 There we go. Hello and welcome everyone. It is Tuesday, April 6, 2021 and it is Active Inference Live Stream series that we're going to be kicking off with John Boyk here. This is going to be really interesting because we're going to have three discussions with John and I and interactive jam boards for people who are working with us live or asynchronously. And then we're going to be having two group discussions later. So it's going to be a really interesting look through a really rich world view and hopefully a paradigm that we can be starting to think about enacting or learning more about what it might look like in reality. So let's just get right into this interesting set of topics. I'm Daniel and I'm a postdoc in California and I'll pass it to John just to introduce himself and this series. Thanks for coming on, John. Thank you, Daniel. Appreciate it. I'm happy to be here with you actually. So we're going to talk in this series about a series of papers that I just published in the Journal of Sustainability. That series is titled Science-Driven Societal Transformation and it covers a lot of material. All three papers are kind of lengthy so that's one of the reasons that we wanted to kind of have some time together, talk out loud and cover some of the some of the main points. I am a data science by training just as a little background, actually PhD and cancer biology, essentially, and also I'm a courtesy faculty at Oregon State University. So the series of papers is published under my affiliation with OSU and they just came out to this last at the end of last year and this this spring. So all right, so I think the Jamboard has links. I won't bother to give the links to them. There's also a little short survey, a short summary paper in Science-X that the link is there so you can also check that out. And I would like to say that for additional information you can go to the project webpage which is principlesocietiesproject.org and there you'd find links for all the papers and a bunch of other material. Also a live simulation model you could play with too. So John let me just ask about the three paper format because like three first author papers in sequence it's kind of a there has to be a story there. So just the story is I'm verbose. Now there's more to it than that. You know I had some ideas in mind. I mean obviously that's what the series is about but I had some ideas in mind of what transformation might look like and how we might get there. But I think the ideas are fairly unusual and I didn't want to just put them in a short paper because without background I think that the ideas could have been easily misinterpreted or misunderstood. So I felt it necessary to explain the background in more detail about what the proposal actually is and what the concept is. The whole series as a whole is kind of a proposal for an R&D project, a science-driven R&D project aimed at societal transformation. But it's a little bit more than that actually. It's a way to think about transformation, a way to context for how we might go about societal transformation and what that looks like and why we would even want to bother with the concept in the first place. So there just was too much to fit in one paper and I had originally written it and put it all in one paper and then I could see that wasn't going to work out. It was going to be way too long. So I split it up and I'm very happy that the journal Sustainability was willing to publish this. They were quite interested. So that all worked out really quite well. And maybe I should say too this work represents about 10 years of my efforts. This is kind of the, in a sense, the culmination of 10 years of my thinking on this topic. Cool. Really interesting and I'm sure we'll hear more about some of those threads. So just the first slide on this Jamboard, which people are welcome to join if they're watching live or if they're looking at the replay. The first slide is capturing how we're going to go through these three parts of the Triptych, the paper series in order at this same time slot, 3 p.m. Pacific for the next three weeks. And then we're going to be having those group discussions. So we're going to kind of pour over these papers. But who knows, maybe take a few tangents as well. And this principle societies project.org website is where more information can be found. So let's just maybe start with a little background before we jump into the paper on just what collaborative dialogue has to do with all of this. Why are we structuring it this way and experimenting with it this way? And just how does dialogue fit in in specific or in general to what you're talking about and thinking about? Well, you know, as I mentioned, the series is kind of a R&D proposal for an R&D project. And while it's important that the concept as a whole move forward, at least it's important to me. And funding, you know, we secure some funding for this project so it can move forward. Perhaps the biggest thing that has to happen is just the concept, a few concepts, a few primary concepts that I talk about. Just getting those concepts articulated out to the science world and to the public. That may be, you know, if I die and that's all that ever happens from this, just that those concepts become talked about more frequently in the public discussion, then I probably have done my job. Obviously, I hope it goes a little further than that. But that's the place to start. And maybe that's the most important thing. Where would just citizen science or participatory science dialogue with really inclusive participation play a role in the R&D programs of the future in what you're kind of thinking about? Yeah, so I frame this R&D program that it's conceptual at the time. It's not funded yet. You know, I'm hoping that we can secure funds. But I frame it as a partnership between this global science community and local communities. So it's very, so dialogue with the public and within the science community and among interested stakeholders is extremely important in this. To me, science has a role in such a R&D program because science is really where we would turn to answer some really difficult questions. Like, if you wanted to build a simulation model of how the environmental or economic outcomes might be given, you know, A, B, C and D, well, then, you know, that's a technical, those are technical questions. If you're asking how can we measure, what kind of metrics are reasonable for environmental and social well-being, those are largely scientific questions. You know, the math can be complicated, for example. But the questions of, you know, how do what do we want? What do people want? How do they want their, you know, how do they want to live their lives in society? Those are questions for, you know, for the public and for communities especially. The intention of the R&D program is not to develop one-size-fits-all solution, you know, to trial it in a local community and then spread it everywhere. That's not at all the idea. The idea is that this is an ongoing learning process, a true partnership between local communities and the science and the science community. And there would be just a million sorts of, you know, experiments that one might run to improve the kinds of societal systems that we have or, you know, that we're proposing. Develop a new system, try it out, see how it works, gather data, you know, do another experiment, all within the partnership with local communities at the local community level. I think maybe, you know, since I know this stuff so well, I think that maybe I'm already jumping ahead of some concepts that might, this all might become clear as we talk a little bit more. Nice. Well, this slide was just to show that there's a bunch of questions that could almost apply to every stage of this model and to every stage of this conversation. Like what are we doing here? What is our goal here? What are the next steps? And how is active inference coming into play? Not that active inference good, not active inference bad, you know, we're gonna kind of go a little bit deeper than that. But what does this have to do with actually having impact will be something that will be cool because what we're talking about is really new synthesis of the theoretical and the applied. So that means that we're always like looking to staple across the divide in new ways with the questions that we're asking and the teams that we're working on, the projects and everything. So maybe we can go to the fourth slide and to kind of frame this research line. What is the issue? Yeah, yeah, so obviously the word transformation is in the title of the series. So the, you know, the general topic is societal transformation. And although that term alone is a little bit, you know, people have different ideas of what societal transformation means. So I want to make a few things clear. I especially in the second paper, I make the distinction between reform and transformation. And by reform, I mean anything that would improve our governance system improve our economic system, improve our legal system, you know, one example might be levying taxes on the wealthy or something and then using those fees to provide medical services or something. Or altering how long a representative can be in, you know, in Congress and the legislature or something like that or ways to vote or things like that. Those really those are all what I would consider reforms. And I'm interested in a different question. I'm interested in the question out of all conceivable ways to organize the societal systems and by societal systems, I really focus on a few of them, governance systems, economic systems, legal systems, educational systems, and I think maybe one or two others out of that. I view those systems as the cognitive architecture of a society. That is, that's how society thinks through those systems. It learns, it adapts, it decides and evolves kind of through that kind of cognitive architecture. And my question is out of all possible conceivable ways, out of all for example, out of all conceivable economic systems, what ones might be best for demonstratively showing that they're, you know, they excel at improving or maintaining social environmental well-being. So even there we have the concept of a fitness coming into this. That is, what out of all conceivable systems, which are the most fit for purpose? And now soon we can talk about what purpose might be, but you know, that is a new question that's, that is a question that's hardly been asked in the in the history. And I think maybe it's only now that science has the tools and the theoretical understandings that maybe it's, maybe this is right, maybe it is time that we can talk about the purpose of a society and how fit a given system design might be. Very interesting because in active inference land we're really familiar with talking about policy selection, but we're coming at it from the angle of model policy or a simulation's policy, but that kind of question about policy setting, and then sense making, again maybe different groups use kind of different terms. But all that sense making and problem solving has been siloed and the fact that there's not a connection and common frameworks to bridge as you're placing it in one integrated brain like societal systems as a cognitive architecture, that's not going to work if the different sections are not properly having there within and between connections working. And we're seeing all these different sectors, all these little regions of the brain, health, governance, political, legal justice, education, scientific, analytical, economic, financial, monetary. You could go to a news site and on any given day see all of these things changing. So very prescient to think about how the total system is going to be changing and finding new stable states, otherwise it's going to be just on a spiral probably not in the right direction. Right, as it seems to be unfortunately, yeah. Yes, so the idea is and we design societal systems like economic and other systems such that the set of them, the set that is the cognitive architecture for society can be designed so that they are serving the same purpose that they are integrated not separate systems. I think you were sort of referring to that a second ago but the idea here is an integrated set of systems that serve a common purpose and for which a fitness, you know, a fitness evaluation, a fitness score can be made. Is that something that we're going to return to is like how do we define common purpose, how do we make a fitness evaluation? We're going to get into that. We're just sort of doing some warm-ups here. Unfortunately, there's, I mean, maybe somebody might help to read this whole series twice because the first time you are like getting just the kind of the big picture context thing like oh what's this? What's this? What's this? Then maybe on the second time around it's a little more clear like ah this fits in. That's just talked about in the first paper but it really fits in in the third paper. Nice. So there's just so many concepts here. It's a little chaotic at first as we cover some of these things. Right, but the framing of the problem is not dissimilar than any number of other perspectives. It really synthesizes a lot and there's a ton of scholarship in the paper so really I would recommend reading it before imagining that it does or doesn't say something. Yeah, please. I get that a lot. Oh, you must have meant this but I'm not done with the paper just FYI. Here on slide five there's the Science X link that people could click to and what is this about or what was this showing? Yeah, this is just a short little summary article that Science X published of mine. And maybe I don't want to be boring him but I might want to talk about these seven points. These seven points on the right there are actually a summary of. Sounds awesome because people who might like to read it or hear it both modalities and you're really going to unpack it in a new way. So no worries about even stating it the way you stated it. Okay, good. All right, so these are the seven main thrusts of the series. So this is this is the spoiler alert. Stop. If you don't like spoilers. Yeah, yeah. So number one societal transformation is necessary if we're to avoid catastrophe and maintain and improve social and ecological well-being. That's the starting. That's where this whole thing starts. That's something that's transformation is necessary. Number two, one kind of societal transformation is a science-driven transformation. You know, you can imagine all kinds of there could be war revolution could be a type of transformation and I'm not talking about revolution here. I'm talking about a science-driven evidence-based development of and migration to fundamentally new systems. So we're talking about de novo design from scratch. So we're not improving capitalism, for example, or representative democracy. We're looking at conceivable de novo designs that might be fit or among the most fit of all possible designs. Number three is, you know, if I were a genius, which I'm not, but if I were a genius and I came up with the greatest plan that everyone could, you know, we could rearrange society according to this, you know, this design, if there were no way to, a practical way to implement that, then I would have been wasting my time. So a big part of this series is actually, especially paper number two, is really focused on what is a, how could this actually be done in the real world? How can you do this? So I claim, at least, that there is a viable and affordable way to go about transformation that within a reasonable span of time, and I consider a 50-year program here to be a reasonable span of time, transformation could spread to near global levels. So, you know, we're talking about a concerted effort over a long period of time to reach a global scale change, but that does not mean that no change happens until the 49th year. It means that change happens exponentially fast. So maybe in the first few years there's, you know, there's not a lot going on, but it goes exponentially fast from there. And those communities, local communities that participate in this R&D program would be obviously be the first to reap the benefits. Number four, the, and this is maybe one of the key worldview aspects of this. Paper number one is all about worldview. The proposed program views society as a cognitive organism and its societal systems as a cognitive architecture. So, you know, that, if indeed society is a cognitive organism and our systems are part of the cognitive architecture, that already lends itself to ideas of how you might measure fitness. That you're starting, we're starting to get an idea of what is a system supposed to do. So we'll be getting more into that today. Number five, the intrinsic purpose of a society. Obviously, if we're going to build a new system we have to know what is a new system supposed to do, like what is an economic system supposed to do, what is a government system supposed to do, what is the purpose of them. So, purpose is also a big part of the worldview in the first paper and one of the points of the fifth point is that the intrinsic purpose of a society, of societal cognition, and thus also of societal systems is to achieve and sustainably maintain social and ecological viability and vitality, broadly, broadly defined. Now, if you're listening carefully and you're of the active inference persuasion, you'll already see active inference in here. When we talk about sustainably maintain, that means anticipation of the future. And also, say here the cognition is largely focused on reducing the uncertainty that our intrinsic purpose will be successfully fulfilled now and in the expected future. So, again, we have a concept from active inference that is uncertainty. The cognitive view opens up many new opportunities for research and I feel like this view is really critical if we are to truly have some kind of optimally beneficial societal systems. And number seven, the last one, this proposed R&D program, like I already mentioned, it represents, it's conceptual now, but it represents a partnership between local communities and the global science community. And the, you know, neither of those are monolithic. The global science community is, you know, however you might want to envision it, 100 labs or 1,000 labs around the world or individual researchers or groups of research or teams, interdisciplinary teams at one institution, teams across institutions. That is what I'm, that is really who I'm speaking, in the series, I'm speaking to the science world and I'm suggesting or offering or, you know, hoping that the science community might find this perspective interesting and see the benefits that would be the scientific benefits that would come of this, the research gains that would come of this, the possibilities that would come of this and and get engaged, right? So, it's kind of a, like, I'm asking the science community to get engaged in this problem and in a particular kind of way and what some people have called second, you know, who I'm going to phrase escapes me in the moment, I forget what what is the title, their second, second, I'm going to just call it second order science, but I think there's a slightly different phrase. Very interesting, I remember reading this second order, yeah, second order science. So, what is second order science? And I'm sure we're going to, again, go into it in this sort of, like, fractal convo, but right, right, right. What is second order science and how does everyone play a role in it? Right, so, so traditional science would be first order science is the, the scientist is removed from the experiment that is an observer to the experiment. Maybe the scientist sets up the experiment but then steps back and watches the, you know, the results unfold and just observes the results and then, you know, does statistics or whatever on the results and records the, the, the knowledge gain. The aim of that is to build knowledge and, you know, that's wonderful and fantastic and terribly useful and important and that is mostly what science has done over the, you know, over the last few hundred years or so. Second order science is a little bit different. It is the science, I would say, that is really appropriate for, for today, for our problems today. In second order science, the, the aim is to change, you know, change the system, change, it's more about unfoldment of an experiment rather than getting to the end a certain goal of an experiment. The scientist is part of the experiment. It's reflective in nature. It's intrinsically reflective and it, and it really represents a partnership between the science world and, and the stakeholders. So it's a little bit more like engineering in a sense. It has more of a flavor of engineering in that there's a group of stakeholders who want some, you know, want something to happen and the science world is engaged in building that, but it's part, it's like a, you know, series of learning together and reflecting on what is important and what is of value. So it's very much value-driven, which is, which is unlike first order science. It's not particularly value-driven. So the, the, the aim is to change and it's value-driven and the science scientists are part of the experiment. Really interesting. That reminds me of working for the first time really closely with someone outside of academia and then their insistence to include stakeholders in the conversation very early. And that was something that I didn't see as a practice around me in academia. So I didn't see it as an affordance. I didn't see it as an opportunity in my projects. And then when I kind of was like, well, I could contact anyone early on potentially and maybe even include them in the project, that's, then you realize how closely clustered all of the current communication and research is in development on not that axis. So it really just casts a stark light on how a lot of these information projects have been organized. And so it's really interesting and as you put it out, like there still is a role for the first order of science because it's sometimes you need to make the measurements like first order cybernetics, the thermometer. We need to keep the temperature in range. Second order, is it the right thermometer? You know, third order, should we even be tracking a thermometer? There's a space for all of us. What do we really want? What do we really value? Yeah. And, and maybe I should just inject here too that, you know, if the science world is going to be involved in a value question, it really just has to be like transparent out front, like this is what we're doing as opposed to more, you know, obtuse. We're actually making value statements, but we're sort of hiding them in first order science language. You know, so it just has to be transparent. This is what we're doing. This is what it's about. We're learning together. We're evolving solutions to real problems that the humanity has. And everything is like transparent and clear and above board. And if it's done that way, then it's, I think, could be extremely useful. And it's just what the world needs, actually, because there are serious, serious, as we'll talk about, you know, like overwhelmingly serious problems that we face. I don't know how or if we can solve them. And if we're not engaging, you know, the scientific world, some of the brightest minds on the planet, if we're not engaging them, then, you know, we're, we're not going to get the kind of solutions that might be, you know, I mean, we're better off engaging the science world in these questions, along with communities, right? Because that Yep. I, I, oh, sorry, continue. No, no, that's fine. That's fine. This, yeah, yeah. Okay. Just one short on that is it's actually a really respectful way for science to approach this question of like, we'll be ready as scientists or as systems thinkers or as modelers will come to the table of policy and value discussion when we have a transparent model that's actually going to be adding value rather than potentially being a vector for some non-transparent force to have undue influence on discussions that really matter. So it's, it's, um, I hope this conversation helps people rethink how science could be involved in these policy discussions because those are going to be the exact terms that we're going to be talking through. Right. Right. And when we talk about the second paper, we'll get more, we'll talk a little bit more about the second order science and its role and their citations there for anyone that's interested. Nice. So here we go to number six. Why transform? Yeah. So, I mean, I'm guessing that for your audience, you know, the, the, the, the, the audience probably has a general sense of things are not good, you know, things are not moving in the right direction. And I think maybe lots of the public has the sense that things are not good, not moving in the right direction. And climate change is certainly part of that on the ecological, on the ecological theme, climate change and biodiversity loss are perhaps two of the, you know, biggest challenges that humanity faces, you know, now and over the next, say 1,000 years or something. But, but our problems go deeper than that. It's, it's not just that there's, that the temperature is warming. It's not just the, the birds are dying and the insects are dying. It's that it also has to do with social issues, poverty, for example, financial instability and all the, all the social problems that climate change and biodiversity loss and other things are going to, to bring. Already with COVID, I think there's, there's the, the split between the rich and the poor and the world is getting even larger than it was and was already disgustingly large. There's a question of power. You know, why, why, why does it, in a sense, if we think of money as a voting tool, why is it that a billionaire has a billion times more power to decide what society should be like than I do? You know, like there's questions like that. There's just fairness, questions of fairness. There's questions of decision making. Who, who, how do we make, who actually makes decisions? Like how, how many people are in, like how much of the, of society is involved when society makes a decision? You know, there's all these questions and all of them can be, I think, addressed together in a, in a, in a R&D program like this and we have to, we have to address them. I don't think there's a, there's an option of not transforming. So, so voices for transforming or for some kind of bold transformation or getting louder and louder, both in academia and, you know, at the, at the global scope, with global policymakers at the UN and other folks like that, just within the general public, with even within some, you know, municipalities, city-states, local governments and, and even the national government governments, there is a rising, rising chorus of voices saying we need to transform and we need to go big and we need to change things, you know, kind of radically somehow so that we can get what we want in the, you know, like, so 50 years from now, my children can be enjoying a better world, not a, not a dying world. Now that doesn't mean that, I'm going to jump just a little bit ahead here, that doesn't mean that everyone thinks that transformation is necessary, but lots of people don't. Lots of people would just as soon keep everything just the way it is, you know, or they just haven't thought about it maybe or, or whatever. So, so I just want to make clear that I'm not, you know, it's fine, people, you know, people can fall wherever they fall on the spectrum, but the paper, the NDRD program is really aimed at that percent of the, you know, the population and the science population that is interested in bold change and it doesn't really need the, the R&D program really doesn't need the rest of the world to even engage or pay attention to this. This whole is, the R&D program is designed to be successful even if a small slice of the population participates. So, so we, you know, that was a long story to say that we face severe social and ecological risks and they're getting worse. I do want to mention one more that kind of secondary risks that we will be facing more of already we have migration, forced migration problems, people fleeing lack of economic opportunity and fleeing violence and things like that. But you can imagine that, that if that's happening today and climate change hasn't really even hit yet and biodiversity loss really hasn't even hit badly yet or at least this hasn't hit widespread badly. Where are we going to be in, in, in 10 years? There's going to be millions. I read a paper and I'm sorry I don't remember the citation, but I read a citation, a paper that said that, you know, possibly a third of the global population could be migrants in the future, you know, in the coming decades. I mean, that's billions of people have to go somewhere and we are not prepared at all like in America or really anywhere. We're not prepared to absorb those people, bring them into some kind of productive, engaged society where we're all working together and cooperating to, you know, to address the needs of society. We're not anywhere near that. And maybe I'll mention one more too because it's one that people don't usually think of. But even if there's no, even if there's no catastrophe, even if things plug along as they're going and there's no mass die-off of humans or anything like that, the population is set to decline. I don't know when the peak is supposed to come, but the peak is supposed to come at, you know, within the next 10, 20 years or so. And after that, the world population will start to decline. How is, how is this growth capitalism model, growth-based capitalism model, how is that going to function when the world is shrinking? You know, so there's just, there's, there's, there's short-term issues. There's long-term issues. There's just, I would say, overwhelming evidence that what we're doing is not sustainable on any level. And if we don't do something, it's going to lead to even greater catastrophe. I have a few questions. One just from the chat, they wrote, the science community here seems to be referring to the natural, rather than the social sciences. So it seems more like this is about STEM and technology. So maybe where do the social sciences fit into this? Oh, I, I, in the, in the second paper, I give a laundry list of fields, scientific fields that I, that I think could really contribute to this project. And it's essentially A to Z, you know, from anthropology to zoology, you know, like every, really every branch, not, and not only every branch of science, but art, you know, media, there's, there would be room for every, you know, kind of branch of human endeavor to get involved with this thing, agriculture and psychology and public health and, you know, sociology, and all that would just be, like at the moment, you know, as I said, this is not a funded program, but I, and if it were funded, I anticipate that the, you know, either the organization would act as a clearinghouse for, for funding, for, for, you know, proposals, or, or there'd be some kind of mechanism set up to, to distribute funds or, or I'm not sure exactly how that would work. But it would be my hope that funds are distributed to literally every branch of science and then beyond science to, like I said, to art and to literature and to, you know, all of that stuff. Well, then let me ask one more question on this slide. So the communities that are likely to institute, for example, the flood prevention, some sustainable strategy, they're going to be in a flood area, or they're where the sea levels are changing, for example. So when we think about cognitive architectures, what kinds of individuals or teams or communities or systems cognitively are like the early Canary and the coal mine that you think are ready to transform, or somebody who like might hear something about a system they're involved in and think, actually, yeah, that sounds like my organization or self might be at this sort of transition point. Right. There, you know, if one looks around on the webinar elsewhere, there are numerous kind of experiments going on in a variety of things in new forms of representative democracy, new forms of decision making, new forms of economies in the sense of, you know, local digital currencies and things like that. I think all of those, you know, all of those are excellent, you know, a resource to draw from. The task is then is to take these ideas, these ideas that are springing up all over and put, integrate them in a way that is functional, you know, can serve a community. Initially, maybe the community is small, just a few thousand, but the idea is that it would that it would grow over time exponentially grow to who knows, you know, hundreds of thousands, millions, I don't know. So how can you take all those ideas and actually make them work? Sometimes I liken it to, you know, you might tinker in the garage with a with an airplane, you know, you might build a two-seater in the garage and that's totally cool, you know, you can, you know, that maybe what the Wright brothers did or something, that's fantastic, but I'm really interested in building a jumbo jet that takes, you know, 500 passengers at a time in an hour and a half to from New York to London or whatever and doesn't fall in the ocean, you know, like, so how do you do that? How do you, how do you build a integrated system that is safe, that is resilient, that is, that has metrics that you can monitor progress that has good anticipation so you know where the, you know, that where you're going tomorrow, you know, where this, where this going, you know, what's going to happen tomorrow and, and sort of what's going to happen to me, you know, that's, that's part of the question. So, so the, so I think the challenge is to take all these ideas that are, that are popping up all over, which are some really great ideas and then to integrate them into, into a coherent whole that, that spans every one of the, I think maybe is it six systems that I talk about so that, so that they're not designed in silos, we're not just building a new economic system, we're not just building a new educational system, we're building a, we're building a, a, a cognitive architecture that includes all of those. Okay, awesome response, awesome response. All right, well, we're, you know, we're moving slowly, it's totally fine, but so this slide, this is again, is the, again, is the topic of why transform and we only got the first paragraph so far. Oh, oh, nice. Okay, we'll stay on six. Oh, you know, we, we get, we get a ways to go on six. We can, we can continue on, you, you can just summarize in one sentence or however you want to do it, but up to you. Okay, all right. I'll try to go a little quick through the rest. Most of that discussion is on humanity faces of your social and ecological risks. The next one is our systems are dysfunctional as is and I would say that is evidenced by the fact that we are under extreme threat. Like, you know, what kind of sensible, healthy person puts themselves on at a ledge hanging by their fingertips. I mean, like that's not something's wrong, you know, like you don't do that. A competent, a cognitively competent society does not push itself to what could literally be the early stage extinction. You know, that's, there's something deeply wrong, deeply dysfunctional that we find ourselves in the situation that we're now in. And we can do far better. We must do far better, obviously, if we're going to thrive and survive. And not only that, but as we talk about purpose, we actually long, we, it would make us deeply deeply happy to do better, to be, to solve problems, to be more engaged with a healthy society, to really feel like there's a deep cooperation happening in society that would, our hearts would burst, you know, of joy sort of, we would be, we would be happier if we were a part of a society like that. It's very stressful. We were designed by, you know, evolution through evolution, we have become, we were, really, every organism, as we'll talk about in a minute, is a problem solving organism. And if I can't solve problems, there's like a, you know, like fundamentally going against the grain of what it means to be an organism. Okay. Nice. All right. Good. Shall we go on to the next one? You can go to the third part or do you want to go to slide seven? What is the third part? Well, I was kind of the, I think that was about it. Great. Let's go to slide seven. So, John, what is this series? Oh, I'm glad you asked. Oh, I have a script. Don't worry. Yeah. So, as I've mentioned, everybody, the series really is a proposal for an R&D program aimed at the de novo development of new societal systems. And it's also a way to context and a way to think about what transformation might mean. So, it is a long-term project, you know, like a 50-year project. This isn't, we're not, it would be dangerous to change society radically overnight. So, this is a long-term project for long-term benefit. And then early communities that become involved early in that process would, of course, see benefits quite early. And there are, I already mentioned that we cover six primary societal systems. So, that's the cognitive architecture. Once again, they are economic, governance, legal, public health, and what I call analytical forecasting and education. And, you know, any one of those can be broken down further, like economic really, also includes a monetary system and the financial system and things like that. So, six primary societal systems. The idea is out of all conceivable designs, what might work best. The whole project is based on three underlying propositions, the papers are, and the project. First is that a society of any scale, and I don't mean society is in billions or billions of people, I mean society as in a thousand people, you know, like a sub-city, a community that is not even a whole city, just a group of like-minded people who are willing to give this, you know, a field trial ago. A society of any scale can be viewed as a super organism. So, that's kind of fundamental. Everything really, really works from there. We are together. We are not just individuals connected. We are a whole. Society is a whole, and it's a whole with the environment and its wider, you know, sphere. So, as we'll talk about today, you know, even the idea of an individual is, it's okay to talk about individuals as fine, but it's kind of like an arbitrary thing. An individual could be an individual cell, or an individual person, or an individual species, or an individual ecosystem, but it's all deeply embedded and enmeshed, entwined with the whole. So, society can be viewed as a super organism. Society's complete set of systems, as we've already said, the six big systems I've mentioned can be viewed as a cognitive architecture. It's the means by which society learns, decides, adapts, and this society's efforts, this is the third underlying position, the society's efforts to learn, decide, and adapt, can be viewed as being driven by an intrinsic purpose. And that's really key, also, because it's not just that we're learning, deciding, and adapting willy-nilly. I mean, maybe it seems that way in the world, you know, in a sense, we're so dysfunctional, it kind of is willy-nilly, but what really matters is that we learn, decide, and adapt in relation to whatever intrinsic purpose we actually have as a society, as individuals in a society. It's that, it's, as I will use the term maybe several times today, it's solving problems that matter, that really matter. That's what we're after. Cool. Well, on this super organism point, as someone who studied ants and still studies the eusocial insects, this kind of multi-scale thinking is really fun. It's just always such an interesting question. What level does purpose exist at? So is it that purpose exists at every level, like the cell, the nesmate, the colony, the colony, and its symbiont, and its predator prey, and the microbes in the niche, and then where you draw the sort of qualia, like philosophy lines. It's a whole debate. It's a whole thing. Yeah, it is. It is. It's rich. It is rich, but then also there's the systems, kind of systems mapping approach that, albeit coldly, does sidestep those questions. And I think part of the humanism is about how to bring these multi-scale questions and multi-scale perspectives, which are basically neutral tools, because one's preference vector and active inference could be to lose money. I mean, then I guess, you know, good job, but we have to have the preference vector. And then what will be interesting to draw out, I think, in these discussions will be, just like you said, with the transparency of the values. What does it look like to actually specify values within a model-based framework and take the bold step from just like, I trust my models, and I'm a fan of liberty, to I trust my model about liberty, and I'm willing to let that model drive for a little bit with respect to my decisions. So that's really, like, such an interesting handoff. And that's also new and related to technology that's evolving. So like, you're bringing a lot of ideas together. I hope people are, you know, listening to it and finding it exciting. Yeah, maybe we'll just jump ahead a little bit here. As far as a preference vector, I took pains not to put a, not to suggest a preference vector of any kind in this series. This wasn't, it wouldn't be very useful for me to do that for one thing. But for those listeners who are active inference fans, you know, having only a preference vector is, maybe I should say, having a check off list, like, you know, there should be this level of education, there should be this level of health, people who live this long. And so we have our fitness, and we're gonna, we've decided in advance, even before the system is running, we've now have a list of things, we're going to check off, we're going to score each one, we're going to come up with some kind of integrated fitness score from that. And that's how we're going to move forward, always going to refer to this fitness, you know, this fitness model, and this fitness vector, and these, and these kind of hard coded values for what's good and what's bad. So, so in the world of artificial intelligence, and in the world of active inference, you know, that really doesn't go very far. That doesn't work. That doesn't work very well. Because what happens is we didn't, you didn't think ahead, like you, something happens tomorrow, and whoever came up with that list of, you know, those values or that model didn't really include the fact that maybe spaceships from Mars, we're going to land and cause a new disruption, and we have to deal with that problem now too, before we deal with anything else. So that wasn't in the, you know, that wasn't in the plan. And now what do we do, you know, so there's, right. So, so this is, you know, this is really where active inference plays into, that's one way that active inference plays into this, is how do you evaluate and act in a world that is full of uncertainties? Right. The unknown unknown, maybe the unknown unknown is the temperature dynamics, but you know, it's going to be temperature. And so how can you plan for what you know it will be in a distributional sense and make stabilization on that? Right, right. So yeah, so you, so you realize, you know, already you realize maybe that this is not a proposal to build a say, like a model of, of, you know, like how society makes decisions, you know, that's, that's not, that's not it. It is, what is the process by which society cognates, and you know, what kind of, what kind of infrastructure and tools and, and, and, and, you know, mechanics can we use that would facilitate that. But it's not to build a thing, it's to build, it's to realize that we are in, we are engaged moment to moment in a cognitive process, society as individuals are. And how can we do that together as a society so that we're, you know, we, we balance exploration with exploitation, you know, so that we, we learn about our environment, we grow, we learn, we explore, we, we make good decisions based on available evidence and based on knowledge, based on cultural knowledge, you know, like all those things, right? So, so this is a, this is the, the, the, you know, I think organisms are a process, they're not a thing anyway, right? Cognition is a process, and societal decision making is a process, and really society is a process, you know, there's, there's not too many things in this world, there's mostly processes, living processes, intelligent processes. So that's, that's the, that's the hope, that's where this is trying to go, is to like, with that in mind, with that, with that broad understanding or broad concept in mind, how do we, how do we think about, you know, how we, how we come together as a society, how we cooperate, how we coordinate, how we make decisions, how we, how we learn, how we explore, what do we, what do we monitor, what kind of information do we seek, you know, what kind of experiments do we do, all that kind of stuff. Great. Do you want to go to that, seek answers to two questions? Oh yeah, yeah, so that's the last one on this, on the slide is, so, so the two questions that we hopefully would try to answer with, with this R&D program is, and one of this I already mentioned, but out of all conceivable designs for societal systems. So, so, so this isn't about capitalism versus socialism or something like that, there's like, I would think there's unlimited potential, we're creative, we're creative people, there would be a million varieties of, of societal systems and integrated societal systems that we might come up with, and some of those probably would work very well, and some of them probably would work very poorly. So, among those, what, what might be among the best, and not the single best, that's not the purpose either, it's not just to find one thing that works, it's to find like a, you know, more of a variety, a process of things, a, a mismatch of things that communities, that communities can choose to implement that, you know, works well for them, and that suits them, and that works well for their neighbors, and works well for everybody, works well for the whole, really. Second question is, by what, and this is, of course, the, the kicker is not only what systems might be best, but what, how would you possibly implement them, what viable way is there to implement new system designs, you know, like otherwise what's the purpose, you know, this isn't, this isn't an academic, this isn't just an academic, you know, excursion, this is like, let's change the world, and that has to be practical, and some, you know, and some has to be viable. So, those are the two questions, what, what, what might be best, and how do we get there? Awesome. Well, it really does, I think, read like you've turned it over a lot and communicated with a lot of people about it. So yeah, we can go to eight. Shall we go to the next one? Yeah, slide eight. Okay, so, so let's suppose, let's suppose your listeners are with me, and, you know, we kind of agree, like, okay, yes, transformation is necessary. And again, I want to emphasize, I'm not talking about reform, I'm not talking about a softer, better capitalism, I'm not talking about, you know, improved voter registration or like any of those things. I'm talking about de novo starting over from scratch, what might be best. And if it turns out that the old systems were better than anything that humanity can come up with, well, you know, that's the answer. But I can't imagine that's true, because the old systems were never designed in any kind of, you know, thoughtful, science driven, you know, process to, to, to test, to explore and to come up with a fitness like what is the, you know, we don't even have a fitness for our current society, much less as a fitness for societal designs. I mean, we have the GDP, but that's a terrible, terrible limited fitness metric. Okay, so suppose you're with me, suppose we're, we're on board, we want to do this de novo design thing. Where do we start? What's the, what, what, where do we even get off the ground on this? And I suggest that the way to do it is through first address worldview, from worldview, once we understand what the worldview is, what a reasonable, useful worldview will be for this project, then, then purpose drives worldview begets purpose. Once you understand what it is you want, what you value, what do you value, once you understand what you value, then you can say, well, I value a, and therefore the purpose is to have a manifest in society, for example. So once you have purpose, then you can think about what metrics, how would you measure whether are you, so here's a new design, is it fit for purpose? Does it do, does it fulfill its purpose? You know, that's the question. And then metrics go with some kind of fitness evaluation. And then finally, last of all of those would be the design. Okay, we know what, we know what we value. We know what this thing is supposed to do. We know what the purpose is. We know that a tractor is supposed to, you know, plow the ground or something. We know what this is supposed to do. We know how to measure success. And now, finally, then let's talk about design. What are the, what are the, you know, the specifics and mechanics and how does that happen? And the series is really kind of laid out this way. The first paper really talks about worldview and purpose. The second paper talks about, you know, the more the mechanics of things, like viability, how would you make this thing viable, things like that. And then the very last paper of that title, subtitle design. Okay, so that's how we, and maybe I will just mention here that I put metrics before design, because we might have some ideas of getting back to that preference vector. We might have some ideas, like we would like people not to die at 30, you know, we would like people to mostly live to a ripe old age and have, you know, enough water to drink and food to eat and all that kind of stuff. So, you know, what kind of design, once now that we have metrics to measure that kind of stuff, longevity and nutrition and things, what kind of designs would help us to reach those targets, you know. So that's one reason why design, why metrics comes before design. Okay, so now we can start to jump into the, you know, now we start to jump into this topic of worldview, the very first one, worldview and purpose. And, you know, I think we're on page 10 maybe of the first paper so far. We still got a long ways to go. Nice. Okay, so what I've tried to do in, you know, the main theme of this first paper is that I've tried to lay out a worldview that is cognizant of, that reflects some of the latest developments in science and a variety of fields and sciences. Those fields would be like complex system science, cognitive science, evolutionary biology, you know, a few fields like that, information theory and a few things like that. I've tried to outline a worldview that makes sense from that leading edge of science. And I would say too that science has gone through really kind of a revolution, you know, there was like, it's kind of like there's the pre 1950s slash 60s science. And then there's what we have today and there's enormous jumps, enormous leaps in understanding that have happened just in the last say 50 years or so. And some of those leaps, the ramifications are only now being, you know, they're now being felt, right? Some of the concepts are a distinct shift from where we were in the pre 1960s or pre 1970s or so. And obviously there's also, you know, we see the changes in our lifetime, you know, like I was watching an old show on TV the other day and somebody put money in a pay phone, you know, like they put a quarter in a pay phone to make telephone call and it's like, okay, well, that's, that's, that's history, you know, so there's all these technologies that have that are that are that I grew up with that are not they don't even exist anymore, they've been replaced by entirely new frameworks. And that's the speed of those of the speed of that evolution is is exponential. So it's tremendous changes happening very quickly. And the task of the program. Oh, yeah, one point on that would be potentially because you're taking such a broad perspective with complex system science and evolutionary bio, you might say that society has always been a cognitive architecture. But if you had asked in 1500, is society a cognitive architecture? Be like, well, no, I mean, you have agriculture, you have this, you have this, you have that. Whereas now if you tell people, hey, telecoms are, they run through everything and the internet of things, the internet of people, like all this sort of stuff, you tell people, right, actually, it's a multi scale cognitive architecture, humans are in the loop, and our algorithms are never independent of master and feedback with us. It's like, yeah, that was what the mainstream was telling me. So actually, it's a total alignment point, because it reflects how rapidly things are changing that it's just undeniably obvious that the communication infrastructure is the system that we're engineering. Right, right, absolutely. Yeah, yeah, communications have mind blowing changes and communications. And that brings mind blowing changes and outlook. But, but I want to emphasize a few points to this worldview that, that, you know, it's not, it's not just that everything is connected, like, you know, like, I know what a complex system is, it just means everything is connected and we're all kind of whole and blah, blah, blah. Okay, fine. But, but even for people who are in that ilk, you know, who understand the basic concept there, there's ideas that are that have come out in the last decade or so that, that are there, that are even pushing that boundary, you know, right. And I just want to highlight a few concepts here. And I think active inference really is playing, you know, is, is like a sense, a culmination of some of some of these ideas, or an embodiment of some of these ideas. The main thing I want to say is that life is intelligent and whole. So it's not just that everything's connected. It's that everything is intelligent. Everything is, you know, life is an intelligent information processing thing. Everything in us is adapting, learning, deciding whether we're talking about everything is cognitive, you know, and cognition really implies information and information processing. So whether we're talking about a slime mold, or a human, you know, there's, there's in everything in plants, in bacteria, in mold, in anything that has any life at all that can be considered alive is intelligent and is learning and reacting, not just reacting, but learning, reacting, and also deciding and acting and remembering and all those things. And you might ask, well, you know, that's impossible. Bacteria doesn't have a brain, you know, it can't be, it can't be cognitive, but it is cognitive. But we just have to relax what we, how we define cognition, you know, and when on the slide, I have a little thing there that every organism is cognitive in the sense that it displays capacities typically associated with human cognition, such as sensing, learning, problem solving, memory storage and recall, anticipation, anticipation is key to everything and attention is key to everything. So every organism does that, plants and everything else. And it doesn't require a central nervous system. And, and I might add to this that not only is every organism is incognitive, but essentially every organism is organism is cooperative to those cooperation and cognition go hand in hand because any intelligent organism, any organism that can act to better its, you know, viability is going to cooperate in meaningful ways with other organisms and, you know, other species and things like that. Nice point because there's cost to communication, whether it's exactly whether it's the cost of making the pheromone or just the time, which is super finite or attention fundamentally. And so costly interactions through time, the game theory are either to exploit and stabilize, which is fragile, or to succeed together. Yeah, exactly. And succeeding together cooperation is like everywhere. Once you understand what you're looking for, it's in the biologic world, it's like everywhere. So this idea that we're, you know, one person against all or, you know, we're a dog eat dog universe. I mean, it's, you know, in a certain sense, it's true. Obviously tigers eat, you know, whatever they eat zebras or whatever. I mean, that happens. Yes, of course. But in the larger picture over and over multiple time scales, not just, you know, in five minutes, but over evolutionary time scales and, you know, developmental time scales and everything. The cooperation is really the rule for the most part. And if you need it, if any listener needs proof of that, just think of who you think of your body. I mean, there, there's about a trillion, some trillion, some cells that are enormously harmonious, like your blood pumps every day or, you know, this is a this is like a miracle. I don't want to use the word miracle because I don't want to get into whatever that might imply. But it is amazing awe inspiring. The depth of cooperation just in our own bodies is like that's that's like evolution must prefer cooperation or else there would never be such a complex pattern of cooperation as we see just in one human body. Just to give one example from the bees. So from a species I study, it's almost like a sparring type of cooperation because when it was discovered that there was some workers with developed ovaries, there was a whole story about cheating and policing and about altruism and this equation says this and that equation says that. And then when you take a step back, it's like the colony having a distribution of ovary activation may be more ecologically resilient. So I as an evolutionary biologist never think well my interpretation of what would be lovey-dovey in this system must be how it works because that's so clearly not true. It's just to say that there are interesting dynamics within and between levels and in the long run cooperation and stable cooperation and like learning to adapt to your niche is a winning strategy in a way that locking down just isn't but unfortunately under high stress and high uncertainty conditions, simple strategies can become rife. So that's sort of a failure mode of a population. So that's what I understand also like you know what you're saying. Yeah no it's nice if a species can avoid highly stressful situations because that's often things don't go well once you get to that point you know things can go awry. What's that? Do you want to go to nine or stay on eight? Oh yeah I think let's go to nine let's move ahead. Perfect what is the individual? Okay so why in the world would I why would we ask this question and why would I spend you know multiple pages in this paper even discussing like of course we know what an individual is right or maybe not like that actually turns out to be a difficult question what is an individual and it's important to this and it's important to this discussion of societal systems because who are we who you know what is the purpose of a societal system what is it what is it supposed who is it supposed to serve you know so you have to ask really like it's it's good to ask if we're going to build a societal system who is it that it's supposed to service you know like who are we what do we want you know as part of figuring out what do we want what do we value who are we start there you know I would say so so we've already kind of touched on these themes but this idea of rugged individualism you know like from a certain perspective and a certain you know from a limited sort of timeframe perspective sure there's there's rugged individualism that exists right and can be useful in certain certain situations but by large that's not what life is doing you know that's not what we are we are it's really even difficult to say like where if I'm a rugged individual where do I actually start and where do I end you know like where is where is me this you know even physically it's hard to say because this physical me is really I think more bacterial cells than it is human cells right so so like I'm a sieve I'm a I'm a process through which things are flowing through I'm a I'm a ecosystem myself with bacteria and viruses and human cells and all of those components are necessary for me to survive today and for humans to survive you know over eons we're like a mix we're a bag of human like things and bacterial like things and viral like things and and we're porous and we're part of the carbon cycle and we're part of the nitrogen cycle and then you and then when you say like okay well how could you be a rugged individual individual when you're really this this porous smorgasbord of things right so so there was an interesting paper that came out I cited in my in my paper number one that was looking at this question of what is an individual and they were looking at it from an information theory standpoint you know so they came up with this they came up with this theory and I think do they have a name for it yeah information theory of individuality and they say base it's done at the bottom of the slide there and they say basically that you know an individual is a process just what's what we've been talking about before that propagates information from the past into the future so that you know implies information flow and implies a cognitive process it implies the anticipation of the future and it probably implies action and this thing that is an individual it is not like it is a layered hierarchical individual it's like you can draw a circle around anything you know in a certain sense and call it an individual under you know with certain definitions you know if you want to define what its mark of blanket is but but you know we are we are we are our cells are individuals our tissues liver say is an individual human isn't an individual a family is an individual you know and it just keeps expanding outward from there the society is an individual so it really it's none of those are have you know any kind of inherent preference levels there's no preference to any of those levels is everything's an individual layered interacting overlapping individuals and it's just it's just a it's really just a the the idea of an individual is just where do you want to draw your circle and then you can you know then you can talk about an individual at whatever level you want yeah so so that's all about information so it's all about processing information right and that's and so society is an individual and we are part of society and we're talking about societal systems and so at that level that seems to be our level of focus here at that level we can talk about society as an organism as a cognitive organism that is propagating information from the past into the future and from an active inference standpoint we can say under the you know under the fitness score whatever word you might want to use for that of reducing uncertainty so we act to reduce uncertainty right one interesting point on the ant colony again I just it's the example I'm coming from is the 1911 paper by William Morton Wheeler is called the ant colony as an organism not a super organism because super organism implies that nest mate would be the true organism and that's something that insect with a six-legged version is now it's the whole super organism oh well the ant colony is a society that whole frame is actually the shadow of what the evolutionary reality is which is that the ant colony is an organism not a super organism and the ant's are tissues and so which level we prioritize or do we say no there's no a priori level ant is just I'm not even gonna say there's anything out there called ants it's it's you how you're thinking about it or do we get lost or we gonna find a ladder in that multi-scale yeah well the ladder is you know the ladder is active inference because it doesn't say active inference doesn't say make a make an internal model of the world that is accurate that actually accurately captures all the all the details of the world of the universe that's not the point that's not what the mind does that's not the point the point is to act under uncertainty given some useful model of the world act under uncertainty so that your fitness score improves and by fitness score here we essentially mean you know and anticipated uncertainty so I would very much like to be have some certainty that I'm going to be alive tomorrow and if it's freezing outside and I don't have a coat on you know that that becomes iffy so I'm going to be happy if I'm going to be I'm going to go find a coat because it is going to reduce my uncertainty about survival over the next 24 hours but you can expand that you know outward right we need to act all organisms are acting under uncertainty and and we can think about that as we can think about that we can think from that perspective as a society of what are we doing and how do we measure success well we're measuring success by acting under uncertainty and then and then paying attention to what happens and then acting the same or differently or you know some other way or somehow some then choosing to act again and this cycle of act uh you know act process act process act process you know model act model act model that reminds me of course of the OOTA observe orient side acts model and other sort of cyclic models of action and perception and then I would say that active inference provides a few nice little benefits over other phrasings of action and perception qualitative and philosophical ones like in activism as well as quantitative ones like cybernetics and other kinds of control there so I totally agree this is nice stuff and uh nice we can carry on if you want you want to go to ten okay yeah yeah ten let's go to ten so everybody's listening you can ask questions in the live chat or you can join us in the jam board and flip over to slide ten so so so I so I'm thoroughly enjoying this conversation but is it true that we have about a half hour left of it yep we have 30 is it going that fast I believe so we can we can slice slides or move slides to another day so however you want it I'll I'll try to go or I can chill for a few more minutes afterwards try to go a little quicker but there's so many things to say you know there's you know it's it's one thing to read the paper and and written you know kind of written in a in a you know sort of scientific terminology and you know it's but it but it doesn't really convey the feeling you know like you have to kind of think about what is the feeling of this whole project and we can do that better in a conversation I think agreed okay so so so then you know we're on this topic of what is what is our worldview what do we value and what is our purpose and then we've come to this question then okay so who the heck are we then you know we're we're and not only who are we but who are we building these systems for you know what what is what should societal systems serve who or what should societal systems serve and the only reasonable answer that you can come up with is that societal systems should serve the the extended self like not just the body not just the family not just the you know the thousand people in a society or the 10,000 or a million or whatever but their environment to the the society next door that they're engaged with and cooperating with and coordinating with the society across the planet that they're sharing information with and learning together with and so it's the whole that we our metrics as we as later as we come to metrics those metrics have to represent both the cognitive process how good how are we cognating how well are we cognating are we functionally cognating and are we achieving through that cognition are we achieving the kinds of aims that is serving the whole is the environment improving is the you know quality of air improving is the quality of life improving for individuals right yes so we are so this internet show we this is the worldview in a way we are intimate with our greater world we are individuals but of the nested overlapping variety individual cells bodies groups communities ecologies nations and all of civilization we're not separate in any absolute sense and there's no privileged level or scale to any of that nor are we passive by standards in this unfolding this is not this evolution is not it's just a chance thing like by chance somebody does this one day and then evolution goes on another another avenue no there there are there are opportunities in the environment that we can react to that lend themselves to to to providing information or providing gain of benefit of some kind and and you know that we are driven we are we are consciously creating and you know even a really great societal system the integrated societal systems would be consciously creating acting cognating acting cognating consciously creating and it towards some towards some goal and that goal then has to be you know the maintaining of vitality being in the for the extended self all right so that comes to slide it's that's the topic of slide 11 and what is our purpose so so over in the right there i just want to re-emphasize we are anticipatory we are cognitive we are problem solvers we are a we and then i have below that i am a we you know like i am i can i am it's yeah i'm intimately connected with this i'm i'm everyone in that sense you know yep well yeah the whitman um you know i contain multitudes and also gilbert at all have a paper called um we were never individuals kind of on that wavelength that you were talking about with the sort of distributed systems all the way down approach and uh also denis noble no privilege level of biological causality similar uh basically realization that multi-scale perspective complexity science basically entails either the choice of a a priori level like saying it is multi-scale and humans are the best scale or gaia is the scale or quantum is the right scale that's a claim as well as it being a claim actually there's no privileged level of causality so that's the sort of table as it's set right right right right right and you know what it's not that really this this entire project you could say in a like a sentence you could say this whole project is to help us be who we are more be more honestly who we are more real to who we are right it's not the it's not to to have people behave in some unusual way or some altruistic way or anything like that it is to it is to have it is to be more more ourselves more fully ourselves more completely ourselves and then all of these pages all these things we're talking about is who that self is who who who are we really and it's about the adjacent possible for who we are who we are is not an essence that is uh there's seven seals and it's being unlocked it's actually something that's being drawn out through inactive realization in the niche through niche modification through stigma through becoming and and then the adjacent possible is where the imagination and the planning comes into play and if people are hesitant to talk about the adjacent possible for who we could be just think about chess it's the adjacent possible with a strategy on the board and we're talking about the adjacent strategy possible for who we could be in terms of our strategy for you know all these recursive layers our strategy for how we think of ourselves and all these other things you're talking about absolutely absolutely and then and then ultimately serving the serving the kind of fitness purpose of you know if we take action a is that going to reduce our uncertainty about those things that we that really matter you know that are that are the the key variables you know okay so uh so uh you know this is maybe a summary now we've we've talked about about about who we are and I just want to say a few words then we have a purpose and from like a biological like call it an intrinsic purpose but like from evolution by being the fact that we are a part of life we have a purpose because all organisms possess this sense making capability a casual power causal powers and the intrinsic purpose of an organism is to achieve and maintain vitality a sustainable flourishing of self which can include that extended self and we do that by sensing and evaluating states of the world and ourselves and implementing appropriate actions that that are based on anticipation we we anticipate what will happen if we do or don't take an action and we choose if we're for functional we choose those actions that can serve our intrinsic intrinsic purpose of of of remaining vital into the future so anticipating vitality um and that obviously implies some kind of modeling of the world anticipation implies some kind of modeling in the world so that's an organisms intrinsic purpose and then society by you know by uh you know it's just that's necessarily shares a similar related intrinsic purpose which is to achieve and maintain vitality maintain and maintain by maintain I mean anticipate into the future maintain uh vitality which is accomplished through cognition and cooperation so the self that we must keep vital is the extended self and it follows that the intrinsic purpose of societal systems like financial systems and other is to serve the intrinsic purposes of society so now we know or we don't know I'm just I'm just putting this out from as my take on it but this is what I'm offering as a concept for the world to chew on you know uh and obviously I'm getting these ideas I'm not coming up with ideas myself I'm I'm digesting hundreds of other papers that have been put into this kind of submarine in a way um so that's uh so that's we need to our purposes to remain vital into the future and and that and when we talk about the self that remains vital it's the larger self all right so um slide 20 the 12 uh or excuse me 12 yep yes slide 12 yeah uh so there's a lot a lot of discussion about complex systems you know we've been discussing complex systems and I just want to make a couple of points here because uh commonly some it is not uncommon that someone will say a complex system well that just means that it's uh liable to fall apart at any moment you know it's just too complex it's going to crash uh but and that that obviously can happen you know the systems can collapse quite quite true but obviously life would not be doing very well if the if the evolution builds complexity in species and you know in organisms and ecosystems if life would be have a rough go of it if it was so fragile that uh complexity became a burden and um and uh you know come and then you know you reach a certain level of complexity and then you'll fall apart that's not really I don't think I mean that can happen but that's but but complex useful complexity doesn't make you fall apart it actually just does the opposite it serves what we've been talking about all along and that's problem solving so we are anticipatory organisms we are problem solving organisms it's our nature most of what the human brain does is to solve problems of one kind or another social problems physical problems whatever and maneuver in the world you know in a useful way and complexity is what allows that the new there's a number of studies that I cite here that show that as a organism even as a robot you know faces uh more difficult pressures from its environment it complexities and complexities by complexity then it's it's it implies a greater number of parts coordinating or cooperating in some way uh to you know solve this new challenge and obviously as a human we're very complex we have we have complex needs we have we can think not just what's going to happen in the next millisecond but what's going to happen we can think about what's going to happen in a hundred years I mean part of this project is to think about what might be happening over the next hundred years or even a thousand years so as an organism complexifies it become at least potentially becomes a better adapted to solving more complex problems so you could in from that sense you could almost equate complexity with problem solving capacity you know at least in a you know in a in a general sense and then I talked about that just reminds me of in the free energy calculations that we have gone over in various papers it's like accuracy is the modeling imperative and then complexity is tolerated to the extent it facilitates accurate modeling so if you get the one parameter model and you got 99% and it's adequate and it's good then you're good to go and you're going to go for simplicity but then what you're saying is actually the appearance and the hallmark of complexity in the world it means that that organism has the need to solve problems at a given level of counterfactual depth or inference skill or temporal depth temporal thickness exactly exactly exactly yeah that's that's it yeah yeah so so I talk in the in the in this kind of middle of the paper now we talk I talk about a few ideas good regulators requisite variety self-organized criticality and then the free energy principle from active inference and maybe I'll just try to briefly talk mention what's what those means for what those ideas mean for people who aren't familiar so good regular really came from the good regular theorem or whatever it's called really came from cybernetics ash ash ash b yeah uh his law of requisite variety and uh the yeah it's the concept is that a organism or a you know a system must be must be a model of that which it but that needs to control so so I am humans are a model of their niche their physical niche the gravity our bones are a model of gravity or you know we are our our our our our our complex problems that we face of maneuvering through the world with our senses and our sensitivities and our our you know vulnerabilities we have to be complex enough ourselves to handle a complex environment so that's just like another way of saying that that systems complexify in order to handle you know adequately handle their environment and and part of that complexity then is having enough dials you know the system has to have enough dials and enough levers and enough movement opportunities to control that which to to control that which they need to control so a good controller has a similar variety to that which controls that's the requisite variety part and I just had the technology allows us to sort of play with that for example someone driving a car with the affordances of just their arms and a generative model of the road can be driving in very challenging situations so this doesn't mean that you need to have the road inside of your head or need to be the road to drive on the road it's a statement about how action-oriented systems choose actions right right and maybe I'll just mention that that plays out on all timescales right so the immediate mechanistic timescale the developmental timescale you know the evolutionary timescale and everything in between right so you know because like an organism you know think of an organism that only once every century it has to deal with a you know a one-year drought or something well somewhere in the mech in the mechanics of that organism has to be a little piece that is capable of digging down into the dirt and just hanging out for you know for a whole year or whatever without water even though that only happens once in a total blue moon you know like like we have to have this we have to have this flexibility within us even for those extremely rare but deadly you know potentially deadly scenarios and in situations that we might face right okay so complexity essentially you can almost equate it with problem-solving capacity and again the world slide 13 slide 13 help organize yeah so yeah so so maybe I want to just kind of back up for a moment and just say like again what is the purpose of going through this stuff you know I mean I mean I mean I mean it's useful sure if you want to build some new system it's nice to know what the purpose of it is you know I mean obviously but but these ideas that we're talking about and self-organized criticality is one criticality is one of them these ideas can not only serve this larger context of understanding what it is we're trying to do here what is what are we building what is this purpose how do we measure success you know it can have immediate input or immediate influence on how a design might happen and the self-organized criticality is is one example of that so it's not just we're not just in the theoretical philosophical set here we're we're also talking guts and bolts like okay so what might designs be like we have to think in our as we're going through the series you know we can think in our head okay that's interesting what kind of designs might reflect that concept and we'll go into an example here with self-organized criticality so the idea there is that was coined by back back in 87 the term self-organized criticality and it's it's really not a controversial that that living systems and many most systems in life complex systems organize in some way but the idea of self-organized criticality is that the organism itself is adjusting is is keeping some kind of adjustment to to maintain a critical state and by critical state I mean a state on the like you can think of a saddle point so if you drop a model on us on a saddle it's going to not stay there it's going to you know it's going to change it's going to change one way or the other right so a critical state is like that that threshold where things are about to change from one way to another way and it turns out with you know work and information theory and other other fields of recent in recent years it turns out that processing whether it's we're talking about a computer or some other you know machine or or a brain turns out that processing is kind of optimal in a sense when this when the system is at a this this this this critical state and some people call it on the edge of chaos because things are things can easily change and sometimes it's you can think of that threshold as a as a as a as a threshold of a critical state you can think of it as a threshold of a threshold be say between exploration and exploitation like should I should I go should I go find a new planet for humans to live on or should I fix the planet that you know should I fix the systems on this planet first you know how do we balance exploration of the new versus using the information we have to improve what we already have so you can think of that as exploration exploitation tradeoff stability agility tradeoff do we do we remain stable and use ideas from the old in the past or do we are we more agile and we're more flexible and we bring in new new ideas so it's like you can call it a old new trade old new tradeoff but whatever whatever tradeoff you want to call it it's this sitting at the edge of going one way or the other maximally flexible of going one way or the other and it's at that threshold that level that point the kind of that region of criticality that information processing seems to be maximal so if there's no wonder then that the human brain is organized in such a way to be living on this threshold between agility and stability and now here's an example of that from like a real world example so a a system that is at a critical state is going to be maximally sensitive to input so that means that there could you know when just when that marble is sitting on the saddle just a little bump to that saddle from one little corner of its universe you know right like just one little organism bumps it and maybe that marble rolls one way or the other right so that one one little input had a major impact on how the whole thing moves its trajectory into the future right but isn't that what we're isn't that kind of what we have in mind for democracy I mean don't we want everyone to have access of engaging into the decision-making processes of a society and have every voice heard in at least in the sense that there's the possibility that just my voice just me doing my participation in this system might actually ripple through the system and have a you know a real effect a useful effect I mean I think like maybe maybe self-organized criticality can help to inform us the concept of self-organized criticality can help to inform us of what do we want from democracy or a decision-making process right you know that just makes me think about different like like landslides and that's something that criticality theory and catastrophe theory has been used to study yeah and instead of cascading failure we can think about like cascading neighborhood cleanups so a bunch of people just say today just for an hour I feel like doing a little cleanup and all of a sudden one person puts up the fly and then it's cascading locally in some just you know unspecified way but all of a sudden you're getting this this distribution with a ton of small little meetups and then several really large sweeping changes but the total number of people cleaning up is higher because you offered the affordance and the ability for the affordance to sort of propagate that's right that's right we're talking about a propagation of a propagation of information a propagation of action and the possibility that even you know just to wonder a few individuals could start a little chain chain reaction that actually does affect in a positive way society now it's a little too it's almost too bad that sandpiles were the original you know topic of this of self-organized criticality because as you point out it's not really about things falling apart it's about if you think of again if you think of a complex system as a system more capable of solving more challenging problems then more often you can think of self-organized criticality as a way to propagate information when it is really needed when the system needs to change then information is you know it ingests information from its world from its senses and can act accordingly we we just um submitted an abstract with criticality and active inference and one of the points was actually the existence of self-organized criticality implies a far from equilibrium system that's actively pumping energy in that's right because when it's a passive system that's not locked and loaded you don't get that kind of a nonlinear response when you poke it it just stays there right right right right and I think you know maybe we won't have time to go into it right now but this also the idea of criticality is a little bit at the center of of structural organization of society through you know in the active inference theme like how is it that that that organisms and life and other systems then you know form structures well they form structures so that as you were just saying they form structures so that they can maximally cognate and those that happens to be structures that are existing in some sense on near critical thresholds so and that and as also as you mentioned that of course takes energy you know it takes energy to make that happen you have to eat food to think you know right nice 14 okay so now finally we get to active inference all this discussion we're finally getting to the point here right welcome for his lab yeah so I had and I had already touched on some of this before but it would you know today if you're going to develop a really good AI system you and you're going to have a you have a robot saying the robot has to act in some environment it is pretty well understood that that if you program that robot to you give it a you give it a I mean traditionally you'll give it a fitness function or some kind of valuation function and it's for example it's good if it you know you lose points if you fall through a trap door and you get points if you you know whatever find find the piece of cake or something well that's that's fine for extremely simple universes that your robot might work in but as soon as you get beyond you know as soon as you get to any kind of more realistic universe that your robot has to work in that pre-programming pre-programming concept just kind of falls apart it you would require the the practitioner to think ahead of all the things that the robot might encounter and then how to value certain you know value those situations in certain ways and that is really what active inference offers is a is a kind of a cognitive understanding or a mechanism by which an organism will where its fitness score is in a sense involves both you know achieving goals and exploring its world to for for for epistemic gain so that's what we would like the that's how we would like to program the robot in a sense so that it can learn from it can learn on the fly from its experiences it can it can alter its actions and goals as it becomes clear as it gathers more information from its universe as it as it meets new situations that were never never conceived of by the by the programmer that through through an active inference or an active inference like you know mechanism it can learn and explore and critically balance exploration with exploitation and then we come right back to that whole concept of of criticality so you know what you would really like your robot to do is remain at that critical phase between exploring what's out there and making use and goal directed behavior of what's in front of it and and you know that's how you could program this world this robot to act in the world and be pretty good at it you know if you if you build it well so that's what the systems of a society can help a society to do you you don't you if we're talking about building new systems i think it would not be wise to say this checklist of like we want it this level of education we want to want this you know to react this way in this situation react this way in the situation and this level of you know whatever money and this level of this and this level of that while those kinds of preferences can be a useful start society has to be alive in its moment you know in the moment a society is alive it's cognating it's it's it's it's actively you know comparing what it's the result of its actions to the model that is in its head and so active inference offers this way to to balance exploration and and and exploitation and remain critical and remain optimally cognitive right so that's part of it and then part of it i mean and for me this the the idea of the embodied you know the three fouries and the body of active extended uh and cultured you know that abcd's got a whole book of adjectives actually they sell they sell that yeah you know the this is what i really am attracted to an active inference is in a sense it's kind of a simple concept it's not really very complicated you know if you've studied bayesian uh theory at all it's kind of straight you know in a way it's kind of straightforward but the the you know the way first and has connected the dots and and and extended that into the bigger picture of life kind of it to me it is it is rich there's a there's a lot yet to be learned and gained and explored in this umbrella of active inference awesome okay so we're getting close to the end here i don't know if maybe we should call it quits or um you know we could either go a few minutes over to just walk through these last few slides or we could um just uh whatever you'd like to do you know i think because i've concede now i'm not very good at walking fast i'm i i'm too verbose you know it goes through a lot it goes through a lot so i think it's it's actually it's good to sometimes just take these little side paths yeah well maybe that maybe this time you know leaving it at the active inference maybe that's a good stopping point well well then maybe to kind of close on that active inference note um you know we are the active inference lab and so we always think about these sessions as a two-directional highway there's people who are in the active inference community who are being exposed to some new ideas and then there's people who are being drawn in by these other things that you're bringing up which are so prescient as far as the issues as well as systems that influence everyone like governance information maybe this is the first time they're hearing about active inference so it's sort of like we're uh on the cliffhanger and maybe when we come back next week we can really um do a little recap and then start in with where is active inference coming into play how are we going to make it specific how are we going to include people in this process what would it look like to do it's a really nice spot i think to to kind of pause okay sounds good nice so um we will hang out again uh 3 p.m on april 13th pacific time and that will be the second of the three sessions so thanks again for joining john this is like an awesome discussion and i hope people read the papers and we can share this first youtube link and invite people for next week's session which will be yeah at the time stated so thanks everyone see you later thanks