Interview with DR. Letychevskiy: Math Modeling and Sustainable Token Economy

Dear Wax community, today Dapplica has a very special blog entry like we’ve never done before – it’s an interview with a real professor and mathematician about how a sustainable token economy can be designed and the ways math can help with that.

So, please, enjoy the transcript below, listen to the podcast or wait a bit for the release of a video!

G: Hello everybody! I am Gleb, a member of the DApplica team. And today we want to welcome and introduce a real superman, Alexander Letychevskyi. Am I right?

A: Sure!

G: Good! This man is a doctor of Computer Science and a real university professor, scientific director of Litsoft, and co-founder of Garuda.AI company.

And today we are here to talk about a very important thing. A thing that almost nobody knows how to do, because there are still no benchmarks. We are going to talk about how to build a sustainable token economy for a blockchain project that will be able to last for a really long time.

So I have a lot of questions! I will start with the first one, and I hope we’ll find the answer together. If not Alexander, then who can know this?

Alexander, first tell us a little bit more about yourself, and then I will ask more precise questions.
Tell us about your experience and why you are here. Why do you think you can answer that big question that really annoys a lot of people, and a lot of people are interested in it, especially nowadays?

A: Okay, initially I’m a programmer. Just an experienced programmer from the 90s.

And my 1st experience was the programming of an algebraic modeling system, which is known in the scientific community.
So I am a follower of the algebraic school of Victor Glushkov and the algebraic school of my father. He was a prominent academician and inventor of the insertion of algebraic modeling which we use in the analysis of different processes starting from the verification of the hardware, and software; the analysis of the economy, biology, analysis of the consensus algorithm, and many-many other things.

And now we consider the token economy as the subject of the application of algebraic modeling, of the formal methods. I think it would be a success because we have experience with such tasks that require a big space of search, analysis, and algebraic methods to allow.

So, my company also works with algebraic modeling – Litsoft.
I am an academic working in a university, in an institute. There is also some research on algebraic modeling in different subjects and domains.

So today I can consider and answer the questions on how to work with such subjects as domains, which are very complicated for understanding, analysis, and modeling; how to estimate the properties of these subjects. So this is one of our purposes in algebraic modeling.

G: Perfect! So you’re the right person to ask these questions. Okay, then let’s get straight to the point. 

My first question will be: from what we see now, most projects, even if they have a whitepaper and seem to have quite a thoughtful token economy, but from lots of perspectives, and if we have some experience, we’ll easily understand that lots of the projects have a pyramid structure or even if creators don’t intend to but somehow the pyramid way of thinking prevails.

And I would ask, from your experience, from your point of view, if we talk about pyramids versus sustainability – how can we understand and see, maybe via some red flags or some criteria, that tokenomics offered by a project really has a potential for a long-term existence and development? Or is it going to be another pyramid as we see in 90-95% of situations?

So pyramid versus sustainability – what is the key difference? How to define it?

A: First of all, when you are creating a token economy, you should understand that you never can embrace all the points, and elements that can be involved in token economy activity. So this is because the human brain can’t catch everything to understand what is better, and what is good.
Because there could be millions of scenarios of the token economy behavior.

So how to analyze all these scenarios? You may consider one or two concrete examples, but a very unexpected scenario is when you like: “oh no, it’s my fault, I didn’t take into account some issues and facts!”

G: Shame on me!

A: Yes! So when we are talking about token behavior we know the agents that interact with each other. What are they doing? One can buy, and one can sell with a token or fiat money. And in my opinion, the only thing that could resolve this problem is using modeling. We know the imitational modeling of simulation. It’s concrete modeling.

But okay, you create a model, consider one scenario, two, fifty, but there are a million scenarios. How to involve all scenarios? For this purpose, we can use algebra, because we use abstractions. We use some abstractions that cover thousands of scenarios.

We can not take into account things that are useless or not necessary for understanding some features of the token economy. In our experience, we start with one of the first token economies that we realize. It was 4 years ago, we started to create an algebraic model and provide some analysis for understanding.
First of all, we should understand the tokenomics equilibrium. Maybe your intuition understands what it is.
But we can prove the equilibrium or we can prove that we will not destroy the behavior that we’ve planned, and built, and will it be the same as we imagine?

For this, first of all, we would like to know – do we have an equilibrium? Then maybe some special requirements: do you want liquidity of the token after two years? Shall it be equal? Or some values that you would like to predict?

Next, maybe you want to research decentralization property – it’s also very important in different projects. Or how good is your service which is the subject of the token economy? Is it good because of how many people are involved or can be involved in the service of the product?

You should predict the number of people, you should predict the liquidity. But you should predict it by modeling and understanding: does it meet your expectations or not? 

So this is a very short answer.

G: Okay, I will just sum this up a little bit.

So we need to understand if a token economy has the potential for long-term existence and development. And that is what a real creator or long-term investor will definitely look for. So we need to understand if there is a way to achieve or if the economy achieves equilibrium at some point and then persists.

A: Some restrictions.

G: And that is the thing that you can’t just see, you need to count. Then understanding the liquidity of the token in the period, centralization property, risks, and quality of the product. I guess it is connected a lot with the use of a token, right?

A: Yes.

G: How and in what situations we can use it etc?
Okay, good. Maybe some red flags or other points that we should look at. Maybe some risks or something that will show us…
A: Regarding red flags: even in the draft of your token economy model, you can consider some distribution of tokens and distribution for the long term.

It’s very easy to consider from concrete different charts. But in algebraic modeling we have no charts, we have a formula. If the formula is valid, then okay.

For example, after 12 months the value of my reserve will be restricted in some values, and the price of the token will exceed some values that you can predict. And you can create a big formula and even on concrete charts, you can consider this.
But the problem is how to create an equilibrium and self-sustainable token economy.

From one side you would like to have an equilibrium, from the other side you – to have some results, some token distribution, or other characteristics shall be in some area. For this purpose we use backward modeling and, for example, I defined some properties that shall be after two years. And I started backward algebraic modeling and reached the first month and I will get initial values that could reach desirable results.

G: It’s like a way back machine: we have input data we think of, and then we have some end results within a year or two. And then we model and see where it leads to. And I believe usually we will not get what we desire, what we want. And then we start to move back to see what should’ve been the initial data, the initial numbers to reach the state we want in the future.

A: Yes.

G: Interesting, interesting. You know, as I’m not a mathematician but for me, that’s clear that it will help. Because usually how it starts – what I see: oh, let’s have 10 million tokens and unlock it during some period. And then it will be fine!

A: Maybe it will lead to impossible initial values, and maybe you will never reach such results. But you understand!

G: But still you can believe it!

A: Yes, you can believe it.

G: Good, today we are here to talk not about belief and hope, but about real stuff – what we can think of and try to make, and even predict something.

Okay then. But you’ve talked about backward modeling. What are the tools, approaches, or methods you use to help people that make the token economies, do a real thing?

A: It was known as a subject domain as symbolic execution. Forward and backward symbolic execution was found at the beginning of the century.

Our team was one of the best in the world at creating backward symbolic modeling when I worked with the Motorola company. We worked with Motorola for 9 years. And all these methods which we use now in the token economy – algebraic method – are applicable to software, hardware, and the token economy.

So we’ve created an algebraic server that consists of a number of formal methods like symbolic execution, and symbolic algebraic modeling – backward and forward.

It also contains other kinds of formal methods. For example, algebraic matching – is a very interesting thing. Because in the token economy, it can be a risk when some people are the fraud and they can do malicious actions, some attacks to get a profit from the token economy. Like in the double-spending consensus algorithms, like the Sybil attack and something like this. It can be also possible in a smart contract, Re-Entrancy, and other attacks.

So how to understand? We know we have a big collection of cases, and attacks for smart contracts or for different platforms. We provide the formalization of these attacks. We create some algebraic signature of attacks, we use it and we have an algebraic server, an algorithm so-called algebraic matching.

We have a token economy and behavior, and we have an attack. We should check whether this attack is reachable. And if it is possible for each condition. Also, there are a number of other formal methods. And we’ve created an algebraic server – we use it for the analysis of the behavior. 

Every subject has behavior. So token economy is also a subject and it has a behavior.
It can be described in algebraic form.

The algebraic formal method can be also applicable to behavior that we describe with algebra.

So of course there are many tools in the world that we can use. Maybe there are more or less friendly, too complicated proving systems, solvers, or modeling systems.
People use it, but I think that our methods can participate in the competition! Because too many prominent and well-known companies like Motorola, Amazon, and Intel are interested in our methods. We cooperated with them and cooperate now on algebraic modeling.

G: Good! I’m sorry, I heard some unknown words, let me ask this way around: I understand that you can compute and model a lot, almost everything. But from the perspective of an ordinary, let’s say, average token economy creator – how do I start with this modeling? How do you do this?

I have my vision, maybe I thought of lots of things, maybe I have even got a blue paper or a draft whitepaper. But, of course, if I am smart enough I will anyway have concerns. You know, smart people don’t trust themselves! And I want to do some modeling. The one thing I need to understand is what I will get in the end, and what will be the end result. I will see some charts, but maybe I will not understand them. So how do we start? You can choose whatever side you want.

A: First of all, you should understand who are the stakeholders of the token economy, and what are the agents – these are the first things. And interaction. We have an agent and a set of agents that interact.

Also, you should define when and what each agent performs. It pays, or it sells, or it buys. When, how much, why.
If you answer these questions, you will have a full picture of what you can do with this.

Then you should find some tools and input all the information in these tools, and get the scenario – one or two.

G: Of behavior?

A: Yes, the behavior of agents. It can be good behavior, it can be bad. Maybe undesirable behavior or something like this.

If you take into account all factors that impact the action of agents, then you have the complete history, [complete] situation, [complete] environment. Then you can use methods.

You can do it yourself, but if you have some methods, and technology to list the agents, to list the actions, and understand the semantics of actions, then the probability that you miss, bad, undesirable, unpredictable behavior is much less.

Of course, it’s better to use some tools – some simulator or some modeling system.

We offer our own kind of tokenomics constructor. So I can tell you about it.

G: What is it? 

You say: “You can use some tool, you can do some modeling”.
But usually, we do internal testing, trying this or that, get results, make adjustments, think of things and just then go.
That’s the question – where to go?

A: I can talk about the tokenomics constructor that we’ve started to implement for token economy beginners.

G: How does it work?

A: So on the website, you can input some initial number of tokens, emissions, ICO rounds, how much fiat money you will spend, how many tokens will you distribute among stakeholders, and how many tokens you will leave in reserve.

If you would like to create some service and token economy for service, you should define the semantics of your service.
For example, the price of your service, and possible rewards from your service. You should define mining and then you will have a complete picture.

Then about tokenomics constructor. This is an initial platform you can input all these values in. You can request some initial charts, and you can estimate – is it what you want or not; you can change, play, and so on.
But this is for the beginner, so to create a good self-governed token economy we are developing an advanced version of the token economy creation service. This one is much more complicated, and you should maybe get our consulting or prompts, you should input more information and then you can request the analysis of your data.
Is it complete data or not, if it’s wrong you will get a message or warning – it is not good or it is very good.

It is for some kind of friendly environment because inputting an algebraic model is too complicated.
When we started our first model we didn’t have any experience. We worked on different projects, and it was too difficult to understand the customer, and what he wanted. And the customer didn’t know if we’d implemented his requirements correctly or not.
I will show him an algebraic model and say: “It was easy, it is just like a programming language, you can read!”.
Or: “Oh no, I just want to know if will I get money in the nearest future or not.”

But now okay, now you have some initial tools, you can input and try. Everything is on the screen – you can play, and get a chart.

I think in the near future we will have tools for tokenomics beginners and for advanced economists.

G: Sounds really good. We’ll look into it. Send me a link when you have it.

But still, we talked about all this modeling. When we think of modeling, an average person like me, what I imagine is that I will see charts, some numbers, and broken lines, hopefully growing when it’s needed. But you said that we entered some data and then we got the whole picture.
What is this “whole picture”? What will I see? And how do I understand what is going on?

A: I think the whole picture in terms of model creator is a scenario of the behavior of every agent: how much he pays, how much he gets, how many rewards he gets. I see a scenario, for example, of an investor. But can I estimate whether his behavior is good or not? Or behavior of the team, of the marketing team, or of the users. Is it good or not?

There are some key points: the number of users on your service.
But yes, you are right, you understand if your token economy is good or not. But how to understand if is it complete or not? If it is not complete, it will not work.

G: Good!

A: I will give you a prompt, that it is necessary to understand the intensity of sales or what you want. Do you want to give us information about the sale you can predict or we can consider some historical data?

Because the model is created with the purpose that if you will input insufficient information then we will prompt – you didn’t input some points you should understand and we can consider them correspondingly default values.

G: And the last in this series: will I know from modeling the token price in a certain time period?
How much will my token cost?

A: Of course! One of the key points is that you should analyze and understand. And you can send a request: I want to know will the price exceed some values after one year. This is an analysis of your token economy. 

G: Yes! That’s what we want – we want to know the token price. It’s all we want to know – will it go to the moon or not?

Maybe one last question. I don’t know if it is possible to describe, but still, I will try to ask.
Maybe from your long experience: do we have any benchmarks or examples of sustainable token economy in existing projects? Maybe there is some kind of North Star we should look at and try to follow and copy what they do.

A: Bitcoin is the best token economy that works. The work of exchanges is also a token economy, Ethereum. But this token economy was created just for trading, not for services. And if you create a token economy for some services, I know good smart contracts, for example, in Australia on the national level of programming. For example, IBM creates a good logistic platform, but I don’t remember the kind of token. I know some platforms that use tokens, but they are prototypes. Not many people use them.

Unfortunately, I don’t know of a successful self-governed and self-sustained token economy. Not much time has passed since the invention of the token economy phenomena

G: Just like a decade.

A: I know, for example, tokens in the game industry, but I have no information if it is successful or not.
Token in science, in the review of scientific papers, the reputation, and awards among the reviewers, a project in education. Really, maybe I can’t remember right now, but there were too many projects, I can’t remember the most successful of them.

G: That’s okay because there is not so much time for the whole ecosystem.
The good news is that we all still have a chance to be the first ones!
So Alexander, thank you so much for your time today. It was an interesting talk.
Shame on me, there were some words I didn’t understand, but I believe it will be explained in some other interview.
Send us a link to the constructor, it sounds interesting. And let’s see who is going to build the first sustainable token economy in product service. Looks like it’s still empty. Who is going to be the next North Star? We’ll see.
Thanks a lot, have a good evening, and see you next time. Bye!

A: Bye! 

That’s it for now. Hope you’ve found this interview interesting and soon we’ll see real token economies brought to life!

Best Regards,

Dapplica Team.