”

ActivePassive

PassiveLossless

These are extremely

different, and need to make

this clear.

Note:In our theory, “highly organized” and extreme nonlinearity play an essential role in active devices, and hence in life and technology.

Us: Stochastic models are a convenience, the result of natural and unavoidable approximations, and are explained mechanistically

PassiveLossless

Passive

Lossless

Our theory is also different at this level, while there are not obvious experimental consequences, the differences show up later in other layers.

Them: Stochastic models are assumed a priori and never “explained” except with vague notions of “chaos”(This is perhaps a minor flaw here but will make things much worse higher up.)

Our theory:Idea is that lossless are dense in passive

PassiveLossless

Passive

LosslessApproximation arbitrarily good on finite (but arbitrarily long) time horizons.

High dimensional lossless circuit passive

Looks

Really lossless

power supply

ActivePassive

Active

Passive

active

Looks

Really passive

Our theory: Active requires “hidden” power supply and nonlinear circuitry

Approximation arbitrarily good on finite (but arbitrarily long) time horizons.

power supplyactive

LooksReally passive

High dimensional lossless circuit passive

LooksReally lossless

• Both approximations arbitrarily good on finite (but arbitrarily long) time horizons.• Both require finely tuned (highly organized) circuits

• Biology and technology= active/passive circuits• Condensed matter physics = passive/lossless gases, …

• Note: fine tuning for (not vs.) robustness • Completely unlike standard physics• Many unresolved issues (e.g. fine tuning here?)

High dimensional lossless circuit passive

LooksReally lossless

Standard physics • Takes infinite time and complexity limits a priori• Takes random ensembles a priori• No other “tuning” required!

• Extensions: phase transitions, criticality, chaos everywhere, scale-free, SOC, edge of chaos, …

• Big (wrong) idea: All complexity is emergent from random ensembles with minimal tuning

We have been using lumped analog systems here, but there are two opposite directions to head in:1. Digital2. Distributed

ActivePassive

PassiveLossless

1. Digital: I think we can do much of this story using CAs to boolean nets to TMs. Easier to understand and math is almost trivial

2. Distributed: Natural direction to connect with physics and QM

ActivePassive

PassiveLossless

“emergent, far from equilibrium,

Prigogine, etc”

ActivePassive

PassiveLossless

“highly organized” with extreme nonlinearity

Huge gap

Can we illustrate this with both automata and lumped circuits (ODEs)?

(Later do distributed/PDE/QM)

power supplyCactive

LooksReally passive

New idea inspired by Deacon

Aim to connect with “dissipative” systems (Prigogine) ideas.• How to distinguish tornadoes from airplanes from birds?• Random circuits from designed circuits from digital?• Deacon’s “morphodynamic” but too much is grouped here• What does this look like if we can “look inside”?• Play with this in the next few slides.

CactiveLooks

Really passive

power supply

Look inside Passive too

PassiveLossless

ActivePassive

PassiveLossless

Thermo-dynamic

RandomMorpho-dynamic

?

?Analog

Active

BiologicalTeleo-

dynamicDeacon has these 3 kinds of systems

“emergent, far from equilibrium, Prigogine, etc”

PassiveLossless

ActivePassive

PassiveLossless

Thermo-dynamic

RandomMorpho-dynamic

DesignedMorpho-dynamic

ActivePassive

PassiveLossless

SoftwareHardware

Apps

Libs, IPC

kernel

DigitalAnalog

Active

EngineeredTeleo-

dynamic

?

?Analog

Active

BiologicalTeleo-

dynamicNeed to

distinguish these

BiologicalTeleo-

dynamic

Probably need to distinguish these

bacteria

eukaryotes

animals

mammals

primates

humans

PassiveLossless

ActivePassive

PassiveLossless

Thermo-dynamic

RandomMorpho-dynamic

DesignedMorpho-dynamic

ActivePassive

PassiveLossless

Need to distinguish these

Statistic physics

“non-equilibrium”

Engineered

Huge gap

Passive

Lossless

Active

Passive

Passive

Lossless

Thermo-dynamic

RandomMorpho-dynamic

DesignedMorpho-dynamic

Active

Passive

Passive

Lossless

SoftwareHardware

Apps

Libs, IPC

kernel

DigitalAnalog

Active

EngineeredTeleo-

dynamic

?

?Analog

Active

BiologicalTeleo-

dynamic

Need to distinguish these

Huge gap