Transcription
[Music] [Applause] thank you. Let us begin. Um, so welcome. I should have put the date on here, but, uh, this is kind of maybe our beginning of a workshop series related to token economics, crypto economics. Um, so this is going to be the first workshop, kind of an overview of, um, uh, the field. I will try to present it, um, in a very objective way, but, yeah, let's get started. Okay, seven people subscribed. Sweet. So I probably foresee this as having different topics of interest, uh, but for a starting point, I think I want to show why this exists in a formal capacity. Um, what is, uh, the field of crypto economics? Um, how it ties into, I don't know, what we know today and what we don't know today. And then maybe an overview of what it looks like, uh, in practical terms. And then maybe think about that and, uh, maybe form kind of a mental picture of what you might call the virtual world. Um, and I guess take this as an opportunity to, to creatively, um, imagine what, what is kind of token, you know, token engineering, crypto economics, or kind of the group of concepts that doesn't have a jargon yet, doesn't have a specific, uh, uh, I guess nomenclature. But you'll over time maybe pick up on some patterns or mathematical formulas that we've looked at and, uh, maybe help you or me better picture, um, for a, a big, for a big freedom, for an introduction or [Music].
Um, uh, a basic explanation of the background history. Um, this was kind of how I think as early as it gets. Uh, maybe when Vitalik decided to just describe, um, what these systems are. Um, uh, a way to program the internet, essentially. You know, the way that we are connected today is through, um, you know, uh, maybe let's say here, either economic, political, or social purpose. And that's what a DAO is, uh, if, for, for all, for all means and purposes. Um, so this allows you to create maybe a network, um, across different nodes. And, uh, that is what the crypto community is. And so, um, what's, what's the motivation? That's what crypto economic networks are. But the motivation for crypto economics, um, is to analyze those systems of networks, um, on a multi-scale perspective. We will, I think, talk about that. Uh, but for now, um, to answer different kind of questions related to what does the world of, um, web 3 look like when you combine, uh, policy objectives, uh, by, you know, people who run the project, uh, then, you know, the agents themselves, who are, um, characters or people who are playing the part in the system. Um, and then being able to analyze whether, uh, the way that the system has been programmed is meeting certain objectives and goals. And then being able to have some kind of a tool or software, whatever it is, to help you analyze how it's performing. Um, that's all. That's not what a multi-scale perspective is really about. A multi-scale perspective, talking to the beginning of that, um, you know, basically, like, I think at a very high level, multi-scale is a way to explain how you want to understand, you want to simulate the environment, you want to know what is the system. And then you also want to be able to have a different way to analyze the system when agents come in or when people act a specific way according to the way the project is defined. Um, uh, I think this is also one way to explain how, you know, a machine can't just do everything a human can do, um, because thinking multimodally, in this, in this case, applying a multi-scale perspective requires you to think about all kinds of aspects to maintain the system. Um, and we can, we'll go over that, of course. But, um, crypto economic systems range from a lot of things, I think, from a technical perspective of hash rate or, um, burning tokens, and then maybe on a more purpose-driven, uh, a basis of looking at, um, the way that they move in the system and in the way that, you know, um, agents behave through an incentive or what, what we designed for them to do. Um, so there's a lot. There's definitely a lot to analyze. Uh, definitely it's not going to get covered in one workshop. Um, but, yeah, I think this, this picture by now should be able to explain that, right?
So I think if we look at, um, the rest of this presentation, but it, uh, what we've done so far with Decon and what we've looked at in terms of macro and what we see today in macro, an international economies, and what I mean internationally, I mean aside from the US. The US itself, um, there's high-level, high-level tools or high-level instruments being put in place which are achieving an objective, um, a policy goal, uh, in this case, a monetary policy objective. And then, uh, let's say today in real life, the perspectives, whatever policy goal setting could be, would be, um, you know, interest rates, like, on how a bank performs and makes us money. Interest rates can affect many different parts of house demand, uh, mortgage demand, so vehicle demand, credit cards, all kinds of stuff that banks use. And then the micro level of understanding how does that implement or how does that play into the behavior that people, um, uh, go about life? Like, what's their behavior? What do they do? Um, and then how that feeds back into, you know, the top bottom, um, uh, kind of, I guess, kind of perspective because there really are two levels to think about a system. There's a top-down framework or thinking about how people behave from, uh, what someone did the top steps as instruction. And then there's bottom-up, um, intuition also, which could explain how, you know, at the ground level, the way that people behave actually defines how the, the system behaves, not, not the person at the top. Um, but in this case, it could be both. And I guess that's what crypto economics tries to look at. Is depending on the project, depending on the specific goal that you're trying to accomplish, there might be different ways or different models or, um, different tools you need to use to analyze if it's getting, uh, the outcome that you're looking for. So this is maybe what you might, what maybe someone can describe as the process or, um, how it goes from point A to point B to point C to, uh, different ways to explain the foundation layer of, uh, this field. Um, crypto economics is by no means fully, uh, evolved. It has a lot of time to, a lot of time, um, to develop and have people who are able to construct theories and explain, practically what's going on, um, through interdisciplinary, uh, fields of work. So we can really go into the whole interdisciplinary piece in a second. I think I want to start off with, um, you know, kind of what our tokenized ecosystems, um, when they're, uh, you know, for example, in a DAO, that might be one thing. When they're in a project, that may be another thing. And then tokenized ecosystems can also explain how Bitcoin works or, um, different white papers that try to elaborate on the ways that their tokens are deserved to be construed with value. Um, so I think that might be the best way to really boil it down. Is today when there's high inflation, that means that there's no one there to control or have the power to control the value of the currency. And, and you can see that in many economies today in the world. Um, you can look at, you know, um, honestly, a lot of banks that, a lot of central banks, that don't enforce monetary policy. Um, and the reason for that is the, the goods inflate, um, for, for whatever reason from a macro level. And if they put inflation down, if they, if they increase the rates, it might even hurt, um, their own economy. And so, you know, international markets, even US markets, anywhere you look at, I think, um, macro can play a lot into how the value of the currency might be misconstrued, might be inflated, or, um, it might be just wrongly programmed, honestly. Or, and that's what the tokenized ecosystem can look at also. Um, and so, uh, I guess kind of there's two ways to look at it. I guess from an agent level model of how do people behave? What's kind of the fundamental economics or or game theory that can explain how agents interact with each other? And that applies a lot of the field of economics, microeconomics specifically. Um, and, uh, game theory as another field, which is also very complex in itself. So, um, deserves to be considered two, two different fields that play into, uh, the first state of analyzing a tokenized ecosystem. Um, but that is on an analysis level. The synthesis level of putting things together and and creating the tokens, the design of the tokens is a completely different level. It doesn't really require as much of, let's call it the microeconomics, but more of, let's say, um, some might call it operations research, some might call it systems and, um, a system of engineering. Uh, also, um, I guess you can call it verification design tools, which are traditionally used in electrical engineering. Um, so a lot of different interdisciplinary fields that also fit into the verification of tokens, analyzing tokens, designing tokens, um, and then even, of course, law. Law, or, or even computer science, uh, different ways to maybe even, um, maybe describe what, what tokenized ecosystems are. Um, and so really, like, there's no real home for, for, for, for crypto economics. It really is an embodiment of many different fields because we are combining the social sciences with the decision sciences. And that's quite broad. Um, so I guess kind of to get started in terms of what is tokens, you know, what, what are the design of tokens, even how to get started on that, on that spectrum, um, I guess there are kind of a few goals. So, uh, you have to look for, you know, um, maybe at a very generic level, what are you trying to maybe analyze or what are you trying to, um, design? If you're trying to design something related to, let's say, um, um, let's say like a token, but the system in which the token is going through needs to be modeled. Um, so you can maintain, let's say, in some case, an objective function, which tells you that you want to, um, maintain the value. And, and to maintain the value, you need to achieve certain objectives, which are burning X and Y, uh, tokens at such rate, or, um, any other objective which might try to, you know, maintain the value of the tokens, or store them in some kind of a pool, or, um, different ways that you need to make sure that the tokens are, are analyzed, like you have oversight over how much is in the system. You can explain the aggregates or the supply from an agent level model or the ground foundational level. Um, so then you can kind of think about, you know, what are the tools or what are you instrumenting, um, through design tools or, or, um, yeah, something related to design tools to verify membership, or, um, you know, kind of the optimization design going about the governance. And then also being able to analyze and make sure that really the value of the token is being maintained. That's kind of more into the mechanism optimization design. But, but that takes you kind of through, kind of the journey of, you know, how do you have, how do you imagine or how do you design a system which, which can show you how everyone is behaving at an aggregate level, um, how programmatic code, and, and mathematical formulation could actually, um, help you understand how it's performing, and then if necessary, how to design incentives and disincentives to change the agent level behavior. Um, and then at the end of the day, you want to have a system that works. Um, and at that point, you can obviously use, um, different tools to analyze maybe the macro, um, or the highest level of the layer of that system that helps you analyze if it's performing well. Um, so that's synthesis. Much more of a combination of macro, computer science, operations research, which is essentially optimization, decision science, which is also optimization, and, um, even electrical engineering, as we said before. Um, so that's kind of maybe even how you can explain this picture. Is, you know, the macro is the top, the middle is, you know, policies and code, and then the bottom is agent level behavior, or maybe microeconomics. So, um, hopefully that was kind of an introduction, um, some way to maybe think about how it looks. Um, you know, uh, definitely kind of getting into the weeds of, you know, what are these, what are these, um, systems gonna look like? How can you really like visualize it or be able to have a white paper that explains agent behavior? That'll definitely be a topic of interest for future, um, for future, uh, workshops. Um, but this is kind of the best way maybe to say that we've transitioned away from maybe the economics workshop, economics out of the workshop, but, um, you know, there are ways to visualize that system. And, and, uh, I think basically, I think for anyone who's looking at this for the first time and doesn't know what, what is optimization, you know, what is, um, uh, you know, game theory and ways to explain agent behavior, those don't really matter at the end of the day because you are a participant in, in the designers of the system are mostly concerned with, you know, the whole picture of, you know, making sure that, um, they're at the top, they're, they're, you know, they're ahead of the bad guys, or, um, they understand it the best because the engineer did, or, um, you know, tokenomic ecosystems can really follow [Music].
Um, some kind of, uh, uh, I guess maybe further discussion as to whether, uh, even people need to know as much about it, um, or just the rules of what is a token in the project that they need to know. Um, but, yeah, this is the theory, of course. Uh, I think we've gone over quite a bit of this already. Um, this is kind of what we discovered, discovered, discussed in a previous workshop. Um, uh, I guess kind of at a very high level, how to, um, I guess sly, um, optimization design and principles of, you know, um, kind of what we talked about so far to economics, um, multi-agent behavior, or what is it actually? I already forgot what I said. Um, uh, multi-scale, there you go. Multi-scale, um, systems, uh, for different purposes. So I think we've kind of gone over this maybe at a small, uh, at some extent today already, but we'll go through this quickly. You know, if you're talking about Bitcoin, you're gonna have one goal, which might be to, I don't know, minimize, maximize hash rate, something like this. Um, if it's going to be something related to a token on a mixed project, it might be maintaining the, the value of the currency. Um, it can be all kinds of things. And, you know, really, block reward function is a fraud term. Probably going to be, um, rediscovered, reimagined in the next 10 years without a doubt, because this is really, that is the primary way that, um, in the token economic world, um, you know, you are going to be modeling, uh, the way that people behave, how our blocks being issued, how they get introduced, um, and then how to measure when they're getting, when they're exiting the system, and what is the way to analyze, uh, their behavior. Uh, so block reward function is also going to be, um, you know, uh, it might not even be related to optimization or what, whatever today we discuss as operations research, which is, if you look at the evolutionary algorithm, this is what people refer to in tokenomics as the closest field of work, which can be introduced into tokenized, tokenized ecosystems, which leverage existing theory. Um, existing theory meaning, uh, evolutionary algorithms are, um, what you might describe as a dynamic model. What that means is they adapt to this, to the system, depending on what the inputs are. Um, and so there's something similar to machine learning, there's something very similar to decision science, or, um, uh, essentially the confluence of of agent-based behavior, um, describing how to systematically or programmatically change, uh, at a macro level, um, uh, the factors to to achieve a new objective. Um, and so that's what evolutionary algorithms are. However, um, the way that they're applied in tokenized ecosystems is certainly going to be new, and going to be unique. So you can only say that the closest field is going to be objective function, something traditionally discussed in operational research as minimization or maximization of of a certain objective, which will tell you, um, what you want to achieve and the constraints that that are going to be required to to achieve that function. Um, I think even one way to describe this for anyone on the call who maybe has never been familiarized with objective functions would be to say, actually, this is such a long time ago, but it's like, what are they called? I'm in like fifth or sixth grade. I used to learn about these like multi, like these, um, multiple equations, solving for like three variables, and you're given three equations, and then you gotta cancel them out using substitution or elimination. I forgot what these are even called, but, um, that kind of tells you what is maybe the variables that'll tell you, um, you know, exactly what should be the way that the system should be designed so that people are, um, uh, performing exactly as intended. That's what it's going to be in real life. But, um, that's the goal. That's one of the goals. Um, yeah, I definitely recommend if, if you're interested in optimization design, it's going to require a whole workshop, but we'll get there. Um, so kind of next will be a measurement and testing, uh, uh, for different purposes. Let's say, how is something performing? Um, so, you know, we'll have something where we're describing, uh, the entire system, and we're saying, okay, you know, considering X, Y, and Z scenario, how are people going to behave differently? Um, and then that's what the simulation is going to tell you. Is, you know, people might enter the system differently, they might exit the system differently. And that's an evolutionary algorithm perspective. That's today's theory of complex systems, how people behave in those, um, in those, in those, in those environments. But we don't know, you know, tokenized ecosystem, whatever you want to call it, how to test that. Uh, that's going to be new. That's going to be novel. Proof of work, proof of stake, these are all different, you know, novel approaches to to creating an ecosystem. And so the measurements for that are going to probably be new as well. Um, system agents, you've discussed is probably what, enough at this point, but how to be described the way that nodes behave? Traditionally, it might have been described in electrical engineering or computer scientists, computer agents or nodes as part of a computer. You know, they're not really humans, they're just computer agents. And so that's going to be completely different, reimagined with miners and token holders, people who are going to be holding the token and behaving to follow a certain incentive or disincentive. So agents are going to be one way for us to say that, are they behaving the way that we want them to behave? You know, just someone, you know, I received tokens for doing this workshop, and so I am an agent. This is how it really is like, you know, what if I decide to do a bad workshop one day, right? Like, like, um, you know, like, uh, that's not going to be what a computer does. So a token holder also needs to be, you know, very much following the code, or let's say, kind of what the goals are, otherwise, there needs to be disincentive. So, you know, maybe there's some system clock or some way to reward my performance or something that that can explain how my behavior is doing. And that's what generation is. If you look at the last model we discussed, generation, I mean, generation can be the same as emergence, to be honest with you. Agent level behavior at the very bottom-up perspective can be explained as by this diagram as emergence. But a generation can also be telling you throughout the system, how is the block, uh, getting rewarded, how is it staying in the system, and how is it growing or or, uh, I guess even following an objective. An incentives and disincentives will then allow you to, you can't control people's choices in an evolutionary, evolutionary algorithm, but you can in a tokenized ecosystem. And that is a good thing because you can punish and reward, like ways you haven't before. And that's how, you know, you really are playing in a simulation, you really are playing in the game. But if it's really designed and construed well, it won't even be, you know, um, it doesn't have to be thought about. It is, it doesn't have to be something to worry about in terms of how your agent level behavior plays into the whole ecosystem. Um, and, yeah, I think that's also going to require a lot more discussion, as well as agent-based behavior, how to test the system, incentives themselves, what are they, all kinds of topics of interest in the future. Um, so, you know, this is kind of maybe some way to tie it back to where we started, but, you know, what is, uh, the model which can explain behavior or can I explain how different people are, you know, achieving, um, a goal for the greater good? Um, I think commonly we'd look at the economy and we say, oh, economy is doing good, doing bad, who cares? You know, I have my own job, I make my own money. Um, and that's true to an extent. For example, in, in the US, um, you know, who, there's two consecutive quarters of declining GDP growth, but if you look at GDI, gross domestic income, there has been no decline, like nothing has been in the negative in the past two quarters. In fact, GDI has either been positive or near zero, which could also be another way to describe as you can see right here in the bottom of this model, disposable income, or how to describe the performance of consumers and how they're saving. And that's what the financial system is. So, um, you know, there's different ways to say that, you know, how is the project performing versus how are people performing and how are they doing? Is two completely different, um, sides of a tokenomic system. But there has to be one unifying model to explain it all, or or at least have some way to, um, quantify how our different variable is performing. Um, you know, you can look at how GDP is measured, for example, how do inventories zi, how, how is exports and imports being defined in in global trade for that com for that country? And then see consumers, how much is spent, you know, G, government purchases, or how much fiscal policy plays into GDP spending, or free money being given by the government for certain initiatives X, you know, don't know what X is, I'll be honest, um, or, um, but that's not the point. That might be exports and imports, and I might be, um, completely different. I have investment, that's right. Um, so, you know, and definitely, I, I would say that, you know, GDP, if something is not performing well on a quarter to quarter basis, it's okay, right? Because today, at least in the US, I can only tell you labor markets are tight, which means that new jobs are being created every day, which means that people have more than enough economic opportunity to succeed in society. Um, but GDP in terms of describing the US as an economic powerhouse or something that displays, I don't know, some kind of a machine or something. I mean, I'm sure over time GDP will will accrue when, you know, back to its regular rate. Um, but, but that's not all that, that's the economy. That's not economics. That's not anything related to, you know, um, economic performance or whatever. So that's, that's the way you can connect real world picture to, you know, how does it really matter for web 3? How does it matter for blockchain? How does it matter for cryptocurrency? You know, everything is going to go through a system that's going to go through some kind of a, um, designed optimization or design state, something that's been intentionally created. And it's going to go through those through those ins and outs. That's the, the, you know, the block-based token, whatever block-face agent goes through the system, and it'll get flushed out eventually. And the performance can be measured, or, and if it's feeling really bad for a long, long time, then that's like a recession for a project or something. But like, you know, in general, you know, countries exist forever, and projects will exist forever, hopefully. But hopefully this model, at least along with some commentary, um, allows you to look at how agents can go through a system or people really do feed into the performance of the economy. Um, so definitely wrapping things up here. Um, I guess kind of if you have any questions, hopefully we can get through those. But, um, you know, really the last thing I wanted to share was maybe a project or something which helps you understand, um, how do tokens come into the system? You know, uh, what is some kind of way you can, uh, analyze that or, or look at their behavior? Um, so, um, obviously this is kind of some, this is the Ocean Protocol. Frankly, I need to look into myself what are these projects really. I kind of pulled this out of the internet. Um, but, uh, I guess kind of I will get better at that as well of, you know, um, something I'd like to start doing as well for these workshops is towards the end, is have some kind of a way to explain how projects are applying the concepts that we discussed in the beginning. Um, I get for an introduction, it's okay if I can't explain it the best way possible because at least I'm giving you an overview. Um, but, uh, you know, I guess tools that help you look at, um, I don't know, like how the token is performing in the ecosystem, that could be voting, or, you know, staking, or different ways that people behave. That'll need to be analyzed, I guess. But, uh, you know, the thing about projects that's going to require a whole different discussion is what's the business model? And then what does the macro itself want to do so that the agents perform the behavior they're looking for? Um, you know, if you want to look at anything from a tokenomics perspective, you have to think about the, the top to bottom, um, you know, what is the entire objective of the project? What's it trying to achieve? What's it trying to minimize? You know, what are kind of the platforms, what's the software, what are the tools they have, so that, you know, you can look at how the project is gonna behave or how people are going to, um, construe some value. You know, even NFT, for example, can be like, you know, people interact with the website really well, and so now these images or these files can be given some value that people will pay a big amount of money for. That's intrinsic value that was created, um, through that ecosystem. So, uh, there's definitely ways to do this. It's been done with NFTs, so it can be done with tokens, right? That has to be the optimism, and optimization. So funny, I guess how that all works together. Um, yeah, so I mean, I really don't have a lot of insight I can give you on this project, because once I do, I'll try to construe it better. But, uh, I hopefully you can look at, you know, um, you know, how, I guess these tokens get created, how they, how they get used, and then how people can unlock their own, I guess, uh, ways to to profit or or get some value from it. And then how that contributes to the project's goals, which can be measured by maybe revenue and volume. And then how to bring the whole, uh, circular loop back together. Because I think they called this in the article I looked at, some kind of a sustainability loop or something, which is supposed to help you, um, look at how everyone benefits at the end. I think profit, what they call it, profit, purpose, and there's another P. But, uh, basically, companies are aligned with good incentives as opposed to just money, because they get money at the same time. And, oh, profit, people, purpose, I think that's what it is. So I, I don't know, I'm gonna be wrong. But, yeah, how to connect everything together so that the ecosystem is functioning and bringing value to everybody. Um, yeah, the bottom left buy and burn Ocean tokens, I guess that's part of the token design, which I would have liked to explain better than what I just said. But I guess, yeah, there's definitely, um, you know, depending on the project, there's a lot of policies, there's going to be a lot of governance, a lot of, you know, um, incentives, disincentives that are going to be created over time, so that you can try to include as many people into the system as possible and, you know, create a booming community. But also, um, find or design some kind of a, you know, what where did they, where did I was talking about this before, you know, uh, tokenized ecosystem or something that can, you know, leverage, um, you know, block reward functions, right? Block reward functions of, um, explaining in mathematics how do these tokens die or how do they get burned, and then how do I get bought? So I think, yeah, that's going to be something very new. Block and blockchain, I don't know where that connects at all, but I think there is a connection there. I think I know what it is in terms of blocks, but, um, yeah, this is definitely a very booming, um, field of work. I think everyone has stuck around. It looks like still 12 people on today, so I guess I managed to keep your attention. But, uh, yeah, I think that's kind of the overview of what tokens are going to look like that can describe the economy today. And I think we can always look at a prior workshop slides to maybe look at the future of, what this present, what these workshops are going to look like. But it's going to be more of a token, a token economics, crypto economics, um, workshop series from now on. Um, so I definitely value any comments, requests, something we can look at in the future. But, um, yeah, that should probably be it. I think that's wrapping it up. But if you want to go back to any slide, you can go back. Have more of a discussion. I think as time goes on, these are going to be less like workshops, these are going to be more like, hopefully, community discussions because I might give you a couple of questions at the end for you to analyze from a philosophical perspective, because that also is another discipline that feeds into crypto economics, is ethics and philosophy. Okay, I will call that the end of the presentation. Anyone want to share how you felt? How is this, uh, be a workshop? Add a workshop that I do. People awake. That was a great presentation for sharing it with us. It's very insightful. So, good question. What do you think the future of physical economics is going to be like in kind of, you know, 10 years or so? Yeah, honestly, [Music] I think it's going to be up to how central banking, um, regulators are going to allow it to, um, evolve and grow. Because this can be a booming field in itself. For example, if you look at structural engineers, my dad's a structural engineer, and he uses CAD, which is a way to simulate how buildings are performing or how they're being designed. And that's also used in electrical engineering, CAD, computer-aided design, CAD, and that's going to be used in in this as well. And how many companies have designed good CAD software? I don't know. I don't know how many projects have been around for them to design good software. So, uh, it's going to be, it's going to depend on, um, how regulation wants to treat cryptocurrency, if it's going to be taxed, or if there's going to be some kind of macro, real-life macro that that impacts the performance of tokens. But, um, there, I have optimism. I think optimization has a lot of optimism. But, uh, um, in terms of where it's gonna go, it can go anywhere. It could be, um, you know, you could measure, uh, the time between Vitalik saying talk, crypto economics in 2014 to now, 2022 was eight years. And then, you know, the W, the dot web opening in '91, and Amazon coming out in '97 or '98, there's eight years, the time span. So the future is obviously now, because that's how technology gets evolved is, um, people sit on the idea for a while, and then there's some adoption. So, yeah, there's, there's unbounded opportunity, but there's also restrictions or constraints, which we'll see from the regulators for sure. Oh, excellent. Thank you. Anything else? You know, um, I guess I can give like a poll or something, or give some kind of an idea, try to get some kind of a, uh, uh, engage on what people want to learn more of. [Music] There's a lot to learn, but, um, yeah, I'll definitely send a poll through. Maybe that's the best way to get some of your feedback on it. But I think if everyone's been listening in today, uh, you definitely know that there's a lot more to come. Um, anything that you want to look back on the presentation, it was confusing to you, something, you know, you wish I talked more about, any feedback helps, right? With that, I guess we can adjourn. Thank you. Bye-bye. Thank you. Thank you. Cheers. All right, take your advice. [Music]