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

Passive Lossless 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 Passive Lossless 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 Active Passive Lossless Active Passive 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 Lossless Passive 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

Software Hardware Apps OS Libs, IPC kernel Digital Analog Active Passive Classical Quantum Lumped Distribute Passive 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

Software Hardware Apps OS Libs, IPC kernel Digital Analog Active Passive Classical Quantum Lumped Distribute Passive 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

Software Hardware Apps OS Libs, IPC kernel Digital Analog Active Passive Classical Quantum Lumped Distribute Passive 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.

Software Hardware Apps OS Libs, IPC kernel Digital AnalogActive Passive Classical Quantum Lumped Distribute What are the right cartoons?

Software Hardware Apps OS Libs, IPC kernel Digital Analog Active Passive ? ? Modularity of digital hardware What are the right cartoons?

Software Hardware Apps OS Libs, IPC kernel Digital Analog Active Passive Layers up here are very different from layers down here This needs clearer exposition

Software Hardware Apps OS Libs, IPC kernel Digital Analog 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 Apps OS Libs, IPC kernel Digital Analog Active Passive Layers here 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 Apps OS Digital Analog 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.Model 1.Universal laws 2.Universal architecture 3.Practical implementation Software Hardware Digital Analog

Software Hardware Apps OS Libs, IPC kernel Digital Analog Active Passive Layers here from layers here are 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

Active Passive Classical Quantum Lumped Distribute Passive Lossless The essence of multiscale physics

Passive Lossless 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 Passive Lossless 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) Active Passive Lossless Active Passive Lossless

These two pictures illustrate the essential challenge Not sure how to draw them to highlight this… Active Passive Lossless Active Passive Lossless Passive Lossless … and underscore the difference with the physics view

Passive Lossless Note: In our theory, “highly organized” and extreme nonlinearity play an essential role in active devices, and hence in life and technology. Active Passive Lossless In physics, even mild nonlinearity is synonymous with chaos, while “highly organized” and active devices are not treated at all.

Active Passive Lossless 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” Active Passive Lossless 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 Passive Lossless 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 Passive Lossless Passive Lossless Approximation arbitrarily good on finite (but arbitrarily long) time horizons. High dimensional lossless circuit passive Looks Really lossless

power supply Active Passive 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 supply active Looks Really passive High dimensional lossless circuit passive Looks Really 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 Looks Really 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.Digital 2.Distributed Active Passive Lossless 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

Active Passive Lossless “emergent, far from equilibrium, Prigogine, etc” Active Passive Lossless “highly organized” with extreme nonlinearity Huge gap Can we illustrate this with both automata and lumped circuits (ODEs)? (Later do distributed/PDE/QM)

power supply C active Looks Really 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. C active Looks Really passive power supply Look inside Passive too

Passive Lossless Active Passive Lossless Thermo- dynamic Random Morpho- dynamic ? ? Analog Active Biological Teleo- dynamic Deacon has these 3 kinds of systems “emergent, far from equilibrium, Prigogine, etc”

Passive Lossless Active Passive Lossless Thermo- dynamic Random Morpho- dynamic Designed Morpho- dynamic Active Passive Lossless Software Hardware Apps Libs, IPC kernel Digital Analog Active Engineered Teleo- dynamic ? ? Analog Active Biological Teleo- dynamic Need to distinguish these

Biological Teleo- dynamic Probably need to distinguish these bacteria eukaryotes animals mammals primates humans

Passive Lossless Active Passive Lossless Thermo- dynamic Random Morpho- dynamic Designed Morpho- dynamic Active Passive Lossless Need to distinguish these Statistic physics “non- equilibrium” Engineered Huge gap

Passive Lossless Active Passive Lossless Thermo- dynamic Random Morpho- dynamic Designed Morpho- dynamic Active Passive Lossless Software Hardware Apps Libs, IPC kernel Digital Analog Active Engineered Teleo- dynamic ? ? Analog Active Biological Teleo- dynamic Need to distinguish these Huge gap