Layering and physics

1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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)

8. 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

9. 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

10. 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

11. 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

12. 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

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

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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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.)

28. 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

29. 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

30. 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

31. 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

32. 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

33. 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

34. 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

35. 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

36. 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

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

38. 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

39. 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