No Way Out
Welcome to the No Way Out podcast where we examine the variety of domains and disciplines behind John R. Boyd’s OODA sketch and why, today, more than ever, it is an imperative to understand Boyd’s axiomatic sketch of how organisms, individuals, teams, corporations, and governments comprehend, shape, and adapt in our VUCA world.
No Way Out
The Reality of Boyd’s Loop: Neuroscience, Complexity and Flow with Bobby Azarian, PhD | Ep 36
Join Bobby Azarian, PhD, as we dive into the fascinating world of science, philosophy, and interconnectedness. In his book, The Romance of Reality, he explores the intricate web of disciplines that influenced Boyd's OODA loop and are behind today's leading natural laws and principles. Journey through neuroscience, physics, and the connection between Information Theory and the Second Law of Thermodynamics. Prepare to challenge the nihilistic view that life lacks meaning or agency and discover a new sense of purpose.
Discover how society can be seen as an interconnected organism and how this understanding benefits not only business leaders and politicians but also individuals seeking to optimize their decision-making. Delve into the world of Active Inference and its role in understanding goal-oriented behavior. Explore the concept of phylogenetic learning, the accumulation of information in the biosphere and the genome of interconnected organisms.
But it doesn't stop there—Azarian explores the science of prediction, cycles in society, and the opportunity for change in our current crisis. Ancient knowledge like sacred geometry and patterns in the universe provide valuable insights for problem-solving. Uncover the relationship between entropy, thermal entropy, information entropy, and the inefficiency of energy conversion in heat engines.
Stay tuned for exciting upcoming projects, including a second book and the Road to Omega Substack, where Azarian continues to explore the complexities of life and the universe.
Bobby Azarian, PhD on LinkedIn
Bobby Azarian, PhD on Joe Rogan
NWO Intro with Boyd
March 25, 2025
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All right, no way out. Listeners, today we have the author of the Romance of Reality. Why is this book so important for the show? For understanding John Boyd's Oodleook, for understanding the universe? Well, let me start here. It is probably the most important book I read about John Boyd's Observe, orient, decide Act Loop and his understanding of the multiple disciplines behind it when he was working on it, without even mentioning John Boyd Oodleook. So why is this book so important? Well, it really walks through the neuroscience, the physics, the second law of thermodynamics and makes connections to what's happening today with active inference to construct a law, things that came about after John Boyd passed away in 1997.
Brian "Ponch" Rivera:So, without further ado, I wanna introduce today's guests and ask him a very important question while I'm introducing them, and that is Bobby Azarian. So, welcome to the show, bobby, thanks for having me. My first question is in your own view, in your words, why is what you're doing on your sub-stack, the book that you released a year ago called the Romance of Reality, and what you're working on now? Why are they so important to executives, athletes, politicians, military members? Why is it so important that they pick up your book and read it?
Bobby Azarian, PhD:Well, I think there's importance for the individual and there's importance for society. So for some of the people you mentioned, it might give them a new sense of purpose. I think a lot of people believe that science has decided that the story of nature is this kind of nihilistic story, like there's no meaning to anything, life is doomed and we don't have agency or free will. And I think a lot of that story is wrong. And when people understand that they can embrace purpose without feeling like they're embracing something irrational.
Bobby Azarian, PhD:At the societal level, for people like business leaders and politicians, I think there is a lot of benefit to looking at society as this, basically analogous to an organism. It's a network of individual agents and our bodies and our brains are similar. They're not single things. They're communities of cells which in some sense have some autonomy, and the magic of cognition arises from the collective interactions of those agents. So we can actually use evolutionary principles, cybernetics principles to understand the dynamics of social systems. And when we start to understand that the nation or the global network of humans is a social organism, it gives us insights we didn't have before. We start to see hurting the environment or not caring about the welfare of others comes back to affect us eventually, because we're part of an interconnected, interdependent network and it sounds obvious like, okay, that's true, but we didn't always think that way and our policies, our system structures are not really designed taking that information, like that knowledge, into account.
Brian "Ponch" Rivera:Okay, there's a lot to unpack there for our listeners. You touched on the law of unintended consequences, that every action we emit into the external environment we may be responsible for down the road right, even if there's inaction there. You talked about agency. You brought up cybernetics, so here's what I like to do. I wanna start off the cybernetics part. Maybe look at some of the work of Ashby to understand what's inside. If we break up the internal world to the external world, which we know, there's really no disconnect or everything's connected. But if we put a blanket around that, or a boundary or a markup blanket, which we've been talking about on the show and it's in your book as well can we start there and talk about putting a boundary around something and how cybernetics informs what's on the inside?
Bobby Azarian, PhD:Yeah, cybernetics was really interesting. It was this field that tried to understand complex adaptive systems like living systems, and then basically that informed design for how to make artificial systems that are adaptive, so even just like a basic thermostat, is kind of modeled around this concept of staying at homeostasis and if there is a change from that desired goal state then, for example, if it gets too hot, the AC kicks on and brings it back down to the goal state. So it's about goal-oriented systems. These systems in nature predominantly living systems. They seem to have a purpose that other system, inanimate systems, don't, so they're goal-oriented.
Bobby Azarian, PhD:In philosophy there was the term teleology or teleology and that referred to like living systems, seem to be animated by this purposeful behavior. And now we attribute that purposeful behavior to the causal power of information. And living systems are special because they encode information about the environment. And that is really what evolution is doing. The evolutionary process and Darwinian mechanisms are encoding information. So when something is evolving and adapting, it's learning, and when it learns that this is a feedback loop, it's updating its model of the world. Basically, living systems to survive have to create a map or a model of the world around them and then to survive optimally to live optimally, they have to make sure they're updating that model of the world to be most consistent with actual reality. Because our model is always gonna be this kind of rough model with uncertainty, that has errors. That's not completely like reality and we wanna try to get our model to match realities best as we can and that's really what the free energy principle. So in cognitive neuroscience there's like a new theory that's trying to understand the brain and the mind and how organisms survive and it's by decreasing your model's error, your prediction error, and it's really based on cybernetics.
Bobby Azarian, PhD:So cybernetics was in like the 30s and 40s and a lot of interesting things were happening, the ideas that led to the computer. So computational theory, information theory, that it's all like these digital world that was coming about, and cybernetics was trying to understand these adaptive systems and made a lot of progress. But I think it was over a lot of people's heads that required understanding math, biology, really everything. And then it became I learned this writing in the book it kind of became part of the Soviet program. It was like kind of based on like cybernetics and during the Cold War US universities because US saw cybernetics as a Soviet ideology kind of suppressed. Cybernetics Like we wasn't taught in schools and it was kind of like, and it was.
Bobby Azarian, PhD:I learned that in Europe's information theory, like cybernetics was called information theory, I believe. But yeah, jim Rut from Santa Fe Institute was talking about this. I didn't know that. I mean, and there is a Russian cybernetist named Valentin Turchin who is was really influential on me. He wrote a book called the Phenomenon of Science in the 70s which you know talked about like neural networks. Basically it was like kind of like the beginning of machine learning from a cybernetics perspective. And he actually had to leave Russia when he tried to publish that book because it really brought up this cybernetics worldview that my book kind of puts forth and it kind of went against the government's ideology and he had to flee to America. So there's a lot of confusion there, a lot of like you know, interesting story. That's like ideological, but basically cybernetics is the study of systems that pursue goals and they do it using feedback loops and yeah, it's been influencing neuroscience more than ever now. So yeah, we're starting to revisit all of those ideas.
Brian "Ponch" Rivera:So cybernetics was a key influencer for John Boyd. You brought up the model of the external world. You need a map of it. John Boyd called that orientation and in your book you start diving. I believe you start diving a little bit into genetics and within orientation and John Boyd's orientation, that internal model or map of the external world that's being updated. Perhaps it starts with genetics, dna for biological creatures, and then we're learning a little bit about epigenetics, the possibility of that trauma or information from our ancestors has passed along our DNA from generation to generation. Is that what you're tracking as well when we start thinking about what's inside of that map, that biological map for humans?
Bobby Azarian, PhD:Yeah, absolutely so. All of that is information that life is encoding and yeah, it's all part of the story, so you can't leave any of that out. But basically, prior to brains, life's memory system were, the only memory system, was genetic memory and genomes were encoding information in living systems from the moment life emerged. And that learning process because I mentioned that in this paradigm, what the emphasis is on is that adaptation is a process of learning. It's accumulating information in the biosphere, in the genome of all of these organisms that are part of this interconnected network, and so this is called phylogenetic learning. Phylogenetic learning refers to phylogeny, so it's generational learning. Basically, before organisms had brains, they couldn't update the model in real time, because brains can rewire, and brains wire from the time we're born in response to our experiences. But before that, when you have a genome, it's pretty much fixed and it's not fully fixed because there is epigenetics. But epigenetics controls how the genes are expressed, but the actual genome for a brainless organism is what it is. So the learning occurred when there's a population and that you have these organisms making copies of themselves and the copies have a variation on that design, and then the copies that aren't fit, that aren't well adapted to the environment die out, and so, basically, evolution is a process that's pumping information from the environment effectively. It's as if the environment is pumping information into life. We're encoding environmental information by interacting with the environment and the ones that are well adapted to the environment stick around. So it's as if natural selection is weeding out the bad designs and the biological information that's not predictive of the environment and we're like, basically, what happens over time is, each generation is like an iteration of this learning process. So it's a Bayesian update. It's an update, an informational update to the gene pool of that population. So that's how learning starts off in life and you get an optimization of this genome for fitness. But the optimization happens over generations and the population becomes optimized over these long periods of evolutionary time where, when brains emerge, you can do this. Like you know, there's adaptation in an individual's life. You're always adapting to your environment and if you want to adapt optimally, you want to.
Bobby Azarian, PhD:So, when it comes to humans and our level awareness, we can. We've become aware of our model and we've become aware of the uncertainty in our model and we want to correct for those errors. So that's really something we don't do, but with this kind of like new height into awareness, because we always kind of assume we're right. We go through life like making decisions and even when those decisions don't pan out, we usually don't change our model or our worldview. Our worldviews are particularly rigid because they are a belief system that we've found to be effective and maybe they bring us comfort. They do all kinds of things like that. So there's a lot of times where reality clashes with one's worldview and a person will not update their worldview for those reasons, but they will be committing errors because they're basically not incorporating. For example, not incorporating evolutionary theory into your model because you think it conflicts with your religious beliefs, for example, will lead to suboptimal decision-making because you're not optimizing your model.
Brian "Ponch" Rivera:Wow, there's so much to unpack there. You got the free energy principle happening. You have surprising environment. You're hitting on entropy, informational entropy, maybe in thermal entropy a little bit. There, that new information that's coming in to update our model, how we can suppress it or adapt to it. Adaptation is learning. There's so much in the last few or four or five minutes there that we can unpack. I do wanna get us focused on the internal model, and reason for that is as humans, as a cell, biological creatures, have this internal model or map of the external world that's getting updated. My question to you is what about teams or organizations? Do they have an organizational learning history that they, you know?
Brian "Ponch" Rivera:we've got yeah that's a great question.
Bobby Azarian, PhD:Just to quickly simplify you know all those things because it can seem overwhelming for the listener. There's a simple story here that integrates it all and it's kind of like problematic that we've. You know these fields got developed separately and it's understandable why it kinda had to be that way. But there's one story here and keeping the fields apart just really obscures everything. So basically, the story of nature is there is this tendency for things to fall apart. We see it all the time in real life, when your house deteriorates or your body deteriorates, for example. And you know living systems are kind of unique because they seem to resist that tendency for a while. And then we have offspring which you know resists that tendency. So really the whole biosphere, if you see it as a functional entity in itself, it's been resisting that tendency since it's emerged. Life never blinked out of existence and came back. So life is incredibly robust against this tendency towards disorder and that's because it's encoding information and it's learning how to stay organized in the world. And it does that by encoding a map of the world, at first through these evolutionary processes and then through, you know, regular individual, you know learning during one's lifetime. I'm trying to argue that we should think of all of those things as evolution, as development, as evolution. So it's broadening the definition of evolution, even though really that's how people thought of evolution before. Darwin's theory referred to all those things, sometimes more even like development. So, yeah, it's a story of life has to get information, to stay organized, to persist, to survive, and that information is encompassed in a model or map of the world. So it's not just independent bits of information, they all fit together in a narrative and that's pretty much what your worldview is. It's like a new version of a survival strategy, but like a worldview is a strategy for survival too. It's part of your model. So the question about organizations they're these. We've, you know, said that societies are organisms basically. So it's actually totally helpful to think of organizations as little social organisms as well. They're these like integrated computational units of agents working together that have formed a collective mind, and there's a lot of power in forming a collective mind. It just increases the intelligence of the system. So, if you wanna get stuff done, if you wanna have superpowers, team up with someone and start working synergistically on something and you help each other.
Bobby Azarian, PhD:Basically, use this evolutionary algorithm, which is variation and selection. So you have an idea, you tell somebody about it and then they you're basically exposing it to a natural selection process, like with criticism. So it's exposing the errors and then it finds out the problems and you are trying different variations on it. So you can use these principles, and that's exactly what John Boyd did with the OODA loop is, you take a cybernetics principle and you can apply it to these practical things, and it's a shame that people haven't done this.
Bobby Azarian, PhD:Whether it's military or business, there's lots of principles from systems, you know, theory, complex systems, science and stuff Many of them that haven't even been articulated in a way where people can see how to apply it, because it just seems completely theoretical and related to biology. So there's lots of low hanging fruit here. And I didn't know much about John Boyd until you know I learned about him from you and then I heard Jim Rutt talking about him and it's really interesting stuff. So, yeah, I think that we need to be thinking in this way.
Bobby Azarian, PhD:And organizations you can actually think of them as having collective models of the world, and some of the work that Friston and others are doing Maxwell Ramstead is another person they're looking at these collective collectives of agents and using the same equations as they do for individual organisms and humans and it looks like the whole collective is minimizing the prediction error of the collective. So there isn't a model encoded in the dynamics of the system. There's, you know, distributed information across the system. It's hard to point to. It's not like you know a genome where you can see, you know it's very clear, but just like a genome or a brain, there's still structures with these relationships and the information is stored in those relationships.
Brian "Ponch" Rivera:We had a phrase shared with us recently called collective orientation, and I think that's kind of what you're talking about is it's distributed as a collective orientation, collective model of the external environment. We can't say world here, because this may actually scale to the universe. Right, the universe may have an internal collective orientation.
Bobby Azarian, PhD:Well, yeah, sometimes when I use the word world sometimes, you know I might be referring to the universe too.
Brian "Ponch" Rivera:Okay, yeah, but it's just that we're not looking at the our world. We're looking globally at the universe, the external environment. To what do we put a boundary around?
Bobby Azarian, PhD:Ideally Well. So, yeah, it depends on what you're interested in, but so, but yeah, I would say that is the problem that a lot of people, their models, are kind of focused on what they think is relevant to their business, without realizing that, because of this interconnected nature of nature, that the model needs to include all of these other dynamics. And until we do that, it's gonna look like there's things that just are completely unexplainable. The stock market, for example. I saw you talking to Jim about that and he said he lost interest in that and there's a lot of randomness in there is. But I would argue that a lot of that apparent randomness could be explained in terms of really interesting cycles that we're not aware of, that are all kind of part of this process, because basically and you, I think you all, you know, we talk about these phase transitions and physical systems, and if you believe that social, you know social systems are these organisms, you know these physical systems that have these similar dynamics, then there are phase transitions in society and basically, I'm saying that's what we're undergoing right now.
Bobby Azarian, PhD:Whenever there's a period of crisis, it's like screaming for change and it seems like the world's gonna end, but it's really the conditions that create. Well, they create the conditions that will allow a new paradigm to emerge. That is the paradigm that is needed at the time. So I think a lot of the things that are going on, it's basically like you have a system and it's trying to self-organize and you're seeing these little signs of it, but it can't like.
Bobby Azarian, PhD:Right now we're talking about this worldview, there's a lot of people interested in like systems, thinking in this interconnected nature of reality and looking at things from this perspective, and we're really trying to make it happen, but it's like we can't bust through that signal.
Bobby Azarian, PhD:People are, like, you know, just focused on, you know, influencers or movies or whatever, and it's really that like, basically, shit hits the fan and it creates this need for it and then suddenly that signal that's been trying to get off the ground can go viral because suddenly there's a global need for it and until that need happens, it can't get off the ground. So there are all of these cycles that I'm arguing have a random component, but they're actually way more predictable, and Jim alluded to that too. They're more predictable than we thought and we can start to create a science of kind of statistically not perfectly predictable behavior and we can model that and we can start to, I believe, predict the future in the way that kind of Isaac Asimov's science fiction stories. Foundation series talked about social statistical mechanics. I think we can predict a lot of where society's going.
Brian "Ponch" Rivera:You know, we've been looking at sacred geometry to understand patterns in the universe and even patterns in the market. So there's some. I think there's something there too, and that's a paradigm shift, because you look at how people think about you know how the pyramids were built or anything like that. They don't think about the dimensions of that, and maybe there's some stored information that we're missing from way back thousands of years ago that we could actually uncover through different things like sacred geometry, which is an entirely different show here. Talking about that paradigm shift, you talked about phase transitions.
Bobby Azarian, PhD:Yeah, those civilizations were around for so long and they didn't have technology, but they were trying to solve problems, so they discovered other algorithms and stuff. Basically. So, yeah, there's lots of meaningful knowledge there. One quick thing to say, though, about this thing of like it's things are predictable. It's tricky though. There's kind of an uncertainty principle, like from quantum mechanics, but at the, you know, at our scale that's not the same, but it's the same idea that we can kind of predict things. But when you put that prediction out there, like I say, okay, society, like, if we like that's the point when I make a prediction, I can see like society's going towards a problem with AI, like we need to do more, have more awareness, for example, maybe a big problem. But so when I make that prediction and if I publicize that and the world becomes aware, that will change the dynamics such that my prediction might not be valid anymore.
Bobby Azarian, PhD:The actual prediction can cause a movement that solves the problem that I'm saying inevitably will lead us to doom. So there's just this because reality isn't determined, that's what the book argues. There are real counterfactuals, there are real possibilities. It changes everything without we think about it too, because it's very tricky when we say, oh, this thing, this future is inevitable. It's not. It's inevitable if we don't act. Bobby, I wanna ask you this this is interesting.
Brian "Ponch" Rivera:I never thought about this, but if you say something on you know, like on this podcast, write something down, put it something on social media, you're actually or publish something, you're emitting an action that interacts with the environment, right, therefore, could change the environment. Because that's the whole idea between the perception action loop or action perception loop is we perceive the external world and we act on it to potentially change it. Is that what you're saying?
Bobby Azarian, PhD:Yes, I think that we need to think in these terms and we don't, but I think it's useful. So people are talking more about causality now and philosophers talk about causal chains. And the reason that's big is because, you know, there was an idea that everything was determined. And if everything was determined in this strict sense, at the atomic level, then it seemed to imply that we had no agency, that our decisions were predetermined, that we're not even really making decisions, and I think that's wrong, in the extreme form at least. And so people started trying to talk about like, okay, so agents initiate these chains of cause and we actually make actions that have this real effect on the world. That's real and we need to think about causal chains at the social level. So there are social causal chains.
Bobby Azarian, PhD:So this podcast reaches people, the information propagates and you can see, you can map that like statistically, you can look at the effects and then that effect every agent it reaches it's potentially updating their model and that change in their model. They could listen to it and not change any of their beliefs. They could go, that's pretty interesting, but then forget it three days later and it just has no change on behavior. But if it actually changes their behavior because they go oh, that one idea I like that. That kind of fits in with how I think I didn't normally think that, but you know I'm gonna adopt that idea there is a change in their behavior that is measurable, such that if we convince people, for example, that squid and octopus you know, they learn that they're really intelligent. They didn't know that because we have no reason to know that, don't eat them.
Bobby Azarian, PhD:Yeah, and so you know, let's say we talk about that and then people hear it and then they go to their favorite restaurant and then maybe they don't not eat calamari but they opt for the popcorn shrimp appetizer over the calamari. And that is really changing the statistical behavior of people. And when it's a big message and like it's really needed and it goes viral, that's immediately creating these social causal chains which have all these feedback loops. So like it's updating models and the collective model, like you can do that like very quickly in the viral age. And so the OOTA loop did I say that right, oota? Yeah, you said right, oota loop. Yeah, it extends to the environment and we have to think about, you know, these loops at the individual level, but also the loops that are being created at the collective level.
Brian "Ponch" Rivera:All right, oh gosh, so much to pull from on here. Let's try this. Let's continue building up on orientation, that internal model that determines how we sense, decide and act, and you touched on that a little bit. To me, a prediction is a decision. When we predict something, it's a decision being made and we get feedback loops. From that we can actually act in a way that provides a counterfactual. We can hold something in our mind, we can come up with a plan or policy and once we emit that action, once we post something on social media, create a podcast, write a book, do something to change the external environment, that's gonna create some type of new information for us. And that new information I'm gonna dive into information theory and ask you the difference between entropy, between thermal entropy and informational entropy, because I think there is a difference. And then, why is this important to our listeners?
Brian "Ponch" Rivera:When John Boyd created the energy maneuverability theory, he was struggling with the concept of entropy, all right. And this is when he was at the Georgia Tech years and years and years ago and he realized that entropy, thermal entropy, was critical in understanding the performance of fighter aircraft. That shifted over the years. He went more towards Shannon entropy to understand how information for biological beings, if you will, determines how we sense, decide and act, and that's why I put new information inside of orientation. So, bobby, can you walk us through a little bit behind the second law of thermodynamics, thermal entropy and the difference between informational entropy, which Shana gave us, because I think this is kind of important.
Bobby Azarian, PhD:Yeah. So I think there are kind of like three distinctions to be made between different types of entropy, because the first kind of phase of people talking about entropy they were kind of talking about something different than disorder. So it came from without going into too much detail. It came from trying to understand how to make engines, heat engines more, if steam engines more efficient. And basically, when you heat something up, so basically how a steam engine works, is you create a heat gradient. So like you create all this heat, so like you can like a simple version would let's say, you have a chamber, this little system that has a gas or a liquid in it, and you burn some coal and you heat it up and that heat cause. Basically, when you have an area in reality of like, where there's like a lot of heat and then there's a cold area, there's a gradient and so basically whenever there's a difference, like that heat wants to flow. Just like you have like water, like if you have like a sink and there's two like tubs, if you open the wall, water will flow from the one that's full to the empty. One same thing with heat. So then you can exploit that tendency for things to flow along that gradient. By, for example, heating something up, it causes that gas or liquid to expand and that creates this motive force that can be used to accelerate like a train, and then you have to cool it, though, and in the cooling it brings, condenses it again and then you repeat it as a cycle and then that can like turn, you know, like wheels or whatever can do some mechanical force, just like a windmill. So really what they realized was that conversion of heat energy into mechanical energy wasn't 100% efficient. There was always some energy loss because basically that that energy that's kind of organized when it's in this form of heat, when it gets converted into mechanical energy, the energy basically gets spread out and there's a release of heat into the environment, and that creates the first kind of concept of entropy was dissipated energy, usable energy, and it's still energy afterwards, but it goes from usable to unusable.
Bobby Azarian, PhD:So entropy was a measure of the amount of energy in a system that's no longer available to do work, to do mechanical work, and so it wasn't about order or disorder, it was really about energy and the fact that the whole, like the big idea that they came to, was like at some point when you know, all of this energy is converted into entropy, we're not going to have any more energy. And then you know life, you know things in general, like you know mechanical things too, like everything would just kind of run out of steam and kind of come to this state of you know, stasis. And then later this guy, boltzmann, was trying to understand entropy from a statistical basis and understood that energy, really how energetic something is, has to do with how fast its molecules are moving at the molecular level. And then there was, you know, so the ideas are related, and but really we were talking about concentrated energy, you know, in the first phase of this concept being spread out into disordered energy. And then Boltzmann showed that you can basically talk about any physical system that's like a material system, its components becoming more spread out and less correlated and less organized, as becoming more entropic. And so then we had the idea of entropy as disorder.
Bobby Azarian, PhD:And then you can think about that kind of entropy in terms of information, and this is where it gets a bit complicated. But basically, if you have a system where everything is kind of disorderly, there's way more ways for that system to be arranged, to have the same collective energy in the system as well, where there's just lots of different ways for a system to be arranged its molecules but have the same total energy. But there's more ways for that to have an equivalent arrangement at the same energy amount collective energy if the systems disorderly. So ordered states have low entropy because there's a low number of ways it can be configured equivalently and disordered states have a lot of ways that they can be arranged equivalently and people leave that equivalently thing out. They just go, oh, disordered stuff, there's a lot of arrangements and like, what are you talking about? Like didn't really make sense, but like. So you have to understand there's a lot of different arrangements that have the same total energy of the system. But so then how that's connected to information they realized like.
Bobby Azarian, PhD:So basically, the idea is, if you have a very disordered system, if you somehow were to able to see the exact state that all the molecules were in, you would be getting more information. If the system is a disordered system because there's all these other alternatives that it could be, so when you observe the actual state, you're collapsing all of this ignorance that you had over the actual state, because disordered states can have many more states they could be in, you know, micro states they're called, that are equivalent, correspond to one similar macro state, which is like the collective energy measure of all the little things, that all these different energies. And then so Shannon's information theory was similar because, basically, shannon developed the idea independently, he wasn't even caught entropy, but, for example, he was trying to understand when you, when you observe something like you, get a message, how much information is in that message. And so, to make a simplest model, is like you're trying to understand the state of something, so, like when you flip a coin, there's uncertainty, there's ignorance about, before you observe it, it could be heads or tails. If you don't know what the state is, if it's encrypted, like what the state is, you don't know. If it's heads or tails, when you observe it you get one bit of information because you collapse two possible states If you have a dice.
Bobby Azarian, PhD:So if you roll like a die that has six states, so you have even more ignorance about the state that it might be in at any given time. And that when you observe that state, you get even more bits of information because there were six states that were collapsed. And if you roll, two die. So if you have a pair of dice, it's even more information that you get because you're collapsing this. You know big space of combinatorics of possible things that you could roll.
Bobby Azarian, PhD:So the amount of information that we get from an observation is related to the amount of alternatives that are ruled out by what you're observing and I. That's really important to consciousness because it suggests that the, how informative our single conscious experience is in a moment is a function of how many states our mind can access that it's not accessing at the moment. So a simple organism like an ant or an amoeba having a conscious, like, if they can have an experience, their momentary experience is going to be much less informative than our experience. Because how informative an experience is is intimately related, mathematically related, to the bandwidth of your, your, your brain, basically how many states you can access. This is so, for example, if you, if you only know black and white, if you can only make a distinction between black and white, your work, your, when you have an experience, you're only. That's the only distinction you're making. You're not aware of shapes, you're not aware of colors, you're not. You're not, you're only aware like, oh, if something is, you know not white, then it's black, and like you don't know anything about that, you just know it's not white, you don't know if it's dark or gray. I mean, you don't know if it's dark in a circle shape or gray. So if you have that logic, then the more things that you could be aware of, like basically like yeah, if you can only experience two states, you're not gonna have a very informative experience. Oh, you know, when you do have that experience, it's only gonna be what you understand is relative to kind of this space of counterfactuals I guess that's the best way to put it. So the more counterfactuals that you can imagine, the more prepared you will be, the more informative your you know momentary analysis will be.
Bobby Azarian, PhD:And that's something we're not doing talking about counterfactuals and talking about like this process, like the Udallup and stuff.
Bobby Azarian, PhD:So we do that. Naturally, we think about, you know, we try to have this decision-making process, but we rarely ask ourselves is the decision that I'm making based on a belief system or model that might have errors in it that's leading me to make a bad decision? So let's say you have the Udallup and you're like I got the Udallup, I got everything solved, like this is right. And then you present this system to your group and they're like, yeah, well, we tried this out and shit's not working Like this. We got a little problem here. And you're like no, and they want to question you Like, no, don't question the Udallup, the Udallup's right, it's been tested. But then you might find out we need nested Udallups or a model of intropic Udallups, and we will always need those models. But we make the mistake of being certain about our theories when they're good and scientists do this more than anyone and you'll see people like Brian Green and Sean Carroll saying what I think are completely irresponsible stuff, because they're constantly overstating their certainty.
Brian "Ponch" Rivera:Yeah, so this when you're talking about this variety in the brain hopefully are you connecting it back to Ashby's requisite variety If we don't have those different states that we can't see the world. I forgot how you put it, but if you just have a few, you really can't experience the world in a different way as somebody else, who or some other biological organism that may have more variety in it.
Bobby Azarian, PhD:Is that what you're talking about? Yeah, and not many people make that connection between that cybernetics principle from Ross Ashby from maybe like 1950 or something, and this stuff that I'm talking about, like counterfactual, like it's just so. Yeah, I guess what I'm saying is the law of requisite variety can be applied to lots of practical situations, and we're not thinking about this, and we really should be so, for specifically, cybernetics says this and it kind of created the logic that the free energy principle, these new neuroscience theories coming out, are based on. So Ashby came up with the good regulator theorem and the law of requisite variety, and that's a lot to throw out, but let's just simplify those things and go through them quickly and not worry too much about the terminology. But the good regulator theorem just said any system that wants to control or regulate another system, and it's not even just that, if an organism wants to survive in its environment, it has to regulate or control its environment in some way, it has to do what it needs to do to survive. So it applies to the biology as well. But it basically says that the system has to have a model of that system and so apply to organisms. That says organisms are a kind of model of their environment. Now the law of requisite variety says okay, we accept that organisms have to model the environment, a system that is a controller of another system. If you're building a system that's like an artificial system that's gonna regulate other subsystems, it has to contain a model of those systems. And so this has become a big part of systems engineering and like this is optimal. There's like theories now based on this, but like optimal control theory, and control theory come from these cybernetics principles. But the law of requisite variety says the systems model has to have as many states as there are ways that the environment can surprise it if it's going to be adaptive. So if your model has less states, then the complexity of your niche, then your model is gonna be insufficient and you're gonna be making errors. You're not gonna be living optimally. However, there's an important lesson here. So, okay, yeah, so basically, you can take the law of requisite variety and you can think about these really good principles that people should have, like this Francis Heilegen of cybernetics has said because of this principle, it means that society as a whole should try to be increasing the number of states that they can respond to, because if we have some sort of existential threat or crisis, we're in a better position to respond, right?
Bobby Azarian, PhD:So I think as a collective, we do wanna slowly be increasing the amount of states and really that we're just. I'm just saying it's kind of obscure to say that when you're talking about a collective rather than a brain, because what do you mean? The amount of states where the states encoded but I'm really talking about, like, the knowledge that we have collectively. We need to increase that and that increases kind of the collective intelligence, the things that the collective is aware of. We're not only thinking about collective intelligence, so we need to think about that At the individual level, though, you can be just fine with a simple model, and sometimes a simple model will be better than an overly complicated model.
Bobby Azarian, PhD:And if your environment is simple for example, if you live in a rural community with just a certain, you know simple social structure Roger Penrose, like a theoretical physicist with a Nobel Prize who's known for being a disorganized thinker, he's kind of all over the place. He might be the town crazy person, the town loon, if you put him in that place, because no one's gonna understand him and a model like a Christian based model of a 10 commandments might lead to someone being way more successful. Because if you try to throw them all the stuff about entropy and there's no context, there's no narrative to really show you what these things are telling us it might not be as effective as a simple religion and that's why religion, self-help systems, they stick around. Sometimes they're not even based on like fact but they're giving you an adaptive principle that is factually like helpful, like we should, you know, use those. We're missing out a lot by not using certain systems like this because we consider them to be like woo or anti-scientific. It's completely wrong, like we're so much from that religion and self-help and spiritual practices have developed that we need to adapt.
Bobby Azarian, PhD:But so my point is, when we wanna think about how complex our model needs to be, we need to think about the niche, because you wanna match the complexity of your model to the complexity of the niche and you could be doing an injustice to yourself, making life harder by having an overly complicated model, because you're not simplifying, you're not distilling the life principles that we need to have an effective strategy. However, I will say, if you're one of these people who are willing to take on a more complex model, like you're curious for knowledge, that that strategy will give you a benefit if you wanna seek that out, because there's always a new frontier for people, there's a new niche that can be created for people who wanna take on something more complicated. You can start new fields, you can start new businesses, and so that is the reason that we should always be curious. Also, we don't know if our simple model it's always gonna have errors, so we should always even if we wanna have a simple worldview like we should still always be mindful that every model has uncertainty, and that's been called, like the principle of incomplete knowledge. I think like it's the biggest thing.
Bobby Azarian, PhD:It's like a simplest principle, but no one has really stated it. We're all ignorant, all of our models are ignorant, and we have to be aware of that. In the moment we're not aware of that. We can be the smartest people in the world, these physicists, but you will make mistakes. Every theory has been shown, whether it's Newton's physics or general relativity or quantum mechanics. There's something that they're not explaining. So it's a big mistake to arrive at a new theory that explains a lot more, but to think that this is the final theory.
Brian "Ponch" Rivera:Bobby, you brought up flow earlier in the context of entropy, free energy principle, the external environments and the way we kinda think about this is, if you're going to update your internal model of the external world, you have to continue or allow the flow of information to come into your system, your personal system, if you're talking about the individual, your team, your organization, and through that information you can potentially create a better world map, if you will, or a better orientation of the external environment. This is why we talk about cognitive diversity so much in teams. How do you leverage that? How we use red teaming techniques, complex facilitation techniques, but again, cognitive biases, mapping, situation awareness and things like that. I do wanna come back to the connection of free energy, new information or information and entropy. I think in your book you talk about free energy equals. What is it? Entropy?
Bobby Azarian, PhD:So free energy is used in two ways. And there's the free energy principle by Carl Friston. That's not talking about literal, thermodynamic free energy. When I say free energy and it's just a confusing term there's just like a bag of worms. It opens because Tesla talked about free energy and then people didn't understand what he was saying and I'm not even sure what he was saying as far as this point goes. But basically that became an idea. That kind of became a pseudoscience idea, but I think that idea. So basically you had an idea that you could get extract usable energy from the atmosphere, like from the environment. That was just all out there and actually that could potentially be true.
Bobby Azarian, PhD:It's a lot to get into, but the way it was phrased in this kind of naive, superficial way seemed to go against the laws of thermodynamics and so people, anytime they heard that word free energy, they'd be like whoo, what are you talking about? That's stupid. But then there was this free energy was basically in physics and thermodynamics it's referring to the energy that hasn't been dissipated and spread out. So when energy is concentrated you can extract it. And free energy is energy that can be used to extract, work, so, like we have, the sun is a big source of energy but it's raiding out its energy and that it's dissipating that energy and it's losing it and it's converting it into entropy. We use that energy, we release body heat, we're converting that usable energy into unusable energy. So that's one source of entropy that I call thermal entropy, because it's specifically energy that's been extracted from like a heat source and then we dissipated it as heat and that's not really entropy. In the same way, when a house crumbles and you look at that remnants, it is a disordered form, it's increased entropy, but it's that statistical entropy of Boltzmann. But so free energy is energy. It's kind of the opposite of entropy in this context.
Bobby Azarian, PhD:So, carl Friston though basically artificial intelligence, people started using that as a metaphor. So you have this free energy which creates this energy gradient where you have this like concentrated energy and basically when organisms use that real free energy, they collapse that gradient and basically when that gradient's collapsed and that energy flows into the organism, when the energy gets spread out, then it's technically entropy. So this free energy gradient is being minimized, and so in AI they're talking about minimizing free energy. But the gradient is this prediction error gradient. So you're trying to reduce. So it's kind of confusing that they use that term. But so basically they're just. When they say an organism needs to minimize its free energy, they're just saying it needs to minimize the difference between its model and actual reality. So you're trying to minimize that error.
Bobby Azarian, PhD:But what I say in the book, what I point out, is that because the Bayesian brain, it's the free energy principle and the Bayesian brain principle and the reason there's two names the Bayesian brain hypothesis was a neuroscience theory that said organisms to survive need to minimize their model's prediction error. But then they realized that this applies to organisms that don't have brains too. So free energy be principle is just more inclusive. So but the way the theory is framed, it's based on this second law of thermodynamics narrative. It says there's a tendency towards disorder and to evade that tendency, organisms need to survive and they need to minimize their model's prediction error.
Bobby Azarian, PhD:However, what I point out in the book is that the more detailed version of the story is that organisms, any ordered system, has a tendency to fall apart. To evade that tendency to fall apart, they have to get real free energy. They have to get usable energy from the environment. If they can find that energy, they can have the energy needed to do this work against this tendency towards disorder, and they can stay in existence. To find that energy, though, then they have to model the world and get information, and then they have to minimize their model's prediction error, that information, theoretic free energy, and so minimizing the abstract version of free energy, what's called information, theoretic free energy, minimizing your model's prediction error, is actually what helps you be able to get that real free energy from the environment, and you're minimizing literal free energy. So, yeah, there's a story. For life to exist against the tendency towards disorder described by the second law, it has to find energy. To do that, it has to get information, it has to model the world and it has to keep refining its model.
Brian "Ponch" Rivera:Is that the?
Bobby Azarian, PhD:constructal law. I connected it to the constructal law. But yeah, it is the constructal law because that creates flow, because basically, when the organisms are looking for all this energy, the sun is giving life energy and that's what pushes this organization to form. And then the order forms and then it keeps trying to get more energy to keep its existence, to maintain its existence, and then it's basically like life is like sucking the energy out of the world. And the world has all these stars which are basically like batteries in the sky. We're unlocking new energy sources, so we're getting nuclear energy, and like it's basically facilitating flow into this island of knowledge which is the network of life, and the island of knowledge keeps expanding because it's sucking that energy out of the world. So you get this sort of constructal law where, like minimizing your model's prediction error, that free energy maximizes this flow in nature and you get a story of flow basically facilitating the increase of complexity in order, yeah.
Brian "Ponch" Rivera:Yeah, so we're big fans of Flow, right?
Bobby Azarian, PhD:So we talk about Flow quite a bit, and yeah, and then there are Flow states which are a bit different, but I think the concepts are related. I think they're related because I saw Carhart Harris.
Brian "Ponch" Rivera:Robin Carhart Harris comment a lot on your book recently and for our listeners who may or may not know, robin Carhart Harris is probably one of the. He's the thought leaders in the space, but when it comes to psychedelic, it's just therapies.
Bobby Azarian, PhD:Definitely.
Brian "Ponch" Rivera:He actually he borrows from Carl Friston's free energy principle and created the entropic brain hypothesis, I believe with Carl Friston.
Bobby Azarian, PhD:Yeah, yeah, they worked together on that. Yeah, because they were at in London together. Now now, robinson, yeah, go out by the say yeah, he's one of all that psychedelics research. When people hear like psychedelics, neuroimaging studies are showing this, like dissolves the ego, like he's behind most of those studies.
Brian "Ponch" Rivera:Yeah, yeah. So that's I mean for going back to construct a lot, and what we've been talking about, even with the OODA loop, it's really about how do you increase the flow of a currency In this case it could be information through the design of the system which we've talked about, minimizing prediction errors. So you do that through design, which Bayesian talks about as being a noun, not necessarily a verb. Right, you don't design it, it's a noun. So what we see is the connection between the construct, the law call, for instance, free energy principle, john Boyd's work at the OODA loop and you get into the constructor theory as well. Substrates, all these other things you're starting to come together and come to. They're all popping up independently, if you will. Yeah, collectively they make helping everybody make sense of the world, and I think that's what you're doing with a lot of your work is bringing it together. Is that correct?
Bobby Azarian, PhD:That's mainly what I'm trying to do is synthesize it into one large picture. The thing that's novel about it is, when you synthesize those things, I think you get a view of a purposeful universe, of a universe moving toward a goal state, an increasingly complex state, an maximally integrated computational state, maybe a fully conscious state. And so not everybody is automatically gonna get on board with that picture, because that's kind of what that was like. One of the kind of big pictures of scientists and philosophers in the 19th century fell out of fashion for some ideological reasons and political reasons, and so people, a lot of people, are under the assumption that that has been debunked.
Bobby Azarian, PhD:This idea that the universe is moving towards has this kind of teleological, purposeful trajectory, and I think it's the biggest mistake we ever made. It's kind of blinding us to this whole purposeful aspect of nature and life. But you can't talk about those theories without getting into the other ones. And one thing I've noticed is that the people creating these theories I love them and they're doing important work, but they don't like to talk about other people's theories, they don't like to integrate that work and they have their careers and they're trying to really champion their theory and that's fine, but somebody's gotta do it.
Brian "Ponch" Rivera:So that's what the book was doing, was trying to integrate for example and I think that's the connection between you and John Boyd, right there is. John Boyd wasn't trying to push his theory of something. He was bringing it all together, synthesizing it.
Bobby Azarian, PhD:And you're doing it too, and that's awesome.
Brian "Ponch" Rivera:So when you read your book, I mean that's why I'm like this. Is John Boyd writing this, if you will? I mean he's bringing synthesizing. Or, in the spirit of John Boyd, he wasn't trying to come up with one universal model that says look what I did. He's standing on the shoulders of giants and taking that information and synthesizing it in a way to make it consumable. And the problem that we've seen with John Boyd's oodle-loop over the years is it's been reduced down to observe, orient, decide, act. It's not a active process with the external environment, it's a. They show it as a passive one, and we know that we humans do not passively interact with our environment. Right, we actively engage with it and that's what we just talked about.
Bobby Azarian, PhD:Yeah, I mean the loop. You know, an ideal form would probably want to have these different variations where something can go wrong in that loop, and then there's the decision tree where there could be possible ways to handle this, if some. So, yeah, I think these integrated approaches are great, and then we got to take them and then constantly refine those as well. It's just an ongoing process that we'll never end, but I think we're in a really exciting time because the connections that we're making, it's bringing together, it's stitching together a theory of everything, and we never had this kind of ever.
Bobby Azarian, PhD:All of the so-called theories of everything were fundamental physics theories that ignored life and ignored consciousness in mind, and I think that was a big mistake in culture and civilization. And a true theory of everything is going to be a non-reductive theory of everything, a theory that includes emergent phenomena, and that's what my book was kind of trying to just at least establish a general outline for. But yeah, then the next step is okay, we understand these things, let's apply them to every aspect of life, and so the-.
Brian "Ponch" Rivera:And that's key, right. Yeah, these loops, yeah, they need to be applied to government. Yeah, yeah, yeah. So you also dive into complex adaptive systems and I've noticed in the last couple of years many of the neuroscientists some that we just I brought up on the podcast here are diving into complex adaptive systems. And one point in your book you start talking about attractors in there, and I've learned a lot of this from Dave Snowden through his Cadevan framework and working with him over the years and understanding that you have these I'm gonna get this wrong these attractor basins within a I forgot the name a fitness landscape, right, and this gets into like affordances and things like that. So if you start thinking about attractors and hopefully you can help me explain this a little bit better on this podcast why are they important? When we start talking about as this universal model of almost everything?
Bobby Azarian, PhD:Yeah, attractors are a really important concept. Like you know, it came from chaos theory and kind of complex systems theory, but it's, these attractors help us understand everything about everyday life, like everything, like really goes. You know, without understanding this component of the explanation, we're completely clueless about everything. But so to start with the simple stuff, with attractors, like basically you have a, you know, any system can be analyzed in this way where you're looking at all the possible trajectories for the ways that system can evolve or develop, and so that lays out kind of a map that's called like a state space or a phase space, and so when you want to understand a system's behavior, you want to understand, like, the trajectory of that system through its state space. Now, when you have a gas, of all these molecules that are just moving around randomly, so there's no statistical correlations between gas molecules, if you have just like a you know common gas in a box, they're all just kind of moving around freely, kind of what we think of as like brownie and motion type, you know statistical free floating, you know, and there's, like you know, it's kind of like an entropic state because things aren't correlated If atoms become bonded, if they start interacting, and they start, you know molecules start condensing. That would be a decrease in statistical entropy, but anyway. So the point I was making was that a gas explores the state space pretty much randomly, so its trajectory, it could go anywhere, and that's called an ergodic system. It kind of randomly explores every possibility given enough time.
Bobby Azarian, PhD:But systems that are these non-equilibrium systems that you know, most of the systems in the world, like in nature, that we've interact with, are these open systems that can be pushed by a flow of energy. So let's say, well, that's how these systems get started. You have, like, energy flowing through a system and, for example, if you heat a pan of water at a certain temperature before boiling, you'll start to see these convection cells form, called bain-yard cells, and that's because the energy is flowing through the system and the system is trying to dissipate that energy and, like, these flow paths perform because they're the paths of least resistance for heat flow. And then you get these organized structures, but anyway. So systems being pushed by a flow of energy will often organize themselves if it's in the right way. You just can't just blast it with energy that can destroy the system, but in the right way energy flowing through a system organizes that system. That's kind of part of the construct of law. And so when you have a system being pushed by this flow of energy, because of the molecules start interacting and because molecules do have these bonding forces, these dynamics, they're not just like metallic, like marbles, like marbles, they don't interact. So you can just have a bag of marbles and mix them up and you're never gonna get order. So there's something about nature where there's these forces that allow things to come together, which is pretty neat.
Bobby Azarian, PhD:But these ordered systems, systems that are being pushed far from this state of disorder and that form these correlations, when the components are interacting and becoming connected, causally connected, then basically those kinds of systems move towards a specific region of the state, space. They move towards basically an organized structure and a structure that actually is good at maintaining its existence. It's good at extracting more energy. So really and people really haven't focused on this much but the state that it's actually kind of being forced towards and that's why it's called an attractor it's like if you have like a system being pushed far from equilibrium, which is a state of maximum disorder, everything's like spread out and randomly moving around. So if things become correlated, basically those correlations represent information in the system.
Bobby Azarian, PhD:Systems start becoming predictive of other systems because they're causally, they're connected, and systems. These systems move towards attractors, they move towards organized states. But they're specifically what I was getting at was their states that encode the world. So these attractors represent, it seems to represent a state where the system has a map of the world and then of itself. So these attractors are even more unique than we thought when we just said oh, they're going towards an ordered region in the state space. It's not just that, they're going towards a region that's this weird recursive region where the system has start to build a model that reflects something external or reflects its own structure, and so systems naturally move towards that.
Bobby Azarian, PhD:Gravity creates an attractive force. So we get the formation of planetary bodies and solar systems, and then we get the emergence of life, because energy is flowing through systems and you start to get these chemical systems that are these self-regulating systems, these called auto-poetic systems that are self-maintaining, and then you have a physical system that is a living loop, and so living systems are loops and they have levels, they're nested. So in the book I try to say we can understand reality in terms of like loops and levels and we don't really think in those terms with fundamental physics. But, like, an organism is a thermodynamic loop, because it always has to be taking in energy from the environment and then it's using that energy to build more biomolecules and then it's releasing that energy in a disordered form and that's an energetic loop. And metabolism is literally a cycle. It's a Krebs cycle. So there's loops in that aspect. But there's also these informational loops where you're updating your model, that are also related to that energetic loop. So living things are loops.
Bobby Azarian, PhD:So it's not just that you know, you have these physical systems where you have these attractors and you have these organized systems like whirlpools and tornadoes are popular inanimate examples of this. But living systems as well are attractors and we're special types because we maintain that spot in the attractor space. We have knowledge that allows us to correct our errors and keep that position and every it's even more than that. So when I say it's part of our life, carl Friston has talked about all of our behavioral patterns.
Bobby Azarian, PhD:We get up, we eat breakfast, we eat lunch and dinner, we watch TV at, you know, routine times. We, you know, might, you know have sex at routine times. So we have all of these attracting sets. They're like loops that are basically things that we need to do to survive, that have been wired in through this reward network where we get dopamine. Every time, you know, we do something that our brain believes is adaptive, and so everything about what we do is a pattern and a loop, and so when we're updating our model what that's doing, you know we think about it in you know this mental way. Oh, updating our model. But when we have that mental change in our belief structure, then that changes the attractor too, so our behavioral patterns change. So we wanna try to carve out new attractors by having a model that's aware of its own ignorance, and we're looking for errors all the time.
Brian "Ponch" Rivera:So on the attractors. It's a complex, adaptive system. We talk about it adjacent possibles, and when we work with organizations we tell them in a complex context, you cannot define a future state. You can't go, hey, this is what we wanna look like in the future and work backwards from that. You can do that in an ordered system somewhat. So this is a big shift in the way we think.
Brian "Ponch" Rivera:Now, when you think about social systems and you think about what's going on the environment today, you gotta look at the adjacent possibles, the attractors out there, and then find out what's possible. Now. And I think what's happening in our social systems is we have some internal models. It doesn't matter if it's on the left side of spectrum, right side of spectrum, in the middle of the political spectrum, where it may be. They have internal models that they want the external world to be, but it's not an adjacent possible. So the environment pushes back on them right on that. It's possible, but not right now. Is this the type of thing you're thinking about when you talk about how you can apply your work towards social systems?
Bobby Azarian, PhD:Absolutely, absolutely so. Yeah, lots of things to say there, let me okay. So I have two things in mind. One is about the adjacent possible, related to things that I said earlier, so let's come back to that.
Bobby Azarian, PhD:The first thing I wanted to say is, yeah, the problem is they have these rigid ideologies that may have worked for a certain amount of time or they may have provided some part, some solutions to potential problems, and I would argue that all of these ideologies, whether it's like conservative ideology or progressive ideology, or capitalist, socialist ideology, they all have useful things here for us to extract and use, and being divided is shutting everybody off to potential solutions that could provide an adapted form of their ideology. So that's exactly with, like FDR, we're a capitalist nation, we flourish under capitalism, but all of those social programs, like we're super helpful to America and everybody adopted them and for a while it was like, understood that, like, okay, we needed those social programs and that became part of what America was about. But in these highly polarized times, if anyone presented those, they'd be like oh, that's socialism, communism, that's the end of the world. And we're seeing the same thing with how people talk about capitalism, who are against it. They're just like oh, it's the end of the world, we need to throw out capitalism. I think those are all mistakes. All of these systems have utility and came, you know, emerged because they had adaptive qualities, but they're all incomplete, they're all works in progress and what you said gets at why.
Bobby Azarian, PhD:I believe that this narrative of that my book is pushing that I said not everyone has adopted is incredibly important because, if you see that cosmic evolution is this kind of predetermined process, it's not determined in its details, like you can't determine what agents are gonna do, there's a lot of uncertainty, but because of these attractors, there is a sort of global determinism where things converge towards certain outcomes. Basically, I'm saying that this is what, that the process of evolution is a process of parts coming together to make greater holes, and so subatomic particles coming together to make atoms, which come together to make molecules which came together to make cells, which came together to make multicellular organisms which form societies. And now the biosphere is the planetary level network and humans are forming something like a global brain on top of the planetary network, which people have called Gaia, which is a cybernetic, self-regulating system. That's all the Gaia theory was at the beginning. It was, yeah, james Lovelock, the Gaia guy was inspired by cybernetics and it was his neighbor. Lord of the Flies author was his neighbor that said, call it Gaia, but it was really a cybernetics theory of the biosphere being a self-regulating system.
Bobby Azarian, PhD:So, if this story is true, this series of attractors is our future and this global brain emerging and this global coordination. This next stage is going to happen because all of these information channels are opening up, opening up all these transportation channels, like we can't stop the connection, the merging of these different cultures, and that creates a problem because we all have different worldviews, and so now we're going to need a unifying worldview to deal with the chaos caused by these previously isolated ideologies coming into contact with each other. And so what I'm saying is, when you see this process, you not only become aware of the attractors that you're in and the attractors of the past and this process of each paradigm being an attractor. Basically, when we have revolutions and governance systems or scientific paradigms, those paradigms are part of the emerging attractor. Like the paradigm is what introduces the stability to the social system, it's the belief system that's aligning everybody's interest. So belief systems are good, they bind us together and that's what created civilization. I think religion in some form, some form of spirituality, was needed. Ideology was needed for the emergence of civilization.
Bobby Azarian, PhD:The problem is the very thing that binds us together, because there's these ones popping up in different places of the world that are going to be different when we start coming into contact. They're the same thing that divides us into tribes, because the things that are making us come together and making us feel good they're different stories, and so it's like organisms are coming into contact. They're gonna compete until they align interests. So we have to find a unifying worldview. But what I'm saying is there's specific attractors that are there in a sort of platonic, abstract sense, and when we understand that, then we see the solution and that adjacent possible is already in some way kind of set in stone. We don't know, you're right, we don't know the characteristics of it. But I'm actually saying that we can know more about it, we can start to make statements about it that minimize our uncertainty. Like we can say we need like a global paradigm, like a unifying paradigm that integrates all these things. Like that's a prediction we can make about it.
Bobby Azarian, PhD:But those people can't see it. And without that systems thinking lens and the cybernetics lens of like loops and stuff and adapting evolutionary principles. They are completely blind about the phase that's coming and all of those worldviews are obsolete. Like we need this. There needs to be a giant movement. They're like I think it's gonna happen either this election or the next one. It might not be ready, but like to fully like crest. But what I believe is that right now the conditions are creating a need that these kinds of conversations that we're having, they're going to go viral at some point. They need to be packaged in the right way because they are the solution and we're kind of self selecting ourselves to kind of be in this discussion by talking about this, because it's not just another thing that could or could happen. I'm saying the evolutionary process supports this discussion that we're having. This paradigm shift is predictable. It's kind of part of the cosmic evolution.
Brian "Ponch" Rivera:Yeah, I agree with you 100%. These platforms are ability to connect with other folks Not necessarily like minded, but like purpose people is essential to change the future, to actually shape it. Hey, bobby, I wanna turn it over to you to see if you have any ideas to share with our guests or any insights with what you're doing. Next, if you're writing a new book, what's coming next for you and where can our listeners find you? And, by the way, before we go today, I wanna make sure you have an open invitation to come back. We could probably spend another five, six hours on this, on a lot of these topics, and there's so many things we didn't get to share.
Bobby Azarian, PhD:I would love to come back and like talk about like practical stuff maybe, like specific problems in like see if we can take some of these models and look at them and see how we can adapt them, because I think it's an exercise that everybody needs to do. I kind of wanna see this thing where, like, society just radically models everything, because then we can start to understand these things that we're completely clueless about. And if our model's wrong, we also know that, like some politicians, like, oh, let's do this system, let's try to attack these problems in this way. But if you're not doing like simulations and comparing that to what we see in the real world, you don't even know if your model's accurate or not. You may need to throw out that model. That model just may just not have correspond to reality. So yeah, as far as what I'm doing right now so the book came out a year ago. I'm working on a second book, but really, what I wanna do?
Bobby Azarian, PhD:The first book was written with an academic audience in mind. I wanted to make an argument for the evolution of the universe being this process of hierarchical self-organization that involves life and that life has a purpose and it's basically helping the universe wake up or come to life, because basically life is physical systems that were once inanimate, modeling the world and becoming aware of the external world and then gaining consciousness through world modeling and self-modeling. It kind of creates this virtual simulation of the world that allows for experience and I'm saying we're part of a larger process that's going to subsume the entire cosmos. That's at least the theory that I'm putting out there and it doesn't go against the second law of thermodynamics and I feel pretty confident saying that at this point. But so what I wanted to change, or what I wasn't fully happy with, is because it's in this academic language, it's not clear how these problems can be applied and it's not condensed enough to where average people find it meaningful, and that's really my motivation for writing the book. I think this is the problem to these solutions, whether they're these real practical problems that we're talking about with society or just personally.
Bobby Azarian, PhD:A lot of people who are rational thinkers. They wanna be like updated with our current understanding of the world to guide their kind of understanding of themselves and their purpose and their spirituality, or whether they should have spirituality even and I think we have the completely wrong narrative with the reductionist model of science and people need to, if they become aware of that, it can change everything in life, cause I know it did for me at least, like it kind of infused my life with a purpose that it didn't have when I believed in determinism. So Roto Omega is my sub-stack and I'm basically trying to take all of the ideas from the book and new ones that weren't really discussed in the book but applications and put those into accessible posts. Have a place where I'm like highlighting other people's work Like I will link this podcast, anybody who is talking about this, trying to facilitate this integration of these different fields into this kind of unified worldview.
Bobby Azarian, PhD:I wanna be a place where I interview people for the Roto Omega YouTube channel. There's a YouTube channel as well. I'm about to start uploading interviews. I've done with Adrian Bijan, who I know is a friend of the channel. I don't know if you've had it. Yeah, I think you've had.
Brian "Ponch" Rivera:Yeah, we've had it.
Bobby Azarian, PhD:Howard Bloom, yeah so, and a bunch of people I'll be interviewing in the future. But yeah, so that's what the second book is based on in the Roto Omega Project. Basically, I'm like drafting the chapters from the second book, posting it there, but it's gonna be more than that. Like I do wanna try to outline like a political platform based on these principles and start uniting these subcultures, and they don't have to all come together under one name or anything. But I think it's a big waste that we're all talking about this in these different communities with slightly different language, and we see the same solutions. And if we just knew about each other, we could create a signal that could be like hey, elon Musk, we have a better thing than this effective altruism in it, but it has all the good qualities of that. But there's all of this other stuff. Hey, whoever Bernie Sanders, you're this anti-establishment candidate and a lot of people wanna see problems with this rigid system, but you're talking about these old solutions. Like maybe it's time to get into systems, thinking Like so try to get people from both sides of the political spectrum who are willing to adopt their worldview, like we need to the culture.
Bobby Azarian, PhD:War is a massive threat to the welfare of the social organism. We cannot exist as two separate social organisms in one nation. We'll just constantly be in conflict. So we have to align interests, we have to bend, we have to think practically.
Bobby Azarian, PhD:Maybe your belief is right in some way, but it's not right if you're polarizing everybody with the way you're communicating it, because then no one's gonna do what you're going to say. So we have to realize we have to meet people halfway. We don't have to bend our morals and stuff, but we need to think in a smart way that actually leads to the outcomes that we want, and that means having a dialogue with the other side, not being ideological and deciding not to talk to people. So I think Twitter like banning people, blocking people is bad. We need to facilitate communication, but we also need to be like hey, if we're gonna bring people together and allow free speech is great, we should promote that, but we should always also talk about understanding when your worldview has errors and is rigid and uncertain, because we need to address that too, or else we'll just be stuck in this loop, yeah absolutely.
Bobby Azarian, PhD:In an unhealthy loop, yeah.
Brian "Ponch" Rivera:No, absolutely Bobby. It's been a pleasure. It's been amazing to meet you. Looking forward to having you on the show again and maybe connecting Northern Virginia down the road. Maybe go hang out at the Quantico, take you through the archives show you some of that?
Bobby Azarian, PhD:That'd be awesome. I'm totally fascinated with that. So, yeah, I'd love to do that.
Brian "Ponch" Rivera:All right, and then, for our listeners, make sure you get the romance reality and check out the Substack Road to Omega. Actually, I just signed in today. I need to go ahead and become a paid member, but it's pretty nice to see what's in there and our overall business.
Bobby Azarian, PhD:Thanks for doing that. Yeah, I really appreciate that the paid members get access. There's probably half the posts are behind a paywall, but yeah, so I'm about to become like start publishing lots of stuff, doing lots of interviews. A lot of exciting stuff is gonna happen. I'm also gonna call to the community to say if you have projects like a blog, a podcast, talking about this stuff, let me know. I'm gonna promote it. I'm gonna organize that to try to facilitate this coming together that I've talked about. So, yeah, check out roadtomegasubstackcom, and this has been a fascinating conversation. I feel like I have a lot to learn from this Udalloop stuff and I'd like to hear in the future about your intropic Udalloop model, because I think there's something really cool there updating that to include all this like thermodynamic stuff, which it sounds like Boyd was already doing. So thanks for having me. I really appreciate it. Oh, thank you and we'll see you next time. All right, take care.
