SIAM Distinguished Speaker Seminar with Prof. Reza Negarestani

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Okay, ma'am. Okay. Yeah, that sounds good. Risa just said he was going to be on at around 2.40 or 2.45. so if you could try and just hop on before he hops on just to make sure everything's working fine, that'd be great alright, talk to you in a couple

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Thank you. Hello, Ivan.

How are you? Hey, how's it going? Nice to... I'm not sure if I can say meet you or not. Well, that's good enough. yeah yeah at least as much as you can meet anybody these days no this is I'm really excited and thank you so much for inviting me to this I'm really actually very excited it's not going to be anything special though I should warn you it's merely introductory I mean otherwise you are not going to, you're not able to deliver a revolutionary talk or anything of that caliber hour, if you can.

And the thing is that I actually only thought that, okay, I'm going to go with a really introductory stuff. And then hopefully, you know, we can, we can unpack it during the Q&A. That's it. Yeah, I think that's definitely the right approach. I mean, obviously, you're bringing a lot of people from your Twitter following and people you know from various walks of life will be here. But in terms of the audience from the UT community, I can say that definitely in terms of philosophical background, understanding of some of the terminology and concepts you might be using were engineers and mathematicians. So I'm sure some of your conceptual

You probably know that my actual background was engineering and applied mathematics. Yeah, yeah, yeah. I know you have a degree from Systems engineering. OK. Systems analysis, yeah. Dynamic systems mostly, yeah. Yeah, yeah. I find that, I find dynamic, my personal research has actually been moving in the direction of dynamic systems recently. So this is a really nice kind of, a lot of things have come together for this time. To be honest with you, I mean, look, I mean, between the two of us, I hope that there isn't, no one is, and we are not recording it. Are we recording it? We are recording. We are recording,

but I will probably end up cutting this first 20 minutes. Yes. No, I mean, engineering can be an extremely frustrating discipline because many people who go to engineering are actually quite bright people and they have a certain kind of worldview, not just there to basically drive in screws and do this and do that. they have they they want a certain kind of engineering worldview of the world right yeah but the thing is that when they go to engineering the school they become utterly bored out of their soul and that drives so many people out of engineering the school at the end to other kinds of disciplines and that's kind of very unfortunate because I think

engineering is absolutely a philosophical enterprise. Yeah, yeah, I think that, so I've definitely, so I come from more of a applied math background, that was what my undergraduate degree was in, and I really enjoyed kind of the pure math classes I took as an undergraduate, as well as the philosophy classes I took. And I came to UT for this program, and I've been taking some classes in the engineering department. And yeah, I can really echo that sentiment in the sense that I really value and appreciate the way I guess you talk about engineering and the way I think

about engineering broadly, but it can be the nuts and bolts of it. Absolutely, absolutely. And I love that so many people are afraid of engineering, even some scientists. But look, the greatest scientific enterprises have been built upon the shoulder of a titan called engineering. Simple as that. Yeah, yeah, yeah, exactly, exactly. Yeah, so I think that, I don't know, I find it, I guess, interesting that I kind of surprised myself almost by taking this route, because as you've talked about, I think in your NERA editions interview, you talk about how kind of any kind

of absolutism or objectivism or rationalism is so kind of out of vogue these days, and especially in the 70s and 80s and at the same time I've taken this engineering trajectory I've been doing a lot of reading or some reading for fun in the kind of currents that are that are very anti that like I read Duluth the first time recently and things like that that take you on some different intellectual trajectories entirely. So your career trajectory

kind of links some of the disparate things I've looked at recently. You see, the thing is that even though I no longer, I mean, sure, I mean, philosophers go through extreme forms of changes. And yes, I don't find Deleuze particularly an interesting philosopher anymore. That doesn't diminish my respect for his work. I think he's a certain kind of an engineering of sorts. But the thing is that that actually requires, if we are going to expand generosity to Duluth as engineer, you should also expand the notion of poesis, that it doesn't

bottom up at crappy poetry. The ultimate forms of poesis is engineering. Yeah, yeah, that's interesting. Yeah, because as I was making that statement that Duluth is kind of separate from these engineering traditions, I was starting to think about the way he wrote A Thousand Plateaus and stuff like that, and how he does try and, in some sense, develop work that's a theory that can be then applied in these different use cases in different ways, maybe there's, yeah, like you're saying, maybe there's the engineering

comparison there. Yeah. Yeah, definitely so, definitely so. And this is the whole point that from the dawn of philosophy, I think that philosophy and engineering had fundamental link in the idea of craftsmanship and poesis and most importantly in the idea of how to assign forms to the furniture of the world right you know because engineers are kind of an ordinary man. They don't actually claim

to be the ultimate scientist. They actually learn from going back and forth between concepts, applied math, and that's the whole idea of applied math, between concepts or models and furniture of the world. And through that, they actually garner far more amount of information, useful information about how the world works than probably, you know, scientists who are just pigeonholed in this kind of extremely particularized way of looking at the world. Right, right. Yeah, exactly. Yeah, there's definitely a lot of kind of implicit, like an

engineer, as you talked about, an engineer implicitly understands that systems have different levels and that different models are applicable at different scales. And an engineer implicitly kind of understands that these models are all really good approximations, but they don't necessarily have, you know, correspondence with any kind of underlying, you know, quote-unquote, you know truth or objectivity they're just they're just fundamental reality fundamental reality so yes yes yes and that that is that that makes in fact to to adopt the engineering paradigm uh for a scientist is to go to the or for a philosopher is to go

with the most respected form precisely because you won't get embarrassed at the end of the line right once your your idea of fundamental reality is shown to be bogus you know yeah yeah yeah if you're an engineer you save yourself a lot of trouble by by doing the engineering uh paradigm yeah that's that's a good way of looking at it you just go okay well we just need a different model for this for this scale or to describe this phenomenon okay no no harm and that this is why i genuinely think that the greatest scientists of our time from einstein to bolts from boltzman

to einstein and henry poncoré like one of the best mathematicians in my like uh in my mind they they always had a certain kind of engineering approach, meaning that they always wanted to, like a pendulum, go back and forth between the certain kind of constraints that this is stuff they are working with, or putting on their theories, and how their theories can be diversified so as they can actually cover a broadened scope of possible observable phenomena with so many other kinds of experiments, both in the realm of conceptualization, mathematization, and in the

realm of technique put into this strategy. And I really think that this is why engineering should be understood as not as a separate lowly underdog of science, but as a fundamental constitutive element of it. Yeah, yeah, yeah, yeah. I think that statement has probably a lot of implications for the way engineering is taught, the way academic engineering department. Yes, definitely so, definitely so, yes. Yeah, more integrated with the other sciences, because as a, as a, as somebody who was, was a math major and undergraduate and

went to a liberal arts college that didn't have, that didn't have an engineering department, I was, a lot of these engineering ideas were so foreign to becoming here, and like they, even if I had never done anything engineering related, I think you make a really good point that having some background, at least conceptually in engineering, would have been a benefit. Yes, absolutely. Yes. But I mean, you brought up a very good point here that the pedagogy of engineering is unfortunately, I think it's quite parochial at this point. And that's why so many bright engineers move out at some point,

precisely because, look, the whole idea of engineering is a certain kind of coordination between conceptualization, you know, conceptual thinking about the world. Yeah. That also requires a certain amount of productive imagination, imagination allocant a certain kind of knowledge of stuff out there right physics so to speak i'm not saying that physics is just the knowledge of stuff out there i'm just trying to formulate it like this way and then and then and and and then the precise discipline of modeling which basically is the business of applied math and so on so forth

then the pedology of engineering should be far more expansive than what it already is. Yeah, yep, yep. Yeah, I can, you know, speak from firsthand experience when I say that in a lot of engineering classes, this idea that there's any kind of philosophical component to it. The modeling, even this idea that in some of the kind of poor engineering classes I've taken, that the modeling assumptions have to be justified and understood can be kind of thrown out. Absolutely, absolutely, absolutely. Look, even the worst sort of engineering paradigms have a certain kind of decency with regard to the application of models.

Yeah. Models only can be applied pragmatically, just like concepts. They're context sensitive. You can't have a free floating model to be applied to everything. And actually to create a model is also a pragmatic business in the sense of linguistic pragmatism, right? That the contexts mean something. The contexts have a significance. that the meaning of words is linked or associated quite closely with their context-sensitive or contextually tight application of meaning.

So that's how basically we use models. Models are just pragmas for us. Right. Nothing else. Yeah, yeah. And as a mathematician engineer, when I'm trying to formulate a problem, there's always this kind of tension between striving for the utmost level of generality, the kind of mathematician in me and the engineer in me who understands and knows the context that the results I'm going to be working on are going to be applied in and wants to formulate problems and models that are specific to the context that they're going to be applied in and therefore arguably more intelligible,

more easily understood by people. Yes. And also optimal, optimal down the line for whatever target system you are analyzing. Right? Yeah. I mean, look precisely because if we already know that the question, I mean, this is like a certain kind of metaphysical premise, I think, in engineering that precedes sciences, that the world is extremely complex. But I'm not going to talk about what kind of complexity we are talking about, what kind of measure of complexity. But that is just like a kind of a common sense understanding that the world is utterly complex.

So how are we going to deal with this complexity? Well, by localized decomplexification. Essentially, we are creating patchworks of models, each targeting a certain sector of the world, and it's phenomenal. And then we are trying to integrate such models, submodels, into a greater model and that greater model can be part of another sub model we are integrating with it and that doesn't require for us to commit to a fundamental model of the world or fundamental theory of the world it's just a constructive business that we do it out of both sheer constraints

of how we respond to the furniture of the world and also out of pure joy of engineering yeah yeah um i i really we hit the talk is starting in three minutes so i definitely let me let me let me i have one second my apology before i yeah two minutes out of the city there is something i really wanted to wanted to dive into but definitely not enough time time for that. And you should, by the way, I've made you a co-host, so you should be able to share your screen. Oh, thank you so much. Thank you so much. Two minutes, I will disappear and then I'll come back. All right. Sounds good.

Thank you. All right, it is 3.58. I'll probably wait until 4.00, 4.01 to formally introduce you and

and get to give everybody a little more time. Yeah, I really want to, I really wish I had time to ask you about a paper you actually shared on Twitter a couple months ago about cybernetic communism, because it really... Yes, yeah, I remember what you were talking about, yeah. Yeah, it really embodied that idea of, of even specifically forming these problems in terms of breaking complex systems up into locally not complex subsystems. And when you're talking about that, that's the first thing that came to mind.

I see, I see, I see. No, I remember, I remember Matilda Marconi, yes. Yeah, yeah, yeah. Yes. Yeah, I think really interesting. All right, it is 4 p.m. So good afternoon and hello everyone and welcome to the second talk in our Distinguished Speaker Seminar Series organized by the UT chapter of the Society of Industrial and Applied Mathematics. I am Evan Scopecrafts, the event coordinator for Siam at UT, and it is my great pleasure and honor to introduce to you one of the most impactful philosophers of the 21st century, Riza Nirgharistani. Riza Nirgharistani first grabbed

the attention of the philosophy community with the publication of his pioneering work of theory fiction, Cyclonopedia, in 2008. Since then, his thought has undergone a series of evolutions. His latest work is Intelligence and Spirit, written at the intersection of German idealism, philosophy of mind, and theoretical computer science. He is currently directing the critical philosophy program at the new Center for Research and Practice. I was first exposed to Mr. Nergiarostani as a philosophy-minded undergraduate student just beginning to develop a serious interest in engineering and mathematics. Mr. Nergiarostani's appreciation of the role of engineering paradigms in philosophy, as well as his ability to synthesize the latest in mathematics and computer science with rich philosophical traditions, deeply resonated with me. I think I speak for everyone

here when I say we are incredibly excited to be here today. And without further ado, let's welcome the man himself. Thank you very much. Thank you, everyone. I'm honored to speak today to you. as I was talking to events, SOC is going to be nothing but an introductory, a cursory look at a kind of a broad you know view of engineering as a philosophical and scientific enterprise, right? And I am

quite fanatic about this fact that a good engineer should always be not merely an engineer because what is engineering really we don't know engineer should be a bastardized synthesis between science and philosophy broadly understood in terms of their ambitions. So I have always thought about engineering in that kind of sense. What I'm going to talk about today is about the issue of complexity.

How engineering deals with complexity. that of course requires a very again um brief account of what we mean by complexity so we can actually understand whether we are on the same page or not and there is no uh basically i'm quite fine if my idea of complexity is not uh you know compatible with yours but essentially I have to pin down my idea of complexity in order for us to actually know what we are talking about. So I'm going to talk about this idea of a problem of tackling with complexity,

conceiving complexity, conceiving both in sense of conceptualization and dealing with, approaching. and then from that I will start to talk about a certain kind of trend it's not it's not no longer trend it's actually established within philosophy of engineering and philosophy of science for quite a long time a certain kind of trend within which the problems of certain kinds of problems of complexity, as I mentioned, are being welded

or synthesized with certain kinds of problems of linguistic and computational problems. Hence, the idea of conceptualizing complexity, conceiving complexity. So first, I'm going to start with a very broad range of questions, a kind of abstract or, you know, briefing. And then I will get into the details a little bit further by way of a few diagrams and explanations.

So, first questions or set of questions, where are the limits and conditions of material manipulability or practical intervention at the level of physical phenomena? more importantly is there a connection between the concept of the material and the function of manipulation like when we say material manipulation in the sense that the latter determines the former like how a scientific theory structures theoretical entities pertaining to the real world like we know that the the the theoretical structure or a structuration

in science within the ambit of scientific theories determines the kind of entities we are talking about right so do we have a certain kind of counterpart of this in engineering as well Where are theories or models of manipulability and optimization or modeling roughly determine the kind of engineering entities we are talking about? So that's the question number two. and ultimately to the extent that the practices of engineering and dealing with complex systems always come back to how we model the world with the scope of existing within the scope of

within the scope of existing scientific theories what kind of models are or can be considered as conducive to the complex systems at hand? And how exactly such models should be thought philosophically and scientifically from an engineering point of view? Philosophically and scientifically from an engineering point of view. Meaning that, look, the question is simple, and that's the kind of question that we want to answer ultimately, or at least touch upon, is that given this kind of measure of complexity over a family of complex, over a family of systems, right?

We want, and of course, as I was just telling to events that engineers always says that the world is complex. Of course, what kind of complexity are we talking about? What is the measure here? But nevertheless, that I think is a very good approach, because engineers are not wishy-washy people. wishy-washy people, because we know that when we are saying that the world is complex, it means that we are not saying it for the sake of that, oh, it's complex for the sake of complexity. It is complexed in accordance to the certain kind of contextual practices that we launch against the target systems within this world, right? And hence, we can categorize

certain kind of complications, certain kinds of responses that this target system gives or provides us with into a family of engineering problems. And that would be our complexity measure, really. And so how are we going to deal with this complexity more optimally from an engineering perspective? Right. Obviously, engineering has to do a lot with modeling. In fact, modeling is at the base of engineering.

and that's why the singular import of applied mathematics. So this is the last question. Now, drawing on some of the recent discussions in the field of engineering with regard to models, cross-level and intra-level causal interventions, renormalization groups and techniques, Topological analysis, namely the science of creating a design space for inhomogeneous forms, if you are familiar with topological optimization.

This presentation aims at providing a concept of material organization beyond but reconcilable with the level of phenomenal appearances and the ordinary common sense talks about the world. so how is that engineers actually can for the most part talk about the extremely intricate complexity of the world in common sense languages sort of sort of that's the emphasis on sort of and other people's cats that's actually a really interesting problem

so at the level of phenomenal appearances that the ordinary talks about the world seven tentative claims will be put forward in conjunction with one another and that constitutes the core of this presentation one material descriptions namely describing a macroscopic phenomenon, such as a train, a massive train, going back and forth on a railroad made of steel.

Right? So when we are talking about these kinds of phenomenon, the first kind of phenomenon that we traditionally know in engineering, traditionally, is we are going to describe the sort of behavior that the steel bar shows under stress. Right. Material descriptions, i.e. descriptions of a real phenomenon or a target system are however blind to explanations. Description is not the same as explanations and we engineers are actually interested in explanation rather than just merely describing phenomena.

Because in explanation we find the certain kind of mechanisms responsible for giving rise to the certain kind of phenomena we are observing such that we can manipulate, intervene at those kinds of mechanisms or explanatory factors to create something else, right? A better railroad, for example, here. And blind to explanations, namely causes of interaction between mechanisms which afford dozens of phenomenal descriptions for a sector of reality. Here our sector of reality is a railroad. Two, only a robust concept of causal and functional explanation is capable of rendering

the material intelligible and making intervention at any level of system possible. So essentially, we need to have a kind of tantamount or combined form of going between the causal mechanisms that support certain kinds of behaviors and certain kinds of functional behaviors that even though are constrained by those kinds of causal mechanisms undergirding them, but nevertheless can be extracted and be supported by other sorts of causal mechanism.

An example of this, think about functionalism in artificial general intelligence, that the causal mechanisms that we usually attribute to the physical undergirding of the brain for minded and minding phenomena or behaviors and functions are necessary but not sufficient. Meaning that the lack of sufficiency here, even though it is necessary, the lack of sufficiency means that we can actually effectuate such minding and minded behavior, such as linguistic practices, conceptualization,

so on and so forth, by another kind of causal substrate. But that, of course, from an engineering perspective does not diminish the role of the causal substrate. It actually shows that from an engineering perspective, you have reached a maximum knowledge of the strictures that the causal substrates put upon the functions or behaviors of a system, right? Dr. Nadeem G. Three, but this concept of explanation explanation here is a is a is a simply I mean it a causal explanation and mechanistic in sense of mechanistic philosophy. Dr. Nadeem G. As in contrast to mere description is not an entirely a product of scientific theoretical labor and or conceptualization, it is equally the practical product of intervening or manipulating system at different levels.

As French mathematician Ronit Tham has suggested, sometimes the best way to unveil a black box is by playing with it. The figure of the engineer who thinks of herself as a daemon placed inside a black box called the real world, or a gambler who is stupid enough to make higher wages, higher bets, at a roulette table after initial losses is closer to the idea of an agent who reveals the undergirding mechanisms of a system or a phenomenon by manipulating or intervening with it than any sort of figurehead,

basically avatar that you have in mind, poet, philosopher, so on and so forth, right? Essentially engineers extract information from this black box called nature, world, whatever you name it, by inserting themselves virtually into this black box, seeing how this insertion reacts, how it unfolds. And the instrument of is insertion as we know it is applied mathematics modeling so to speak. But this is not simply a matter of experimental heuristics,

it is a matter of heuristics as well as hypostasization, abductive reasoning, right, and the use of counterfactuals, as in what would have happened to the outcome Y if under conditions C1, C2, CN, the action P had been performed instead of X, or a new counterfactual condition D for the system had been imagined. The so-called manipulation conditionals, as we know it in philosophy of engineering, for example, if I could bend a brittle plate made of tungsten, right, are first and foremost counterfactual scenarios conceived in accordance with available

heuristic methods, no questions asked, but also existing theoretical frameworks and known physical loss. The heuristics of bending a brittle metal plate are of no significance unless the presumptions, conditions, and consequences of these heuristic experiments can be contrasted with existing theories about bending ratio, measurement of a strength, for example, when x permanently deforms or fractures under strain, elasticity, or Young's modulus, i.e. measurements with regard to a material's capacity to withstand changes in length under lengthwise tension.

Toughness threshold, for example, the amount of energy a tungsten plate can absorb before fracturing, etc., etc. Now, four, the concept of causal explanation, meaning mechanisms, can be understood in terms of mechanisms which have causal relevancy for bringing about a phenomenon, a behavior. But mechanisms are scale-sensitives. And mechanisms, look, are causal thickets, are causal thickets. But mechanisms are scale-sensitive, meaning that there are...

By here, at this point, I am not going to talk about how we are talking about scales or levels. The metaphor of levels or scale in science is quite a complex one. We can actually do talk about levels within an organism or a piece of metal, a piece of whatever you can think of in different kinds of way, by way of compositionality, by way of laws, by way of natural obeying. responding to certain kind of natural laws, so on and so forth. Here, I'm simply making this extra simple,

kind of like brushing aside some of the more complicated matters for now because of the time limits. I'm merely here talking about a scale length here, a scale as a scale length. in the physical sense, right? But mechanisms are scale sensitive, meaning that to distinguish them and study their interactions, one has to have a multi-level view or modeling or multi-scalar modeling view of a system. And I'm going to shortly talk about the greatest example

that we know it in engineering, we know it in philosophy, we cherish it. It's what Mark Wilson actually used it, but it's a very, very old engineering topic, actually. It's called the metaphor of the incredible shrinking man. The metaphor of the well-shrinking man. You know, the old classic movie where there is, so the man, there's this family man starts and you know is is the size of any ordinary person right talking to his wife so on so forth and then as the movie goes on he starts to shrink

it starts to shrink so the second time that he shrinks is the something like the size of a baby Then the third time, the size of basically one of those dolls in a dollhouse. Then next time, he's up against a cat his size. Then again, a tanchula. Then he finds himself in a pumice stone. And he sees this as a palace replete with crystals of nanometric scale length.

So essentially, this movie is a modeling movie, really, from an engineering perspective, that you see that the shift here between this man at a regular macroscopic scale length to very microscopic scale length is accompanied by a drastic shift in perspective. perspective in characteristics of this person with regard to the kind of events which we have taken for granted as natural events, right? You see, when at a micro level you interact with

tranchula is a different kind of reaction if you had a full length of a man interacting with tranchula, right? So this is what something that Mark Wilson calls RVE, representative volume element, meaning that certain kinds of elements now respond to the events unfolds unfolds at a certain scale differently as opposed to if they had been events unfolded to previous scales that they are familiar with, right? So this is the thing. So when we are talking about

scale sensitivity, this is kind of a scale sensitivity. And scale sensitivity is an extremely important problem for all sorts of forms of engineering, from computer engineering to metallurgy to all of this stuff. You know, when we are talking about steel, about granite, about cement, what are we talking about? I mean, are we talking about low distress? I mean, we have all these kinds of stuff, Young's modulus. But what the thing is that how are we actually talking about the behavior of, for example, a piece, as I mentioned, a piece of ordinary steel under a persistent stress of a massive train going back and forth on a train, on a railway,

right? You are that piece of steel, the shrinking man. How are you going to respond to this stress? Right? You have to have a certain kind of multi-perspective. So that's what I was talking, and I will get back to this in a richer way. So I said that, but mechanisms are scale sensitive, meaning that to distinguish them and I studied their interactions, one has to have a multi-level view or modeling of a system.

In the multi-level view, top-down and bottom-up approaches are replaced by mixed-level view or mesoscale approach. That allows both to a study of interactions between mechanisms at each distinct level and between different levels, intra- and inter-level interactions between components. Remember, one of the most polemics about elasticity in the history of engineering. right? It's Cauchy and Stoke versus Navier. So Cauchy and Stoke are the bottom-up, are proponents of the, sorry, top-down modeling, right, with regard to elasticity. Navier is

a bottom-up one, right? And then comes Augustus Love, Augustus Edward Love, a treatise on concept of elasticity, where basically he adopts a kind of a multi-variational model that gets both the good strategies of the top-down model and certain kinds of strategies from the bottom-up model, bunch them together, becomes a kind of a certain kind of a mesoscale or intermediate scale modeling, multi-scalar, so to speak, modeling. Accordingly, levels can be differentiated by criteria such as length scales, coarse-grained,

fine-grained, molecular, atomic, electroweak, et cetera, length scales. Composition, how upper-level objects and processes are composed of lower-level objects and processes. forces how distinct forces operate at distinct levels laws under which laws the specific levels behave right theories and techniques how different theories and instruments pertain to or detect items at distinct levels so there are all these kinds of stuff but so we obviously as engineers we need to be informed by all such things where we are not going to respond to all of them,

precisely because engineers are cheaters. We are hackers of the world. We are not supposed to actually go for, chase all of these kinds of details. Yes, we need to be informed. That's what makes us philosophical. Philosophers, poets, philosophers, constructors, right? poets, poets as poets as constructors, but that is not how we are going to work with these kinds of stuff. We are coming with strategies to the best of our capacities without losing a great deal of information, compress this information, make a model

that can be pragmatic in the real world, and also can be repaired or substituted with another model if necessary. And that's how we are going to approach it, really. Five, a multi-level view of a system or material condition coincides with complexity-oriented approach to the phenomenon at hand. Complexity as it is understood in today's complexity science is not an index of variation, multiplicity, and nonlinear dynamics or feedback.

For example, we can envision a simple system with nonlinear dynamics, i.e. sensitivity to initial conditions, such as a system consisting of one double pendulum or a system akin to a centrifugal governor that regulates in a feedback loop the admission of a stream into cylinders, James Watts steam engines, so to speak. none of these systems can be considered as complex in today's parlance of complexity science. Now, finally, I'm going to cut some stuff because I see that I've been talking 30 minutes.

number six the multi-level view of complex structures look complex structure when we are talking about complex structures think about that any sort of form of complexity that we are talking about in nature is a version of a structural stability structural stability by that we do not mean that the structural is frozen but even in its dynamicity it has a certain kind of stability, evolutionary stability. Like you can think about that. Why is that we can, I cannot grow an elbow or a hand out of my skull? Well, precisely because there are different layers, entrenched layers of a structuration mechanisms are there already that you cannot simply

create a certain kind of drastic diversification or variation out of this kind of entrenched parameters already in place, right? And that's part of the complexity of the kind of world we are talking about, entrenchment, generative entrenchment. The multi-level view of complex structures require a paradigm of conceptual engineering or what Rodolf Carnap has called explication. The task of explication. So why do we need actually explication? Look, can we just do our good old-fashioned applied mathematic

modeling that, look, you learned this in engineering school that, look guys, here, you know, you're talking about grain of steel, right? At this kind of micro length, scale length. You're doing good as long as you follow these kinds of sequential formulas and your applied modeling. But look, as soon as you find a certain kind of inconsistency in your model, how about that you go a little bit down, take an elevator to like dislocation level in crystals, right? In metallurgy.

Or even how about this? To molecular binding. But that just looks way too convenient for an engineer to be true, right? It's just not like that. Oh, I'm, I'm, it's not an algorithmic knowledge that as, so I'm merely using certain kinds of sequential routines. And as soon as I find a certain kind of minor inconsistency, I should say that, oh, I'm going to go to a different level of modeling, dealing with molecular bindings in piece of steel. No, that's not how it works. essentially an engineer just like a good scientist is reliant not just on mathematical models

but also in common sense linguistic practices the kind of way that we all humans think about the world of course the way that we think about the world might be completely different precisely because we might have actually access to different contexts of a certain kind of concept, right? So this is why Carnap issues these kinds of recipe, that how about this, that in tandem with refinement of our models, let us also refine the way we cognize

about the world in the common sense way. From a common sense way to more and more refined way. And sometimes refinement is not just enough because if you could just simply refine our models, We will be in the same goddamn world that Ptolemaic system was, right? People who believe in Ptolemaic or Aristotelian system. Sometimes it is, in fact, mandatory to construct, to engineer, and not merely re-engineer a new concept. a new edifice to latch upon a different aspect of the world.

And this is what Carnap's putting forward. So we have the whole idea of physical complexity, engineering complexity, and Carnap's being the philosopher, engineer, and scientist, thinks that you have to complement it. with a certain kind of also conceptual engineering, meaning that for every time that you make a move from one sector of reality to a more fine-grained sector of reality, you should also think about the kind of concepts that you carry with you from the old sector of reality to the new one.

Because why? Do you think that it is taken for granted that the load, the semantic load, the meaning of the old concepts that you have carried from the old sector of reality to the new one, from one level of a phenomenon to another level, are actually going to hold? No, they are not going to hold, most probably. Sometimes you can do an ameliorative, passing up, repairing tactic. And many philosophers think of conceptual engineering like that, like Sally Haslanger. I think that this is a really, a fundamentally naive understanding of explication.

In fact, explication was formed to say that, look, we can make new concepts as we move in the depth of a piece of a steel. The riddle of a steel, like Conan the Barbarian. The riddle of the steel. This is the riddle of the steel. As we move through the riddle of the steel from grain to dislocation to the decomposition of pearl of the decomposition to pearlite, you know, where basically under massive amount of back and forth stress, basically the.

My apologies. All those elements, all those so-called austenite elements were basically responsible for keeping the toughness of a steel, a sort of deteriorate under what is usually called the transformations of austenite to perlite. This gives rise to a certain kind

of a new steel, right? This new steel cannot, it's not tough enough. So under massive amount of a stress is going to create cracks, is going to break apart, right? You know, but it also creates a new kind of steel, so-called eutectonite steel. Eutectonite steel is the one that we use to make knives, to make blades. they are not designed to be tough they are they are basically they are supposed to be

extremely manipulable like you know you can you can actually make a blade out of them right so so there are all these kinds of certain kinds of so what i'm trying to say that these kinds of transformation or energy cascade that is put by a running train on a piece of a steel railway under a persistent amount of motion over a very long time, create a certain kind of riddle, the riddle of the steel in the canon Conan's, John Milius' Conan's sort of way that at the very at the beginning

it seems that just nothing is happening, right? You know, precisely because of the way that we model this phenomenon. Right? Why? Because you don't actually see the transformation, the eutectonic transformation of pearlite to austenite right away. You can't, you might say that, oh, well, you know, pearlites and austenite and semantite, these kinds of stuff that are happening are at the level of bottom scale lengths, very, very small scale length in a piece of steel.

And if we had adopted a bottom-up model, we perhaps could be aware of these kinds of stuff happening. But no, no, no, no, no, no. Absolutely, you can't. I mean, it's just like some sort of physicist that, look, if we had known the kind of quantum fluctuations fluctuations, a la Boltzmann, we could actually predict the rise of consciousness, right? Boltzmann problem. But the thing is that even from a very empirical sense, when you look at a microscopic level at a piece of steel under these kinds of huge amounts of stress, persistent

and sheer amounts of stress, you won't be able at the beginning to predict what is going from an evolutionary perspective of metal, what is going to happen to this metal at the macroscopic level, knowing, for example, these patches of dislocations at micro length, scale lengths, here, there, there, there, there, there, when under microscope. You can't put them together and predict that they are actually create something, a crack in the actual macroscopic steel grain, right?

So actually, mostly it's really better to go from top down but with reservations for bottom-up, what? Bottom-up revisions. So you start with this idea that you see these kinds of, oh shit, this is happening. This is like, look at this. This is like this chipping. This metal is chipping. This is like bending. This is like, there is a crack here. A small, a small one, a small one. But then a small one the microscopic level points to massive amounts of information at the microscopic level of the steel at the macro scale length. So then you will notice that oh this is precisely because

pearlites are now after at some point pearlites so pearlites are still trying to invade to austenite but at some point and this is just like so so there are just basically disjointed clusters disjointed clusters like a small perforations like in a cheese right none of them are connected so far but at some point you see that within the at some point under real amount of stress, these kinds of dislocations at microscale length translate to transformations in molecular lattice of a metal, meaning that what we are going to witness here

is that cementite walls will be formed and these cementite walls will imprison these clusters of pearlites trying to invade the austenites and then they start to become explosive they start to create rifts because they don't have anywhere to go they create dislocations those small cluster of dislocations that we could not actually see or talk about from a microscopic level now become the order of the day. They become fully macroscopic. And that's the whole point. What we are trying to say is that, look,

there are certain kinds of... We usually have the habit of of making concepts from the top to the bottom. Or sometimes if we are really maverick scientifically, bottom to the top, but these are not really good sorts of conceptualizations. The things that how we should conceptualize about such phenomena is that we are creating a certain kind of strategies, strategies through which we can talk about these kinds of stuff, these kinds of behaviors,

and with the understanding that all these spikings, these glitches, these kinds of perforations should be also accounted for in our way of conceptualization. Meaning that our conceptualization should accommodate not just revision, but fundamentally new phenomenon. just like how our models of still under duress, under massive amount of stress, should accommodate

hysterics effects, right? You know? And that's the point of Karna. So we have to to have to basically do a conceptual engineering to basically, to not, because the whole idea of modeling as we engineer understand is not just modeling, it's not just free play mathematics, applied mathematics. It also comes hand in hand with how we observe in a common sense term, the furniture of the world. Right? And so we have to use that, that furniture of conceptualization, not rather than just

furniture of the world, furniture of conceptualization. We have to refine it in order and refine it, not just refine it, but also constructing it in order to come up with models of the worlds which cover new sectors of reality, new sectors of behaviors. So I wanted to talk so much, but I'm seeing that we are really at the end. So I'm just going to give you a couple of diagrams. My apologies. One second.

Okay, one second. Share the screen. Please let me know if you can see it. Yes. Looks good. So essentially, what is a model in engineering? So a model, it consists of a structure, a core, right? core is usually a mathematical series of mathematical formula, right? We are talking about, and this mathematical formula obviously is enclosed within or molded by a certain kind of explicit scientific theory. And that explicit scientific theory, like for example, theory of

gravitation in Newtonian sense, a theory of elasticity in this kind of context, that kind of explicit theory also is enclosed within implicit metatheory, meaning that all theories are ultimately metatheories without even knowing. Essentially, for something to be a theory from a scientific perspective means that it is going to be unconscious form of a metatheory. because otherwise it wouldn't be a theory, meaning that it doesn't have fully fleshed out its relations with other kinds of theories in a counterfactual or possible sense, right? So model consists of a structure. Then a structure consists of two things,

model description, which is not actually the model itself. And model construals. Now, model construals are kind of like interpreting factors that make a model a model or a target system. Model construals are themselves are roughly consisting of three factors, factors, three sort of controls. A scope. First assignment, actually. Assignment. Now, what kind of target system this model is talking about? Assignment. Target system. A scope. What aspects or properties of this target system the model is trying to cover?

scope. Fidelity criteria, that's the most important one. That's when the business of modeling for engineer really starts to get very messy real fast. Representational fidelity, you know, what kind of degree of representation or fidelity or faithfulness are we talking about? In fact, what does guarantee our representational fidelity here? Dynamic fidelity. Like if the target system change, how much are models faithful to cover the change, the dynamicity of the system? Right? As opposed to pretending that it's just like a static one, a static phenomenon.

Like, you know, that, I mean, I don't need to say this, but I mean, the history of science, we have seen it in particularly in the age transition from, you know, medieval to Renaissance science, the majority of the static systems that were always a static were precisely because we didn't have in our models of celestial bodies or the world have anything like this. that a model can track the dynamic behaviors of the system, of the target system in question. Hence, we said that, oh, the system is a static, right? But they were dynamic to begin with. Now, final one for our purpose here,

purpose of complexity engineering, resolution or a scale fidelity, a scale in, levels, resolution. At what resolution are we talking about a certain kind of phenomenon? Do we have actually an interesting answer to this? Right? So if we have actually an interesting answer to this, then we are dealing with something called problems of multi-scalar modeling. or after robert batterman the tyranny of scales the tyranny of the scales so you are dealing with all these kinds of scales of steel or granite or all sorts of these kinds of things biological

phenomena like hormonal pathways rather than just you know dna and this kinds of stuff like in in kind of, you know, reductionist biological way, right? Where basically behaviors, even mathematical models change, right? I mean, to talk about DNA is one thing. To talk about making algorithms about cells is one thing. And to talk about hormonal pathways is another thing. discreteness, continuity, various kinds of behaviors that the model needs to take in, right?

So this is the problem, our problem, engineering, truly engineering problem. This is called the three-partite system of multiscalar modeling. essentially all of our engineering problems will be shown to not be merely engineering problems but precisely because there is no such a thing as a fundamental engineering problem the nature of engineering problem is always a manifold and here we see that the true engineering problems are made of a tripartite system of linguistic practices, conceptual engineering

of the one that I mentioned. Computation and engineering computational techniques. Why? you know so remember when we are talking about the scales we are not just talking about different scales but also the relations between components within each scale length or each level like I think people who are familiar with computational engineering or biology, biological engineering know this quite well, that essentially when we are talking about various scales, not only

we have different components at each level which are kind of linked together, but we have also need to kind of make inferential links between components or sets of components situated at each level to another level. And these kinds of linking or inferential linking is not as straightforward. It's not a matter of back and forth between upper and level. Sometimes the upper intervenes at the lower. Sometimes the lower intervenes at the upper level. So there is this kind of really complex sort of connections. And that kind of sort of connections are actually

really bad for engineering. It's always bad news for engineering, precisely because these kinds of connections lead to what we call a computational explosion or massive computational cost. Increasing computational complexity is always bad for engineering. So we have to kind of decrease this computational complexity, decrease the cost, the computational cost, by two ways. Either changing the model, which is a very respectful way, but sometimes not really possible, or cheating, cheating, engineering cheating, meaning creating

computational evasion techniques, certain kinds of evading techniques that can allow us to kind of put lower level details, massive amount of details under a certain kind of new set level, such that we can control the compression techniques, like how we really from, we compress a TIFF to a JPEG, right? We don't just encode pixel one by one. We create certain kinds of overall algorithms about colors, quadrants, so on and so forth

in order to transform this massive amount of size that the TIFF holds to a JPEG, right? It's the same thing about computational evading techniques. And the third one, engineering modeling material manipulation. As I said, working on the problems of modeling. Now, so problems of multiscale modeling to any of the scales go basically bottom out to four main criteria. models created based on the knowledge of intermediate or mesoscale and their descriptive

vocabularies as I talked that when we are talking about different scales of a physical phenomenon we are always talking about different descriptive vocabularies pertinent to such scales the events happening like a shrinking man to the characteristics of this man dealing with the kind of tribulation at those kinds of scales dealing with the tarantula versus the cat. So that's a problem of meso-scale modeling. It kind of a kind of making an integral for integration of these descriptive vocabularies. Number two, that's a really bad one. Overpopulation

of levels and their links. Maynon's jungle problem for making classes for multi-scalar model that I mentioned, computational explosion. At some point, these causal tickets or mechanisms just start to grow uncontrollably, like a tropical forest, to the point that we can no longer cut back, no matter how much we try to cut back at this forest, and these causal tickets and these connections and this sort of information that we are receiving, it just grows more and more. So you have to actually not submit submit to the Mainan's jungle view of the scale problem, right?

But rather, just think about it, that how am I supposed to actually turn it into a good parking lot? That's what's Hentika's problem. Logician Hentika's solution was that, look, that the best way to do this is that forget the mainland jungle, think about of this as a, as how you can actually turn it into a well-organized lots without actually become too analytic, without actually becoming too myopic. You can still have a little bit of the Amazons, of these tropical causal thickets growing uncontrollably,

but you still can actually make a sense of it as well. Third one, a controlled pluralism of methods, developments of an integrated and ranked system of methods. That's something that many and many scientists and engineers both forget. This is a problem that has been first proposed by Adolf Gurnbaum and Yahshua Bar-Hillel. So when we are talking about problem of scales and to the fact that at these different scales, we are dealing with these kinds of different phenomena and different constraints and so on and so forth, we are always tempted to kind of adopt a certain kind of full-fledged pluralism.

Let's go pluralism, kids, because that will save us in engineering. But no, the problem is that that kind of pluralism actually is like basically a fertilizer for the mainland's jungle. You have to have a controlled pluralism, meaning that that pluralism should have a ranking method for the kind of ranking system of method, for the kind of method that you think are necessary to actually manage this jungle rather than letting it loose. Four, managing descriptive vocabularies

specifically to different locales and levels while making integrated system of classification. I thought that that was, that's actually the final goal. So we can talk about these kinds of sub models that we have been talking about at different length scales, different levels as part of an integrated model. But how are we going to integrate them? Right. Because if we are not going to integrate them, then our vision of the world is always going to be drifting away from us. It's never going to be integrated. right this is something that andrei karis philosopher andrei karis says that the the the contrast between a drifter and engineer an engineer wants to create an integral

vision of the world a drifter wants this world to drift away so you will never see it the same as it was before. Now, all of these four can be put or can be formulated in terms of computational obstacles of various sorts. First, computational cost, explosion of the scales and their links, computational evasion, renormalization techniques. You see the idea of bridging scales, different scales and different scales of different phenomena with one another or with intra-scale relations and inter-scale relationships.

That's the question of renormalization techniques. But most obviously is also the question of computational evasion techniques that I mentioned briefly in engineering. Then managing the model size. What is a model? From an information theoretic perspective, a model should have a balance between signal and noise. order and chaos from an information theoretic perspective. So size matters. Too big, too small, the model is not optimal. And we are in the business of optimal, optimality.

Because optimality for us translates to manipulability. Manipulability also translates to proper descriptive vocabularies. proper descriptive vocabulary translates to well-defined conceptual practices, linguistic practices. Now, final one, pragmatics of managing descriptive vocabularies for different levels. That is, was what I mentioned earlier on to events, I don't know whether it's recorded or not, that the whole point of engineering is a pragmatics, sort of pragmatics, not pragmatics in the vulgar sense, pragmatics in the linguistic

sense, meaning of use, the context sensitivity of the concepts we use. So whereas pragmatics is for language, engineering is the pragmatics of material phenomena. And I think I'm way over my ration. Thank you so much. Thank you, everyone. Wow, thank you so much for that excellent talk that hopefully has the engineers thinking a little bit more like philosophers and has the philosophers hopefully thinking a little bit more like engineers. At this point, I'd love to open the floor to

questions. Does anybody want to throw a question down? Feel free to put it in the chat. You should be able to unmute yourself. And we'll also be tracking lead. Razor, can you hear me? Yes, absolutely. Yeah. Okay. Hi. Thanks for the great talk. I, so I'm also a computational science engineer and I have often dealt with this multi-scale problems where you have this top level approach like Navier-Stokes, which is macro scale. And then you have like a mesoscale problem like Boltzmann and then you can also go to molecular level where you have like all these molecular dynamic simulations and and I really like the fact that you sort of asked to pick the

right scale in order to like study a particular problem like if I'm studying bending of a beam I don't have to go to molecular level to study it I can just have simpler equations to you know like get to the final shape which I want to see or like just to find the result but sometimes what happens is these scales interact and they interact strongly and and for Navier Stokes Navier Stokes is defined uh at the smallest scale like at the scale where the smallest eddy dissipates so that's like the scale at which Navier-Stokes has defined. But that's not a particular scale. That is like an abstract scale.

So sometimes these governing equations, these model equations, the problem we want to study and the computational techniques which we impose, like say I study numerical methods. So like if you go to really fine scale, your governing equation actually starts to blow up and you see a different physics from that same modeling equation. Right. Yes, absolutely. Absolutely. Yes. I mean, obviously, Luke, I mean, this is why I said that, you know, I'm actually, this whole talk was that at least try to come off as a kind of hybrid approach that neither top down nor bottom up.

Essentially, we say that, yes, we can actually start with this idea of this kind of locales, contextual locales, right? but obviously these contextual locales can only be approached against the backdrop of strategies for linking scales. But the thing is that Boltzmann also had something like that, but he made a mistake. I mean those of you who are familiar with the late works of Boltzmann, Post-molecular chaos thesis.

So molecular chaos thesis, I've forgotten the German word for it, or molecular collision. So molecular chaos is a very innocent axiom for a renormalization group called mu space, moon with space. So the thing is that this is how it works. So we have a certain kind of configuration or classification for a set of microestates particles. We are going to show no matter what happens

according to the statistical sense of mechanics this ensemble will lead to such and such macroscopic macrostate forms of mechanics essentially he's trying to say that look guys i am going to say that no matter what happens at the level of these kinds of neutral particle modeling, I am still going to corroborate the fact that entropy increases with time. Right? So the linkage between these two is the Mu space. And Mu space is a certain kind of a very

early form of a mesoscale bridging of the scale kind of model, right? But the thing is that this is the bane of or curse of multi-scalar modelers, that sometimes we actually think that, right, we are doing the right thing. We are going to be the emancipatory guys by saying that, oh, Look, we didn't actually take side with top-down model or bottom-up model. We actually did the right thing and went for the mesoscale. But in that choice, Boltzmann actually commits a fundamental flaw

where he assumes, according to the axiom of the molecular chaos, and what is molecular chaos? two particles that have not collided yet are uncorrelated in terms of momentum and energy. But this assumption has no place whatsoever within the realm of the micro-state, micro-scale, so to speak. This is only an assumption that can be held within the realm of the macro space. Phenomenal understanding of time and entropy.

Because otherwise, why are you saying that two particles that haven't collided yet are uncorrelated? you know because that is that is just a certain kind of time asymmetric phenomenon that you were supposed to explain by the micro level by the microstatist statistical equipment not it's not supposed to be the so you are basically you're in a kind of a smuggling you're switching your explainance, your explanandum with your explainance.

That which is to be explained is now becoming that which is explained. This was supposed to be explained, not part of the axiom. So this happens a lot with these kinds of mesoscaled approaches where you smuggle certain kind of assumptions that shouldn't have a role in lower levels or in higher levels if you were using a lower level model. And the reason that I actually mentioned conceptual engineering brought this is precisely because it's a safety factor in an engineering sense. It's a safety trigger. safety trigger, it should potentially create a certain kind of robust conceptualization that

does not allow us to make such unintentional smuggling of assumptions across various scale models, right? Such as Boltzmann's. Thank you for the explanation. That was really insightful. Thank you. Yeah, that was great. And it's on something I thought a lot about learning the second law of entropy. Sanjana and Abraham have had their hand up for a while. So go ahead and unmute yourself, please. Hello. So I come from a mainly philosophical perspective, quite detached from any computation or mathematics. So I get a sense, of course,

of what has been talked about, but the picture I have is far from comprehensive. However, I did stick to a concept that you brought in, which you termed as generative entrenchment, which I see as some sort of morphological ontogenesis in the sense of having a priori blueprint that an organism might grow into, predisposed by its nature. So my question is whether this generative entrenchment and excuse if this question begs the question because of its circularity, what causes generative entrenchment? Does it have a cause or is it simply the nature of the world?

I mean obviously generative entrenchment like I mean generative entrenchment is a term invented by a philosopher of science William Wimsatt in the 60s and it featured prominently in his book it's called Re-engineering Philosophy for Limited Being, Peacewise Approximation of Reality. Extremely great book, particularly for engineers. I really appreciate this

book. But the thing is that of course WimSat is merely tries to talk about this from a very high biologist philosophy of biology kind of way. But no, I actually think that that's just, that's actually, that's essentially what you were talking about. I don't think that many people have talked about generative entrenchment. What is the cause of it? People actually think that generative entrenchment is a cause of something. No, generative entrenchment, where does it come from? Let's say not what causes it, but where does it come from? I mean, why should generative entrenchment be a fundamental morsel of reality, aspect of reality, right?

I think that information theory has a very convincing answer to this problem. One, I'm not going to talk too much about this, but one way to think about this is that how how structures do emerge right? How do structures emerge? That's an information theoretical question I think and a great answer to a series of these questions even though yet unconvincing but nevertheless

great, great effort, is by James Crutchfield, an essay called The Calculi of Emergence. This is about a notion called, in information and computer science, called epsilon machine reconstruction, a fundamental notion in how structures emerge, become stabilized, become dynamic, and what is the measure of structural stability, right? Because the structural stability needs precisely to follow what we were talking about in terms of

computational cost in terms of model size, particularly like, you know, a structure cannot be too big to a small. So these are, these are, these are actually fundamental questions that information theory has been trying to tackle with computer science, of course, at the very least since 60s, I would say. Right. It reminds me a little bit of the implicate order to some extent. But yes, thank you very much. Absolutely. Absolutely. Absolutely. Absolutely. My pleasure. OK, great. We have a couple of questions from the YouTube feed

that I've thrown into the chat. We can just go through them one by one or Risa, if you want to take a look and see if one appeals to you. No, I mean, I cannot, I cannot, that would be just like really bad kind of speaker. Okay. So the first question, also, by the way, in the chat, I've thrown a feedback form. And so if you have any comments. I can't, for some reason, I did something to my Zoom and now I cannot see it in chat option. That happens, you know, to the best of us. The first question is, have you read Gravity's Rainbow? And if so, what impact?

Gravity's Rainbow, yes. Very, very, I mean, probably like 17 years ago. I can't remember it really. I mean, I can remember the core of it, but not. Yeah, yeah. So Mason Kerr wants to know what impact Gravity Rainbow had on your thinking. And if you remember the scene with the ghost of Walter Rathenu describing... No, I can't remember. I absolutely cannot remember that. But I mean, to be honest, I mean, I hope that this is not frustrating, precisely because I read it 17 years ago. I think even longer than 17 years ago. I say that it didn't do anything. for me really um um one of the really the first engineering book that was magnificent for me um

i always forget the name of it for some reason you know you always forget the greatest works that influenced you um yeah it is it is it's a french science sci-fi written i think in 1928 or early it's like something called the of the fourth dimension i mean you can uh french sci-fi let me let me actually that that was that was really something magnificent for me um it's a certain kind of you know uh that the whole idea of dimensionality not not multiverse multiverse is a very vulgar idea yeah but dimensionality dimensionality of reality

is a magnificent engineering idea um uh uh fourth fourth yeah journey to the land of the fourth dimension yes that's his voyage voyage yes that's it that's it absolutely absolutely for anyone interested um i will definitely check that out um uh just uh uh probably should try and wrap up around around 5 30 but next question um uh uh what do you think about the role of games as modeling resources uh uh how is the ability to gain a form of legibility upon target phenomenon games that model biophysical isn't isn't it the whole idea of uh modeling is a form of gaming

right is that we have it in engineering uh that's that we we that that essentially gaming gaming in a very kind of uh broad way and not a specific way i actually this is one of the times i would say that everything that we do in engineering is a form of gaming first of all we do interact with our subject matter namely the target system and the target system we sometimes wait for response from it and sometimes we just do it regardless of what it responds so it's a very complex asynchronic form of response and answer

or interaction game with target system, that what engineers do, but also gaming in the sense that we make blueprints. We make all sorts of kinds of things that are just like the, you know, the, the, the, the fundamentals of what people usually know as gaming industry. It's just that gaming industry is, didn't actually arise by itself. gaming industry is in response to a certain kinds of problems of cognition, of how we see the world and how we respond to it. And engineering is probably one of the most fundamental forms. It's just that

it's not, it's probably too geeky for people but so as people who actually play games. Yeah. Okay, great. One last question from YouTube. And then I think we have one more question from the Zoom channel and then we'll wrap things up. The last YouTube question is from Chris Hokema. He wants to know if you can say anything about the phylogenic computation or fractals as self-consistency across scales? May I be honest?

I don't, I, you know, whenever I hear the word fractal, I actually stop to talk. As an engineer, it's not my business to talk about fractals. Let some goddamn scientists talk about it. but i mean the idea of self-consistency is really important self-consistency this is again what i wanted to talk about and this is why what my joke was self-consistency in what sense in what sort of context according to what kind of target system you see this is a matter of conceptual engineering you know are we talking about self-consistency in a in an algorithmic sense, you know, that within a recursive regime, you have a certain kind of

self-consistency? Are we going to talk about self-consistency with regard to moving from one scale to another, such that we can actually revise the information that we have of some scale by the kind of information flow that we get from another scale? So that's idea of self-consistency, I think, should be a little bit more optimized. And that's the task of conceptual engineering that I have been talking about. Yeah, that's a great point about it, not just being about recursive definitions. Somebody adds, Riza does not go through phases. Riza is phase to the chat.

OK. we have uh alexander albright if you want to unmute yourself i think i think you'll be the final question um yeah uh thanks for the great talk uh reza i had a question about uh um yeah thank you the conceptual construction because now you go from recognition or re reconstruction of a concept to the actual construction of a new concept and i had the question how one would construct such a new concept does it ride on the back of new empirical discoveries or like a machinic film or does it do you construct a new concept and through that have new empirical vistas

but how do you then construct something new magnificent magnificent magnificent question Yes. So first of all, the idea of construction of concept, conceptual engineering, which is something that Carnarv's put forward, not in the first half of his life, but in the second half of the life. We should understand what is the locus of this new thesis. Where does it happen? In what book? So the thing is that it's actually like 10 pages basically

really at the beginning of the foundations of probability logic. One of the most technical works in philosophy. and it's actually quite really interesting. So the thing is that I'd say, given the fact that the foundation of probability is actually the synthesis of all of the previous works that Carnap did, particularly his work in logic, namely the principle of tolerance. What is the principle of tolerance? Everyone can have its own logic as long as this logic works. You can make in your house, in your kitchen, your own system of logic.

As long as it works, it's good for me, right? That's what Carnap says, right? Kids, you wanted to do it in your basement, do it. So this is the thing. So it is, first, we should understand the principle that the idea of conceptual engineering is built upon, as a default, upon the thesis of the principle of toleration, logical toleration, or logical tolerance. Number one. Two, the idea of logical probability, computation.

Logical probability in the foundations of logical probability, Carnac in the last two chapters, he explicitly says the logical foundation of probability, namely his thesis of induction, is actually a computational thesis. It is what we know currently as Solomanov thesis, knowing that Solomanov actually attended Carnap's seminars when he came to the US. Solomanov, in his famous paper on induction, formal induction, actually gives a thumbs up to Carnap.

So computation, logical tolerance. Now, observability or empirical criteria. really hard to say really hard to say i would say that carnapp would say that no precisely because observability empirical observation is already subsumed within these two criteria namely what having a logic precisely because logic is what constitutes our observations about the world Otherwise, we wouldn't have observations. I mean, what is observation? A piece of rain, it would be a myth of the given, right? A lot of sellers, right? So, and to computation. What is a computation? What does it mean to compute?

to have a certain kinds of logical references to certain kinds of pieces of information about the world, put them together within a certain kind of formula and confirm that this statement is true, that there is a fact out there. So I would say that Carnac would adamantly would say that no, I'm not going to talk about observation or empirical evidence for this to be what should be, I'm only going to talk about logical tolerance, namely the primacy of logic, the kitchen logic. Kids can make their own logic in the kitchen

as long as it's kosher and it's going to be functioning correctly, right? And computation, meaning that how all these things are going to work computationally altogether in order to yield a certain kind of truth statement or falsifiable statement about the world. So this is one of the things that I really love about Karna, that he move against traditional empiricism. That's why he continues and becomes mature in his way of logical empiricism that observations are no longer important. It is, what is important is how we are talking

about thus and so atomic observations that we had plugged into our logical systems and how we compute statements about them. That I think is a magnificent thesis. Well, I think that was a great point to end on and hit on a lot of themes from this afternoon. With that, on behalf of Siam at UT and everybody here on Zoom and YouTube, I'd like to thank you for joining us. And if we could give a round of virtual applause, that would be...

The pleasure is all mine. Thank you so much everyone. Thank you very much for the honor. Thank you. Thank you. All right. I'm going to go ahead and it was really nice spending the afternoon.