Hello and welcome to the new Center for Research and Practice. This is our third season. This will be the start of our third season. And with us here is Reza Negarstani. He's going to be teaching our first three-credit course. course. It's going to be titled Complexity and Computation. And with that, I will pass it on to him and let him begin. So, Reza? Reza Zahra- Thank you very much. Okay. Thanks, everyone. So, just a couple of introductory stuff. I mean, the first thing that would be great for me to know, just so I can gauge
how I should proceed and how I should compose materials. It would be fantastic if you guys, one by one, just give me a quick rundown of what your background is. That would be fantastic. So for the next session, I can adjust the kind of stuff that I put together accordingly. I'll probably call on people better so we can start. Go ahead. Oh, no, no, no. That would be fine. Okay. You guys feel free to start. I mean, just give me a very brief information about your background and what you are working
on so I can get the picture. I think Steven was going to speak, so Steven is going to start. How does that mean? My name is Steven Savignano. I'm a PhD student at the University of Minnesota in sociocultural anthropology. My work mostly relates on models for intelligence systems in science and engineering. I'm doing field work with SETI. I'm going to get into some artificial intelligence. Okay. Anything else? Anything else? That's fantastic. OK. Next person.
Adam, do you want to introduce yourself? Hey, sure. My name's Adam Burke. I don't have an institutional affiliation at the moment. educationally, I have a software computer science background. My interests, I have like a software and finance day job. My interests are really around software construction and sort of the missing theory in software, in a way. Thank you very much. Elizabeth or Buffy, I'm not sure which one we should use, so I'll wait for your introduction.
You're muted right now, so just unmute yourself. There's like a, yeah. Yep. I'm Buffy Kane. I studied computational linguistics and philosophy at MIT. I have a day job in software and finance as well, and then I also am a writer and I make art. Excellent. Okay, and Matthew? Thank you, Matthew. Yeah, Matthew. Hey, I am Matthew. I am a transdisciplinary student. I'm also a traditional student studying biology, so I'm looking at critical perspective,
perspective of stress and yeah, that's what I'm studying. Excellent, thank you very much. What about Sean? Yeah, Sean. Hey, I'm Sean Braithwaite, hold on. Hi, I'm a machine learning engineer for SoundCloud SoundCloud out of Berlin, and I study sort of, or work as a sort of research liaison doing industrial research with collaborators in distributed systems and network science, and study quantum mechanics on the side. Okay, excellent. Tao?
Hi, my background is in computer science and mathematics. And originally from Israel, now I'm in Paris. Actually, this is my first seminar in the new center. And I know you from the internet. I guess this is my main point of interest. Thank you. I know Aaron said he'd be back, so I'm not sure if Aaron's here, and it looks like Victor might be frozen, so that would be the students for writing.
So thanks very much. I mean, that's fantastic to know. Okay. So, I mean, generally, you know, the entire seminar is... It's supposed to be very introductory. So I mean, those of you that are apparently background in computer science and are interested in the kind of applied stuff or hardware theory stuff might be disappointed. also those of you who are more of the philosophy theory background I'd have
you know kind of like once in a while when we are going to you know a little bit of more kind of detailed scenarios might find it a little bit hard to follow So I try to, as I plan, I try to keep it very introductory, starting from the most basic stuff. And the progression would be something like this, that the first module, the one on complexity, I'm going to start from the background history of science.
And this is basically the whole, you know, the first two modules, in fact, the computation and the complexity would be more like a kind of a philosophy of science approach rather than just going to the actual practice. The third one, which is I think probably the most technical one, and probably would be something that the people who are usually, you know, have a background in computer science or have a background in kind of like philosophy, especially analytic philosophy,
I think that would be kind of like an alien, unfamiliar territory for them, because I try to kind of put together some of the new materials, haven't been philosophically reflected upon since. I mean, probably just recently they have philosophers started to think about some of these issues. And they are relatively new, if not completely new, in theoretical computer science. So, yes, so the first module, which is the complexity one, I start with, I think the first two sessions would be quite, you know,
introductory quite rudimentary and then the last two sessions it would be more of it I will go into a little bit more details about the complexity scenarios that I have been discussing I will go into the work of William Wimsatt and I I will go a little bit into a structural stability and statistical complexity scenarios. Once in a while, in those last two sessions, I give kind of, again, elementary examples
in, for example, economy or ecology, so on and so forth. The second module, again, the first two sessions quite introductory. The last two sessions would be a little bit more into computational scenarios. So basically, we are trying to build all of these kind of introductory materials toward basically a very philosophical approach to basically cognitive complexity and a kind of a computational linguistic picture of thinking which would be our last module.
So this session, I will start with, you know, I have some notes. I will read my notes, just give those people who are unfamiliar with the background of where basically complexity science comes from. I will give a very short history of emergence of complexity sciences, you know, in a fashion of quite history lesson, like a history of science lesson. And then I will go to, I will, you know, do the screen sharing. We have some slides start to talk about, you know, basically the basic features of complexity,
of the basic concepts in complexity sciences and I suspect that we can't really finish all the materials that I prepared for this session so we'll probably extend it to the next session and this next session whatever is left from today we will you know engage with those materials plus the focus of the next session would be kind of introduction to the measures of complexity so and Tony um have you shared their reading materials that I
shared it move yes so everything's on the classroom for everybody for all of of the modules, but I did email out also last night, so they can start to look at it, the two texts that you wanted to do for the first module, the ones you just were mentioning. So ontology of complexity and the other, what's the other one on complexity? The Davis text. The Whitman text and then the Wimset text, right? And then, sorry. It doesn't matter. It's okay. I will give some recommendation, I mean, for anyone who's interested.
Again, as I said, our approach is mainly inside kind of, it's introductory. and even in that scope of its introductoryness, it's mainly history of science and philosophy of science. Yeah, so just to say, it was the five questions on complexity, I think, by Davis, and then... Yeah, yes. Okay, so... So, I think the best... The story of complexity, and please feel free to interrupt me whenever you have questions. But for now, I will be just reading off of my notes about the kind of emergence of complexity
sciences. So the story of complexity, I think, is best told by beginning with prior physics-based framework that essentially excluded complex systems. From the publication of Newton's Principia almost 1945, close to the end of the Second World War, the defining characteristic of fundamental advance in physics was the understanding of dynamical symmetry and conservation. In a nutshell, symmetries, and this is, again,
a very rudimentary passion of defining symmetries. This is not completely true, but nevertheless, just roughly speaking. Symmetries are invariances under operations. The invariant quantity is said to be conserved. For physics, fundamental symmetries are invariances of, hence the conservation, of dynamical quantities under various continuous space-time translations. For example, conservation of linear, respectively, angular momentum under shift in the spatial position, or of energy under time shift.
Now, around early 20th century, various people showed that it was the invariance of the form of the dynamical laws themselves that was expressed. collections of the same space-time shifts form mathematical groups and the corresponding invariances then form dynamical symmetry groups. This is really formalized in mathematics under group theory. For instance, Newton's equations obey the Galilean symmetry group.
Symmetry forms the deepest principles for understanding and investigating fundamental dynamical laws. Now, in addition to their general dynamical symmetries, many system states have additional symmetries. For example, the lattice symmetries of a crystal. Within this framework, thermodynamics emerged for analytic dynamics of many bodied systems, like embodied systems, three body systems because all residual motion is random hence a spatio temporarily a stochastically symmetric moreover each stable equilibrium state
is invariant with respect to transitory pathways leading to it the outcome is independent of those initial conditions so its history can be ignored in a studying its dynamics. And this is one of the most important things. Basically, you can basically look into the evolution of system moving forward from this present state of measurement without actually having any model of tracking its history. Basically, you just disregard the history of the system.
Now, the dynamic itself can then be developed in a simplified form, namely in terms of local, small, and reversible, hence linearizable, departures from stable equilibria. Again, this is another characteristic of linear systems that are reversible. Departure from a stable equilibrium. Yielding classical thermodynamics, the only place for complexity here is simply the large number of particles involved. The least profound dimension of the notion, which is basically, we can talk about it,
is in terms of intuitive example of, for example, we have an engine or a watch. It has many components and basically that kind of complexity that linear systems are trying to define can be, you know, basically can be modeled on the interaction, linear interaction of these many components.
The study of simple physical systems of a few components and of many component systems at or near stable equilibrium supported the idea that the paradigm of scientific understanding was linear causal analysis and reduction to linear causal mechanisms with the real as what was invariant under symmetry group or basically a bit in a criteria for formal stability or invariant to a small perturbations dynamical stability paradigm cases included you know two-body solar system dynamics engineering lever lever circuit equations equilibrium thermodynamics of The philosophy of science, however, evolved compatibly, focusing on determinism, universal,
atemporal, hence, you know, condition independent, causal laws, analysis into fundamental constituents, then yielding bottom-up mechanical synthesis. And to this was added a simple deductive model of explanation and prediction. Deduction from theory plus initial conditions gives explanation after the event and prediction before it. So we have a kind of a classical framework.