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John Doyle on network architecture and control theory

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Season 2011
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Why do bacteria have more elegant network architecture than the internet , and what can both teach us about building robust, evolvable systems? John Doyle unpacks the universal design principle of layered constraints that biology and technology share. Subscribe for more from the Convergent Science Network podcast series. John Doyle is a control theorist and mathematician who found himself drawn to biology by a simple observation: the bacterial biosphere has one of the most robust and evolvable architectures on Earth. It evolved into us, yet continues to adapt with remarkable speed on every timescale , rearranging protein networks in seconds, swapping genes across species over generations. Doyle argues that this dual capacity for rapid robustness and rapid evolvability stems from a shared architectural principle: layered constraints that deconstrain. The concept, borrowed from biologists Gerhardt and Kirschner, holds that a few wisely chosen constraints , like ATP as a universal energy carrier or TCP/IP as a packet protocol , create platforms that enable enormous flexibility above them. In bacteria, core metabolic protocols have persisted for billions of years, yet they enable wildly dynamic responses to environmental challenges. In technology, operating systems sit between hardware and applications, enabling the plug-and-play modularity we take for granted. Doyle argues that layering is the highest-level expression of modularity, and that much of the scientific literature on modularity misses this point by focusing on component-level decomposition rather than the architectural constraints that make modularity possible. The interview draws a sobering contrast between biological and engineered systems. While bacterial biochemistry appears spectacularly well-designed from an engineering perspective, refined over billions of years of selection, human-built large-scale systems are profoundly unsustainable. Doyle is blunt: our energy, transportation, water, and food networks have recognizable design flaws, and the interplay between technology, markets, and policy is the least understood system of all. He uses a clothing metaphor to make architecture accessible: garments have both an inner-to-outer layering (skin layer, insulation, weather protection) and a compositional layering (fiber to yarn to cloth to garment), illustrating how different dimensions of constraint combine synergistically. Doyle also highlights a critical lesson from both biology and the internet: if you make a mistake in a core protocol and build extensively on top of it, correction becomes nearly impossible. The internet’s early design choices, made by operating systems engineers who won a historical battle against information theorists, are now deeply embedded , brilliant in some respects, flawed in others, and extraordinarily difficult to change.

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Both the triumphs of humanity and its most evil deeds have resulted from collaboration. In a time where humanity is required to aspire to the former and minimize the latter, the question arises of how collaboration arises and why it fails. Surprisingly, this phenomenon, so central to who we are, is not well understood. Hence, a collaborative effort is required to understand collaboration in its full biological, psychological, sociological, cultural, and economic complexity and to translate this understanding into operational impact. This series of podcasts is one step toward achieving these complementary goals. The Collaboration Podcast presents interviews with people who are central orchestrators of collaboration in various domains including business, government, science, art, health, sustainability, and the military. The discussions were conducted by Prof. Dr. Paul F.M.J. Verschure and members of the Program Advisory Committee of the Ernst Strungmann Forum on Collaboration (https://www.esforum.de/forums/ESF32_Collaboration.html) during 2021 and had the goal to sketch a map of opportunities, challenges, and obstacles in human collaboration. The forum took place in May 2022, and now we would like to share this series of interviews with a broader audience. The full report of the Forum will be published in 2023 by MIT Press. The podcast was produced by the Convergent Science Network (https://www.convergentsciencenetwork.org/). Context: The stability of social systems depends critically on realizing sustainable methods of “collaboration,” yet how and by which means collaboration is achieved is not clearly understood; neither are the conditions or processes that lead to its breakdown or failure. Collaboration can be understood as cooperation between agents toward mutually constructed goals. Part of the reason for our lack of understanding is that the phenomenon of collaboration is, by nature, a highly multidisciplinary problem, and effective research into its complexities has been difficult to achieve across the broad range of scientific and technical disciplines involved. The need for a fundamental understanding of collaboration, however, has become increasingly important. Not only does humankind demand answers as it attempts to address critical challenges at multiple scales (e.g., climate change, migration, enhanced automation, social and economic inequality), but ever-increasing technological and economic means of interconnecting people and societies are disrupting long-established, familiar patterns of how we interact. Radical technological changes that are ongoing have the potential to reshape collaboration in ways that are currently hard to predict or influence (e.g., by altering configurations in interaction, information creation, and modes of communication). On one hand, such changes could disrupt hitherto stable forms of collaboration by affecting critical communication channels and traditional roles, as can be observed in the rapidly changing patterns in governance, commerce, and social interaction. Conversely, technology could lead to the emergence of novel, successful forms of collaboration that deviate from traditional “hierarchical” architectures. Evidence of this can be seen in areas as diverse as highly automated manufacturing plants, the open science movement, collaborative software repositories, user-centered services, and the sharing of economy-based modes of organization. Without a fundamental understanding of the mechanisms, processes, and boundary conditions of collaboration, it is not possible to evaluate or predict which of these possible scenarios are sustainable or even plausible. The Forum “How Collaboration Arises and Why it Fails” (May 8–13, 2022, Location: Frankfurt am Main, Germany) Chairs: Andreas Roepstorff and Paul Verschure Program Advisory Committee: Jenna Bednar, Julia R. Lupp, Bhavani R. Rao , Andreas Roepstorff, Ferdinand von Siemens, and Paul Verschure

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  • fast_forward00:00:00 - Okay, so I'm here with John Doyle. We're still both at the KITP for the workshop
  • fast_forward00:00:07 - on network architecture.
  • fast_forward00:00:08 - This is Paul Fouchure, the Convergence Science Network.
  • fast_forward00:00:11 - Work and john you you presented your work on on architectures as a control engineer,
  • fast_forward00:00:19 - you looked at at architectures in
  • fast_forward00:00:22 - particular focusing on single cellular organisms right
  • fast_forward00:00:26 - so so and that seems a bit of counterintuitive move
  • fast_forward00:00:29 - for a control engineer but why why did
  • fast_forward00:00:31 - you move to the to biology well i
  • fast_forward00:00:35 - got interested in biology you know around 10 years ago or
  • fast_forward00:00:38 - 10 or 15 years ago um i'd been
  • fast_forward00:00:41 - working initially motivated i'm pretty much
  • fast_forward00:00:44 - a very much at the theoretical end my phd is in math but i
  • fast_forward00:00:47 - was early mostly motivated by aerospace control problems because they were doing
  • fast_forward00:00:52 - in you know the 80s the most cutting edge things but then in the 90s networks
  • fast_forward00:00:56 - started coming you know we we the internet really grew to prominence um didn't
  • fast_forward00:01:01 - really have a theory so i got interested more in networks and uh just kind of gravitated towards,
  • fast_forward00:01:07 - um bacteria because i had uh colleagues at caltech who worked on it it was kind
  • fast_forward00:01:15 - of a convenient um but also um i've found that they have in nature one of the most remarkable.
  • fast_forward00:01:24 - And evolvable architectures the architecture of
  • fast_forward00:01:27 - the bacterial biosphere um for one thing it evolved into us but it also continues
  • fast_forward00:01:34 - to have tremendous evolvability and robustness and so i want to understand what
  • fast_forward00:01:39 - gave that biosphere that robustness and i think we've now found.
  • fast_forward00:01:47 - Deep principles that are very consistent with what we're learning about how
  • fast_forward00:01:53 - to build technology networks so it was examples in addition to the internet
  • fast_forward00:01:59 - of really sophisticated
  • fast_forward00:02:00 - evolvable networks that we
  • fast_forward00:02:01 - could compare but now before you go to internet and talk about robustness,
  • fast_forward00:02:06 - What's what are these principles of robustness that?
  • fast_forward00:02:09 - That you see these biological systems. Well so one thing is they are.
  • fast_forward00:02:17 - They get robustness by having a plug-and-play, sort of what we in technology
  • fast_forward00:02:25 - call a plug-and-play architecture.
  • fast_forward00:02:27 - That is, on almost every timescale you look, they can quickly adjust to,
  • fast_forward00:02:35 - say, changing environments.
  • fast_forward00:02:39 - And so on very short timescales, they can very quickly rearrange their protein
  • fast_forward00:02:44 - networks to respond as they needed. But on longer time scales,
  • fast_forward00:02:48 - they can change their genomes very rapidly.
  • fast_forward00:02:51 - They can swap genes.
  • fast_forward00:02:54 - So the bacterial biosphere as a whole almost acts as one large gene pool.
  • fast_forward00:03:00 - So on long time scales, they can very quickly evolve their genome.
  • fast_forward00:03:05 - And on short time scales, they're very quick in responding to the environment.
  • fast_forward00:03:08 - And what's interesting is I think their architecture makes both of those better.
  • fast_forward00:03:13 - And that's one of the things that I think has surprised the biologists a bit
  • fast_forward00:03:16 - is that the proper architecture can facilitate both rapid robustness on short
  • fast_forward00:03:22 - time scales and rapid evolvability on long time scales.
  • fast_forward00:03:26 - Else but now in your so
  • fast_forward00:03:30 - this is a bit the phenomena right these are
  • fast_forward00:03:32 - the phenomena we want to understand but not in in terms of principles you you
  • fast_forward00:03:37 - talked about layering you talked about constraints um and you talk about let's
  • fast_forward00:03:44 - say feedback so so how does this now relate to these phenomena that we're trying
  • fast_forward00:03:48 - to look at right well the way we the way in engineering Engineering,
  • fast_forward00:03:52 - we think about, and mathematics,
  • fast_forward00:03:55 - the way we think about describing architecture is in terms of constraints.
  • fast_forward00:03:58 - And the IEEE Transactions paper on architecture takes that point of view,
  • fast_forward00:04:09 - as does the recent PNAS paper aimed at neuroscientists, and it tries to explain
  • fast_forward00:04:12 - how we think about constraints.
  • fast_forward00:04:14 - And one of the place constraints come from is the behavior of the system as
  • fast_forward00:04:18 - whole is constrained by the environment it must act in and if it's going to survive and persist.
  • fast_forward00:04:26 - And then there's usually constraints on the components which come up from the bottom.
  • fast_forward00:04:31 - What can you build? What are the resources? What can you build it out of?
  • fast_forward00:04:35 - And then additionally, there are constraints that come out of theory in some sense.
  • fast_forward00:04:41 - I mean, theory reflects reality. But the idea is that much of engineering theory
  • fast_forward00:04:47 - is about the additional hard limits that come either out of limitations on computing,
  • fast_forward00:04:54 - which is Turing, limitations on communication, which is Shannon.
  • fast_forward00:04:57 - And what I talked about in this KTP lectures is the Bode limits on robustness and feedback systems.
  • fast_forward00:05:05 - These aren't well known in the scientific community.
  • fast_forward00:05:09 - And they're a little bit fragmented even within engineering.
  • fast_forward00:05:11 - But we're trying to build unified theories now.
  • fast_forward00:05:14 - But the final constraint then is the design choices that are made,
  • fast_forward00:05:18 - either through evolution or through deliberate design.
  • fast_forward00:05:21 - And Gerhardt and Kirshner, who are biologists, have a very nice phrase for what
  • fast_forward00:05:27 - a good architecture is, is constraints that deconstrain.
  • fast_forward00:05:29 - And you choose a few constraints, and it might be, for example,
  • fast_forward00:05:34 - the use of ATP as an energy carrier, the use of NADH as a redox carrier, the codons.
  • fast_forward00:05:41 - In the internet, it would be the packet formats and the TCPIP protocol.
  • fast_forward00:05:47 - Those are chosen wisely. Then they free you up to use that as a platform.
  • fast_forward00:05:53 - For both very robust networks and very evolvable networks and that's
  • fast_forward00:05:56 - where layering comes in is because layering is seems to
  • fast_forward00:05:59 - be the strategy that both engineers and biology have adopted to make flexible
  • fast_forward00:06:04 - uh robust evolvable systems and what layering is is a particular way of structuring
  • fast_forward00:06:12 - the chosen constraints and so that's what we talked a lot about it's not a simple topic
  • fast_forward00:06:17 - but um it's familiar enough
  • fast_forward00:06:20 - to people from neuroscience and from cell biology and
  • fast_forward00:06:24 - from engineering that there is becoming
  • fast_forward00:06:27 - i'm hoping a common language which is what i'm trying to promote about
  • fast_forward00:06:30 - what is layering but then how do
  • fast_forward00:06:33 - layers map to modules right because also in
  • fast_forward00:06:36 - your analysis you you stress the notion of
  • fast_forward00:06:39 - modules quite a bit yeah so how should i see this
  • fast_forward00:06:42 - is related to layers and modules so layering is
  • fast_forward00:06:46 - i think the the highest level view of what modularity is and i think it's the
  • fast_forward00:06:53 - most uh important aspect of modularity um i think there's been a lot written
  • fast_forward00:06:59 - in the scientific community lately about modularity that is i think
  • fast_forward00:07:02 - naive and not wrong so much as not the most important aspects of Larry.
  • fast_forward00:07:12 - And so the thing that people are probably most familiar with modularity is the laptop that they have.
  • fast_forward00:07:19 - And they can, or they download this podcast and it just runs immediately.
  • fast_forward00:07:24 - And so the idea is that the kind of modularity it has is that you can download
  • fast_forward00:07:28 - new software, you can download new podcasts and they immediately work largely
  • fast_forward00:07:35 - independent of which computer you happen to have.
  • fast_forward00:07:38 - Um, and you can, you can go to the store, uh, order online, uh,
  • fast_forward00:07:44 - hardware that you just plug in.
  • fast_forward00:07:46 - And what makes that possible is what's hidden,
  • fast_forward00:07:50 - what you don't see, which is the operating systems and that,
  • fast_forward00:07:54 - that they sit between all these applications and the hardware and,
  • fast_forward00:07:59 - uh, and allow you the modularity.
  • fast_forward00:08:01 - And the idea is that the modularity looks so simple and so easy when it works.
  • fast_forward00:08:05 - It usually either works perfectly or not at all.
  • fast_forward00:08:10 - But it's the layered structure that allows that to happen.
  • fast_forward00:08:16 - And we believe that's the same thing in biology. It's what allows for horizontal
  • fast_forward00:08:21 - gene transfer as a mechanism for bacteria to evolve rapidly, for example.
  • fast_forward00:08:27 - And that so they have the same sort of
  • fast_forward00:08:30 - plug and play modularity that our technologies
  • fast_forward00:08:33 - have when they're good but now does modularity always
  • fast_forward00:08:37 - imply let's say a static organization of
  • fast_forward00:08:41 - of a structure or can you
  • fast_forward00:08:44 - have let's say virtual modules that change if
  • fast_forward00:08:47 - you want their boundaries and their interfaces to other modules so
  • fast_forward00:08:51 - how could i think about this well i think i think
  • fast_forward00:08:54 - the constraints a deconstraint way of looking at
  • fast_forward00:08:57 - this is that there's always different time
  • fast_forward00:09:00 - scales for different aspects of the architecture there's there's usually
  • fast_forward00:09:03 - things that are persistent on very long time scale so an example of something
  • fast_forward00:09:07 - that is persistent on very long time scales in biology would be the use of atp
  • fast_forward00:09:11 - as an energy carrier it's both universal across all of biology and probably
  • fast_forward00:09:16 - has been there for billions of years um the the uh the codon usage is nearly universal and has
  • fast_forward00:09:23 - probably been there for also similar long times.
  • fast_forward00:09:26 - So those are constraints that really persist for a long, long time.
  • fast_forward00:09:31 - What they facilitate, however, is tremendously dynamic responses.
  • fast_forward00:09:38 - And so by having a common energy carrier throughout the cell.
  • fast_forward00:09:42 - Individual modules don't have to worry about an energy source.
  • fast_forward00:09:47 - Core metabolism make sure that that
  • fast_forward00:09:50 - the atp charge is maintained as constant as
  • fast_forward00:09:53 - possible and an enormous amount
  • fast_forward00:09:56 - of sophisticated control goes on inside the cell or inside our bodies to maintain
  • fast_forward00:10:01 - that energy charge um and then what that allows you to do is on again on every
  • fast_forward00:10:07 - time scale adapt very dynamically to the challenges so what you see is the combination of a
  • fast_forward00:10:13 - permanent constraint that's fixed forever.
  • fast_forward00:10:17 - But that choice, if done wisely, enables very, very much dynamics.
  • fast_forward00:10:23 - Now what happens is on fast time scales, even what's a module is changing very rapidly.
  • fast_forward00:10:31 - So for example, just in say in metabolism, you'll the depending on what,
  • fast_forward00:10:39 - what biosynthetic pathways are needed, we may come and go on demand.
  • fast_forward00:10:45 - Um and and what
  • fast_forward00:10:48 - what and depending again on what's in the environment what
  • fast_forward00:10:51 - was a food source may go away
  • fast_forward00:10:54 - and now all of a sudden there that's a say an amino acid that
  • fast_forward00:10:57 - you have to start manufacturing again this is bacteria are the
  • fast_forward00:11:00 - most plastic in this regard we're not very flexible right but
  • fast_forward00:11:03 - at the same time we can eat all sorts of things um and so we're
  • fast_forward00:11:06 - omnivores and our ability to do that is
  • fast_forward00:11:09 - again because we have a
  • fast_forward00:11:12 - sort of front-end process that turns
  • fast_forward00:11:15 - whatever we eat into a few basic building blocks from
  • fast_forward00:11:18 - which we synthesize all of our tissues okay but now if you
  • fast_forward00:11:21 - if you describe it like this coming from engineering you
  • fast_forward00:11:24 - seem to be implying that actually engineering also
  • fast_forward00:11:28 - works according to these principles yeah is it actually true
  • fast_forward00:11:31 - for for sort of the larger systems
  • fast_forward00:11:34 - that we have put together as humans well one.
  • fast_forward00:11:37 - Of the problems is the larger systems we put together as humans
  • fast_forward00:11:40 - are not sustainable and so they
  • fast_forward00:11:44 - have design flaws and one of the ironies is uh
  • fast_forward00:11:47 - when i first started getting interested in biology the biologists
  • fast_forward00:11:50 - would tell me oh you know you're going to see biology's kludgy it's
  • fast_forward00:11:53 - an accident it's a tinker evolution's the tinkerer um that's
  • fast_forward00:11:57 - very true but in fact you don't
  • fast_forward00:12:00 - you don't tend to see uh the gross
  • fast_forward00:12:03 - design flaws in biology that are all over in engineering and the reason for
  • fast_forward00:12:10 - that is that that much of the core uh these core protocols in biology have had
  • fast_forward00:12:14 - millions to billions of years to evolve um and And sure,
  • fast_forward00:12:21 - we have some funny aspects of because we were built out of fish parts.
  • fast_forward00:12:25 - And so building a human out of fish parts is going to have some problems.
  • fast_forward00:12:30 - But the the biochemical layer.
  • fast_forward00:12:35 - It seems to be spectacularly uh well designed from an engineering point of view,
  • fast_forward00:12:40 - so i think and so what the what you what's hard to do with any of these systems
  • fast_forward00:12:44 - is to sort out what is a hard necessary constraint and trade-off and and what is a,
  • fast_forward00:12:52 - an accidental design flaw either of evolution or of engineering and then you
  • fast_forward00:12:56 - see both um so the problem with engineering though is in our at the big scale
  • fast_forward00:13:02 - we're profoundly unsustainable,
  • fast_forward00:13:05 - In our energy, transportation, waste, water, food, almost everything.
  • fast_forward00:13:10 - And the more you learn about it, the more disturbing it is.
  • fast_forward00:13:13 - The more I understand ecosystems and their interactions with our technologies,
  • fast_forward00:13:17 - the more frightened I am.
  • fast_forward00:13:20 - I think I'm, funny, I'm kind of a global warming skeptic. I think it's much
  • fast_forward00:13:24 - worse than the scientists are telling us.
  • fast_forward00:13:27 - I don't think they have the tools yet to do the sort of worst case scenario
  • fast_forward00:13:31 - analysis that we do in engineering.
  • fast_forward00:13:34 - And we take for granted that our planes rarely crash, and that's because engineers
  • fast_forward00:13:40 - do that kind of worst-case analysis.
  • fast_forward00:13:42 - So we do certain things very, very well in engineering. We've gotten to the
  • fast_forward00:13:45 - point where we can build platforms like airplanes and vehicles that are very
  • fast_forward00:13:50 - sophisticated and very robust, but we're not so good at the networks yet.
  • fast_forward00:13:56 - And the internet was a spectacular innovation, but it now has a lot of recognizable design flaws,
  • fast_forward00:14:02 - but we've built so much on top of it
  • fast_forward00:14:05 - that it's hard to now change
  • fast_forward00:14:08 - those if you make a if you make a mistake in
  • fast_forward00:14:11 - a core protocol and then you build a
  • fast_forward00:14:13 - lot on top of it it can be very hard to now change that right so if atp wasn't
  • fast_forward00:14:18 - a good energy carrier biology would have very little choice but to just stick
  • fast_forward00:14:24 - with it yeah exactly so but now so so what but this seems to be also a conundrum
  • fast_forward00:14:29 - a bit in your, well, these ladies are getting a bit noisy, right?
  • fast_forward00:14:32 - So maybe we should change location.
  • fast_forward00:14:39 - Because I have to filter all this stuff out later again. Keep on going.
  • fast_forward00:14:44 - Because what I want to bring up now is that in your, so the one that now we
  • fast_forward00:14:49 - see, if you want, a bit of a contrast here, right, between your analysis of
  • fast_forward00:14:55 - life, of biology, biological systems, Thank you.
  • fast_forward00:15:00 - And the success of this framework you see in nature, in actual development of
  • fast_forward00:15:06 - these large integrated systems like internet, right?
  • fast_forward00:15:11 - Because like you said earlier, if you look at how internet has been put together,
  • fast_forward00:15:15 - it's one big kludge, right? It's not really.
  • fast_forward00:15:18 - But it was based on a few very profound and visionary insights.
  • fast_forward00:15:28 - And it's not necessarily from from control engineering
  • fast_forward00:15:32 - in any way no no in fact it was operating systems
  • fast_forward00:15:35 - uh and engineers right and um and
  • fast_forward00:15:39 - they now they knew elementary
  • fast_forward00:15:42 - control theory and made quite a bit of use of
  • fast_forward00:15:45 - it at a time and they did it at a
  • fast_forward00:15:47 - time when uh communication theory was almost
  • fast_forward00:15:50 - entirely dominated by information theory and
  • fast_forward00:15:54 - so decades ago there was a major battle between sort of the information theorists
  • fast_forward00:15:59 - and the operating systems people over how to build these networks and and for
  • fast_forward00:16:09 - almost accidental reasons in the u.s.
  • fast_forward00:16:11 - The OS system people won and they built the internet and now what's happened
  • fast_forward00:16:16 - in the last 10 to 15 years as we really now have a much more integrated theory
  • fast_forward00:16:21 - that includes information and control theory and And greatly explains both the
  • fast_forward00:16:26 - successes and the flaws in the current Internet architecture.
  • fast_forward00:16:29 - And one of the big challenges now is to use those insights to build a next generation.
  • fast_forward00:16:35 - But the Internet's a good example of a situation where the really brilliant
  • fast_forward00:16:40 - engineers are always way out ahead of any of the theoretical understanding.
  • fast_forward00:16:46 - And so we're always playing catch up with the best.
  • fast_forward00:16:49 - It's the same way, I think, in medicine. And I think the best doctors are always
  • fast_forward00:16:54 - making leaps way out ahead of any scientific basis.
  • fast_forward00:16:58 - And then, and I think the scientific community in some ways quit doing this,
  • fast_forward00:17:03 - is trying to come back and help the doctors flesh out the details.
  • fast_forward00:17:07 - I think the doctors have, for a few decades, really been on their own as the
  • fast_forward00:17:12 - scientific community pursued genomics as the answer to everything.
  • fast_forward00:17:17 - Thing right but the only problem is of course that with respect
  • fast_forward00:17:20 - to internet we might be already so much committed into
  • fast_forward00:17:23 - one certain way of structuring the system that we have no chance ever to to
  • fast_forward00:17:28 - re-engineer or redeploy anything that's a big worry yeah that's a big worry
  • fast_forward00:17:32 - and so um i'm a little less concerned about that than i was from a technical
  • fast_forward00:17:39 - point of view in the sense that i think.
  • fast_forward00:17:42 - The architectures that I think are the most appealing that are being proposed
  • fast_forward00:17:48 - actually have the internet as kind of a lousy special case.
  • fast_forward00:17:53 - So I think it could be organically grown around a new architecture and then
  • fast_forward00:17:58 - it's a little bit like a city.
  • fast_forward00:18:00 - You could sort of leave the old parts as a kind of scruffy old neighborhood.
  • fast_forward00:18:07 - I think that's possible. I think the challenge is going to be probably in these
  • fast_forward00:18:13 - big systems, not technological, but the interplay between technology, markets, and policy.
  • fast_forward00:18:20 - And I think if there's a system that we understand most poorly,
  • fast_forward00:18:27 - it's the interplay between technology and policy and markets.
  • fast_forward00:18:30 - And right now, extremely ideologically driven, not the least bit rigorous.
  • fast_forward00:18:40 - And I would say of the fields that I've looked at, I think the area of economics,
  • fast_forward00:18:46 - and finance and markets in that world is among the most ideological driven of any.
  • fast_forward00:18:53 - And so the problems ultimately there may not be just purely technological. Right.
  • fast_forward00:18:56 - But now to get back to the first architecture, right?
  • fast_forward00:19:01 - So in some of those Also in the discussions here in this workshop.
  • fast_forward00:19:07 - We want to get to some notion of, let's say, brain architecture.
  • fast_forward00:19:11 - But then let's first try to understand what we mean with architecture.
  • fast_forward00:19:15 - And you have used this analogy with, let's say, a garment to illustrate how
  • fast_forward00:19:22 - you think about architecture.
  • fast_forward00:19:23 - Could you maybe explain? Yeah, I've been trying to look for an example of an
  • fast_forward00:19:30 - architecture that would be so simple you could explain it to a school child, right?
  • fast_forward00:19:38 - Because I think we need these concrete metaphors. And of course,
  • fast_forward00:19:42 - anything that simple is dangerous because it can't have many of the important
  • fast_forward00:19:47 - features that we care about. But the reason I picked on clothing is everybody knows about clothing.
  • fast_forward00:19:52 - And the thing about clothing is that it shows two very different kind of layers.
  • fast_forward00:19:57 - And the PNAS paper that's now online and soon to be in print discusses that.
  • fast_forward00:20:04 - And the idea is that the most familiar one is sort of inner to outer wear.
  • fast_forward00:20:08 - So you have an inner layer that's
  • fast_forward00:20:09 - close to the skin. And I'm imagining clothing in a cold environment.
  • fast_forward00:20:14 - We're naturally adapted to live in a very hot environment and don't really even
  • fast_forward00:20:20 - need clothing in a hot environment.
  • fast_forward00:20:21 - But where we really need clothing is harsh, cold environments.
  • fast_forward00:20:25 - And so in those environments, we often have an inner layer that's close to the skin. Right.
  • fast_forward00:20:30 - And then you have an outer layer that protects you against the weather from
  • fast_forward00:20:34 - the outside, wet, cold, wind.
  • fast_forward00:20:36 - And then a middle layer that provides warmth. And what's interesting about this
  • fast_forward00:20:41 - view of layering is it's so simple anybody can understand it.
  • fast_forward00:20:44 - What you have is three very different kinds of layers that combine synergistically
  • fast_forward00:20:49 - to create a whole that feels good to the skin, protective of the outside,
  • fast_forward00:20:55 - and as warm as necessary.
  • fast_forward00:20:56 - And tremendous plug
  • fast_forward00:20:59 - and play modularity you can swap in different parts
  • fast_forward00:21:02 - of the garments but it also shows another
  • fast_forward00:21:06 - point which is enormously confusing within
  • fast_forward00:21:09 - the scientific community not in engineering but most
  • fast_forward00:21:12 - combinations of garments do not produce functional outfits
  • fast_forward00:21:15 - they really while there's enormous variability
  • fast_forward00:21:18 - in what you can do there's still
  • fast_forward00:21:21 - pretty strict strict rules in what can go where or they don't
  • fast_forward00:21:24 - work at all so you can't you can't make underwear
  • fast_forward00:21:27 - outerwear you can't make socks into hats
  • fast_forward00:21:30 - you can't you know for the most part so that's one
  • fast_forward00:21:34 - way of looking at the layering and but another is the composition of fiber which
  • fast_forward00:21:42 - is then um spun into yarn which is then knit or woven into cloth which is then
  • fast_forward00:21:49 - sewn into garments A whole other dimension of layering.
  • fast_forward00:21:53 - Very different than the first, but again, illustrates properties both of biology and technology.
  • fast_forward00:22:01 - And so, in that PNS paper, I try to flesh out that example as one that I think would help.
  • fast_forward00:22:08 - Both engineers and neuroscientists talk to each other because it's a,
  • fast_forward00:22:13 - it's such a simple example.
  • fast_forward00:22:14 - Now the danger is it's too simple. It doesn't have dynamics.
  • fast_forward00:22:19 - The, the, these compositional forms are very, very simple.
  • fast_forward00:22:23 - The way clothing just sits on top of, you know, one layer of clothing sits on
  • fast_forward00:22:27 - top of another doesn't really, doesn't really lock in like you would in a,
  • fast_forward00:22:33 - in, in many other systems. There's a lot of things that are wrong with the thing,
  • fast_forward00:22:35 - but it gets a starting point.
  • fast_forward00:22:37 - Okay, that's good. So if we have layers, we also have different,
  • fast_forward00:22:41 - let's say, processes that give rise to the key properties of these layers.
  • fast_forward00:22:45 - But now, if I would like to have, let's say, a more parametric description of
  • fast_forward00:22:49 - such an architecture, what are the key, let's say, parameters that I should worry about?
  • fast_forward00:22:55 - Well, in this, I mean, again, using this, stretching this probably to the point where we'll break,
  • fast_forward00:22:59 - um there is there is the rules by which the constraints by which you can assemble
  • fast_forward00:23:10 - so like i said most most combinations of garments do not produce a functional outfit,
  • fast_forward00:23:19 - and there's a few rules that you can write down in principle anyway and we certainly
  • fast_forward00:23:27 - learn them And children learn them as to what make a functional garment.
  • fast_forward00:23:32 - But the interesting thing is, once the garments are selected...

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