Layering and physics
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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

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Layering and physics

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


Layering and physics

  • 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

Active

Active

Passive

Passive

Passive

Lossless

Lossless

Passive

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

Lossless


Layering and physics

  • 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

Active

Active

Passive

Passive

Passive

Lossless

Lossless

Infinite time horizon

Active

Passive

Lossless

Finite time horizon


Layering and physics

  • 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

Passive

Passive

Lossless

Lossless

Passive

Lossless


Caution

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


Big big picture

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

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

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 DavrolisEvoArch

  • 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

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


Layering and physics

Apps

Libs, IPC

OS

kernel

Software

Digital

Active

Hardware

Analog

Lumped

Passive

Passive

Distribute

  • 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

Lossless

Classical

Quantum


Layering and physics

Issues

Apps

Libs, IPC

OS

kernel

Software

Digital

Active

Hardware

Analog

Lumped

Passive

Passive

  • 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

Distribute

Lossless

Classical

Quantum


Layering and physics

Apps

OS

Libs, IPC

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.

Lumped

kernel

Passive

Distribute

Software

Active

Lossless

Digital

Passive

Hardware

Analog

Classical

Quantum


Layering and physics

Apps

OS

Libs, IPC

Software

kernel

Hardware

Digital

Analog

Active

Passive

Lumped

What are the right cartoons?

Distribute

Classical

Quantum


Layering and physics

Apps

Libs, IPC

OS

kernel

Software

Hardware

?

Digital

Modularity of digital hardware

Analog

?

What are the right cartoons?

Active

Passive


Layering and physics

This needs clearer exposition

Apps

Libs, IPC

Layers up here

OS

kernel

are very different

from layers down here

Software

Digital

Active

Hardware

Analog

Passive


Layering and physics

Need better nomenclature

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

Apps

Libs, IPC

OS

kernel

  • Whereas

  • SW is X of HW

  • Digital is X of Analog

  • What is “X”?

  • State, organization, large/thin…???

Software

Digital

Hardware

Analog


Layering and physics

Drawn a different way

Apps

Software

Libs, IPC

OS

Hardware

kernel

from layers here

Layers here

are very different

Digital

Analog

Active

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

Passive


Layering and physics

New idea: Turing style?

Apps

Software

OS

Hardware

Maybe start from here with Turing’s 3 step research:

hard limits, (un)decidability using standard model (TM)

Universal architecture achieving hard limits (UTM)

Practical implementation in digital electronics

Digital

Analog


Layering and physics

Essentials:

0.Model

Universal laws

Universal architecture

Practical implementation

Software

Hardware

Maybe start from here with Turing’s 3 step research:

hard limits, (un)decidability using standard model (TM)

Universal architecture achieving hard limits (UTM)

Practical implementation in digital electronics

Digital

Analog


Layering and physics

Apps

Software

Libs, IPC

OS

Hardware

kernel

from layers here

are very different

Digital

Layers here

Important questions

Analog

  • 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

Active

Passive


Layering and physics

Active

Passive

The essence of multiscale physics

Lumped

Passive

Distribute

Lossless

Classical

Quantum


Layering and physics

  • 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

Active

Active

Passive

Passive

Passive

Lossless

Lossless

Passive

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

Lossless


Layering and physics

  • 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)

Active

Active

Passive

Passive

Passive

Lossless

Lossless


Layering and physics

  • These two pictures illustrate the essential challenge

  • Not sure how to draw them to highlight this…

Active

Active

Passive

Passive

Passive

Lossless

Lossless

… and underscore the difference with the physics view

Passive

Lossless


Layering and physics

Note:

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

Active

Passive

Passive

Lossless

In physics, even mild nonlinearity is synonymous with chaos, while

“highly organized” and active devices are not treated at all.

Passive

Lossless


Layering and physics

Note:

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

Active

Passive

These are extremely different, and need to make this clear.

Passive

Lossless

“emergent, far from equilibrium, Prigogine, etc”

Active

Passive

In physics, even mild nonlinearity is synonymous with chaos, while

“highly organized” and active devices are not treated at all.

Passive

Lossless


Layering and physics

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

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

Passive

Passive

Lossless

Lossless

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.)


Layering and physics

Our theory:

Idea is that lossless are dense in passive

Passive

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

Passive

Lossless

Lossless

Really lossless

Looks

High dimensional lossless circuit

passive


Layering and physics

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

Active

Active

Passive

Passive

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

Really passive

Looks

power supply

active


Layering and physics

  • 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?)

Really passive

Looks

power supply

active

Really lossless

Looks

High dimensional lossless circuit

passive


Layering and physics

  • 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

Really lossless

Looks

High dimensional lossless circuit

passive


Layering and physics

Active

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

Digital

Distributed

Passive

Passive

Lossless

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

Distributed: Natural direction to connect with physics and QM


Layering and physics

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

(Later do distributed/PDE/QM)

“emergent, far from equilibrium, Prigogine, etc”

“highly organized” with extreme nonlinearity

Active

Active

Huge gap

Passive

Passive

Passive

Passive

Lossless

Lossless


Layering and physics

New idea inspired by Deacon

Really passive

Looks

C

power supply

active

  • 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.

Passive too

Really passive

Look inside

power supply

Looks

C

active


Layering and physics

Biological

Teleo-

dynamic

Deacon has these 3 kinds of systems

Random

Morpho-

dynamic

?

Thermo-

dynamic

“emergent, far from equilibrium, Prigogine, etc”

Active

?

Analog

Passive

Passive

Active

Passive

Lossless

Lossless


Layering and physics

Engineered

Teleo-

dynamic

Biological

Teleo-

dynamic

Need to distinguish these

Apps

Libs, IPC

Random

Morpho-

dynamic

Designed

Morpho-

dynamic

kernel

Software

?

Thermo-

dynamic

Hardware

Active

Active

Digital

?

Analog

Analog

Passive

Passive

Passive

Active

Active

Passive

Passive

Lossless

Lossless

Lossless


Layering and physics

Probably need to distinguish these

Biological

Teleo-

dynamic

humans

primates

mammals

animals

eukaryotes

bacteria


Layering and physics

Need to distinguish these

Statistic physics

Engineered

“non-

equilibrium”

Designed

Morpho-

dynamic

Random

Morpho-

dynamic

Thermo-

dynamic

Huge gap

Active

Active

Passive

Passive

Passive

Passive

Passive

Lossless

Lossless

Lossless


Layering and physics

Engineered

Teleo-

dynamic

Biological

Teleo-

dynamic

Need to distinguish these

Apps

Libs, IPC

Random

Morpho-

dynamic

Designed

Morpho-

dynamic

kernel

Software

?

Thermo-

dynamic

Hardware

Huge gap

Active

Active

Digital

?

Analog

Analog

Passive

Passive

Passive

Active

Active

Passive

Passive

Lossless

Lossless

Lossless


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