Web 3.0 – where will it take us?

Maciej Zieliński

01 Mar 2022
Web 3.0 – where will it take us?

Decentralization and token-based economics are concepts that have started to reach far beyond the Blockchain industry. Web 3.0 - check about what the world’s biggest tech and venture capital companies are talking about today. 

Read about:

  • Web 2.0
  • Semantic web 
  • Decentralized web
  • AI and web 3.0
  • Change of user experience

Web 2.0 - How does the World Wide Web work today?

If you wonder which technology benefits from over 3 billion users, here is the answer: the World Wide Web. Today it’s difficult to imagine the modern world without it, even for people who remember times before its creation. This technology changed and defines how we share, create and consume information. It's present in every industry, shaping the way we work, learn and play - for many the internet became the central point of their lifestyle. 

Web 1.0 and web 2.0

Essentially terms web 1.0 and web 2.0 refer to time periods in the web's evolution as it evolved through different formats and technologies. 

Web 1.0, also known as Static Web, was the first version of the World Wide Web created in the 1990s. Back then user interaction wasn't a thing and searching for information was extremely inconvenient for internet users, because of the lack of search engines. 

Thanks to more advanced web technologies, such as Javascript or CSS, web 2.0 made the internet far more interactive. From that moment social networks and interactive platforms have been flourishing. 

Growth of the web 2.0 was largely driven by 3 factors:

  • mobile technology
  • social networks
  • cloud solutions
Growth of web 3.0

Mobile technologies

Smartphones creation resulting in mobile internet access drastically increased both the number of web users and time of its usage. Since then we’ve started living in an always-connected state. Reaching your pocket - that’s all it takes to get access to the web. 

Social Network 

Meta isn’t the 11th most-valuable company for no reason. Before Facebook or Myspace, the internet was largely anonymous with limited interactions between users. Social media platforms brought revolutionary possibilities. User-generated content, sharing, and commenting disrupted the information circulation.

What’s more, our internet persona became an extension of the real one. Thus, not only did social life partly move to the web, but we started to trust each other there, having tools that to some extent enable us to verify each other's identity. Without it, the success of companies such as Airbnb or Uber would never be possible. 

Cloud solutions

This article was created, reviewed, and edited using Google docs - a part of the cloud solution provided by Google, that most of the readers are probably familiar with. 

Cloud providers redefined how we store and share the data. It is the cloud that enables the creation and maintenance of most web pages and applications we know today. Companies were able to move from possessing expensive infrastructure to renting data storage, tools, or even computing power from dedicated companies. 

Disadvantages of Web 2.0

Web 2.0 definitely shapes how the current society functions, giving us possibilities we couldn’t even dream about before. Yet, it's not free from disadvantages. 

  • centralization
  • abundance of information
  • non-sufficient verification
  • monopolization
  • low personalization

With more and more issues that we’re grappling with, one question has become inevitable: What will be next?

web 2.0 vs web 3.0

Semantic Web 

The semantic web is a concept formulated in 1999 by Tim Berners Lee, the World Wide Web creator:

I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A "Semantic Web", which makes this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy, and our daily lives will be handled by machines talking to machines. The "intelligent agents" people have touted for ages will finally materialize.

The vision of an intelligent internet that can understand the users and work without external governance back then was far from being realistic. Yet, today, with new technologies that we’ve developed, it may become reality sooner than we could ever predict. This is the moment to introduce you to the phenomenon of web 3.0. 

An original concept of Web 3.0 was coined by Gavin Wood, Ethereum, and Polkadot creator, somewhere around 2019, that refers to a "decentralized online ecosystem based on blockchain." The idea of the web which instead of using centralized servers relies on scattered nodes quickly gained a significant number of supporters.

Key features of web 3.0

Web 3.0 - key features

  • Semantic Web
  • Artificial Intelligence
  • Decentralization
  • 3D Graphics
Semantic analysis

Semantic web and web 3.0

In the semantic web, computers are able to analyze data with an understanding of its content, including text, transactions, and connections between users or events. In such systems, machines are able to accurately read our emotions, feelings, and intentions just by analyzing our input.  Applying it would greatly increase data connectivity, and in consequence, provide a better experience to the web users. 

AI in web 3.0

Artificial intelligence

Machine learning and artificial intelligence are key technologies for web 3.0. Currently, Web 2.0 already presents some semantic capabilities, but they are in fact most human-based. Therefore it is prone to biases and manipulations. 

Let’s take online reviews as an example. Today, any company can simply gather a large number of users and pay them to write a positive review of their product or service. Implementing AI, that would be able to distinguish fake from real, would increase the reliability of data available online.

Essentially, AI and machine learning will not only enable computers to decode meanings contained in data but also provide a more personalized experience to web 3.0 users. Online platforms will be able to tailor their appearance or content to an individual web user. This will bring a revolutionary change to the e-commerce sector as targeted advertising will become routine.

3D graphics in web 3.0

3D graphics 

According to some theories, with the introduction of web 3.0 borders between the real and digital world will begin to fade. The constant development of graphic technologies may even enable us to create whole 3D virtual worlds in web 3.0.

This concept is closely related to another issue that recently has gained significant popularity: metaverse. 3D graphics in web 3.0 will revolutionize sectors such as gaming, e-commerce, healthcare, and real estate. 

Decentralised web 3.0

Decentralized web

Current web infrastructure is based on data stored in centralized locations - single servers. That can potentially make it prone to manipulations or attacks. Furthermore, most of the databases are controlled by a limited number of companies such as Meta or Google. Web 3.0 aims to change that by introducing decentralized networks. 

In web 3.0 data will be stored in multiple locations - nodes. Any change of data will have to be authorized by every node in the infrastructure. The exchange of information will be taking place in peer-to-peer networks. It will not only take the data from the central authority but also make it more immune.

Digital assets in 3.0

Web 3.0 is expected to bring a totally new approach to digital assets. Tokens economy based on blockchain technology will become an even more common phenomenon.

Even today we can observe how blockchain technology is shaping the exchange of goods, investments, or ownership rights. Fungible and nonfungible tokens constantly find new applications that provide users with groundbreaking possibilities in industries such as gaming, real estate, or even healthcare.

On the internet of future ownership, control will become an even more vital issue. Blockchain technologies, and NFTs to be more precise can bring significant improvement in this area. What if assets, such as digital art or virtual land plots, were already carrying data about their owners and creators? Data that would be impossible to manipulate because it will be stored and confirmed in distributed ledgers.

What will change for web pages with web 3.0

Where web 3.0 will take us? According to many experts, we shouldn't treat web 3.0 as a totally new internet. It's just another stage of its evolution. Some of the solutions on which web 3.0 will be based already exist and function. In many cases, it's just about the scale.

Yet, the new web will definitely make a place for revolutionary business models. Personalized web pages or shops in 3D virtual spaces are just some examples of new possibilities that web 3.0 will form.

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Monte Carlo Simulations in Tokenomics

Kajetan Olas

01 May 2024
Monte Carlo Simulations in Tokenomics

As the web3 field grows in complexity, traditional analytical tools often fall short in capturing the dynamics of digital markets. This is where Monte Carlo simulations come into play, offering a mathematical technique to model systems fraught with uncertainty.

Monte Carlo simulations employ random sampling to understand probable outcomes in processes that are too complex for straightforward analytic solutions. By simulating thousands, or even millions, of scenarios, Monte Carlo methods can provide insights into the likelihood of different outcomes, helping stakeholders make informed decisions under conditions of uncertainty.

In this article, we will explore the role of Monte Carlo simulations within the context of tokenomics.  illustrating how they are employed to forecast market dynamics, assess risk, and optimize strategies in the volatile realm of cryptocurrencies. By integrating this powerful tool, businesses and investors can enhance their analytical capabilities, paving the way for more resilient and adaptable economic models in the digital age.

Understanding Monte Carlo Simulations

The Monte Carlo method is an approach to solving problems that involve random sampling to understand probable outcomes. This technique was first developed in the 1940s by scientists working on the atomic bomb during the Manhattan Project. The method was designed to simplify the complex simulations of neutron diffusion, but it has since evolved to address a broad spectrum of problems across various fields including finance, engineering, and research.

Random Sampling and Statistical Experimentation

At the heart of Monte Carlo simulations is the concept of random sampling from a probability distribution to compute results. This method does not seek a singular precise answer but rather a probability distribution of possible outcomes. By performing a large number of trials with random variables, these simulations mimic the real-life fluctuations and uncertainties inherent in complex systems.

Role of Randomness and Probability Distributions in Simulations

Monte Carlo simulations leverage the power of probability distributions to model potential scenarios in processes where exact outcomes cannot be determined due to uncertainty. Each simulation iteration uses randomly generated values that follow a specific statistical distribution to model different outcomes. This method allows analysts to quantify and visualize the probability of different scenarios occurring.

The strength of Monte Carlo simulations lies in the insight they offer into potential risks. They allow modelers to see into the probabilistic "what-if" scenarios that more closely mimic real-world conditions.

Monte Carlo Simulations in Tokenomics

Monte Carlo simulations are instrumental tool for token engineers. They're so useful due to their ability to model emergent behaviors. Here are some key areas where these simulations are applied:

Pricing and Valuation of Tokens

Determining the value of a new token can be challenging due to the volatile nature of cryptocurrency markets. Monte Carlo simulations help by modeling various market scenarios and price fluctuations over time, allowing analysts to estimate a token's potential future value under different conditions.

Assessing Market Dynamics and Investor Behavior

Cryptocurrency markets are influenced by a myriad of factors including regulatory changes, technological advancements, and shifts in investor sentiment. Monte Carlo methods allow researchers to simulate these variables in an integrated environment to see how they might impact token economics, from overall market cap fluctuations to liquidity concerns.

Assesing Possible Risks

By running a large number of simulations it’s possible to stress-test the project in multiple scenarios and identify emergent risks. This is perhaps the most important function of Monte Carlo Process, since these risks can’t be assessed any other way.

Source: How to use Monte Carlo simulation for reliability analysis?

Benefits of Using Monte Carlo Simulations

By generating a range of possible outcomes and their probabilities, Monte Carlo simulations help decision-makers in the cryptocurrency space anticipate potential futures and make informed strategic choices. This capability is invaluable for planning token launches, managing supply mechanisms, and designing marketing strategies to optimize market penetration.

Using Monte Carlo simulations, stakeholders in the tokenomics field can not only understand and mitigate risks but also explore the potential impact of different strategic decisions. This predictive power supports more robust economic models and can lead to more stable and successful token launches. 

Implementing Monte Carlo Simulations

Several tools and software packages can facilitate the implementation of Monte Carlo simulations in tokenomics. One of the most notable is cadCAD, a Python library that provides a flexible and powerful environment for simulating complex systems. 

Overview of cadCAD configuration Components

To better understand how Monte Carlo simulations work in practice, let’s take a look at the cadCAD code snippet:

sim_config = {

    'T': range(200),  # number of timesteps

    'N': 3,           # number of Monte Carlo runs

    'M': params       # model parameters

}

Explanation of Simulation Configuration Components

T: Number of Time Steps

  • Definition: The 'T' parameter in CadCAD configurations specifies the number of time steps the simulation should execute. Each time step represents one iteration of the model, during which the system is updated. That update is based on various rules defined by token engineers in other parts of the code. For example: we might assume that one iteration = one day, and define data-based functions that predict token demand on that day.

N: Number of Monte Carlo Runs

  • Definition: The 'N' parameter sets the number of Monte Carlo runs. Each run represents a complete execution of the simulation from start to finish, using potentially different random seeds for each run. This is essential for capturing variability and understanding the distribution of possible outcomes. For example, we can acknowledge that token’s price will be correlated with the broad cryptocurrency market, which acts somewhat unpredictably.

M: Model Parameters

  • Definition: The 'M' key contains the model parameters, which are variables that influence system's behavior but do not change dynamically with each time step. These parameters can be constants or distributions that are used within the policy and update functions to model the external and internal factors affecting the system.

Importance of These Components

Together, these components define the skeleton of your Monte Carlo simulation in CadCAD. The combination of multiple time steps and Monte Carlo runs allows for a comprehensive exploration of the stochastic nature of the modeled system. By varying the number of timesteps (T) and runs (N), you can adjust the depth and breadth of the exploration, respectively. The parameters (M) provide the necessary context and ensure that each simulation is realistic.

Messy graph representing Monte Carlo simulation, source: Bitcoin Monte Carlo Simulation

Conclusion

Monte Carlo simulations represent a powerful analytical tool in the arsenal of token engineers. By leveraging the principles of statistics, these simulations provide deep insights into the complex dynamics of token-based systems. This method allows for a nuanced understanding of potential future scenarios and helps with making informed decisions.

We encourage all stakeholders in the blockchain and cryptocurrency space to consider implementing Monte Carlo simulations. The insights gained from such analytical techniques can lead to more effective and resilient economic models, paving the way for the sustainable growth and success of digital currencies.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

FAQ

What is a Monte Carlo simulation in tokenomics context?

  • It's a mathematical method that uses random sampling to predict uncertain outcomes.

What are the benefits of using Monte Carlo simulations in tokenomics?

  • These simulations help foresee potential market scenarios, aiding in strategic planning and risk management for token launches.

Why are Monte Carlo simulations unique in cryptocurrency analysis?

  • They provide probabilistic outcomes rather than fixed predictions, effectively simulating real-world market variability and risk.

Behavioral Economics in Token Design

Kajetan Olas

22 Apr 2024
Behavioral Economics in Token Design

Behavioral economics is a field that explores the effects of psychological factors on economic decision-making. This branch of study is especially pertinent while designing a token since user perception can significantly impact a token's adoption.

We will delve into how token design choices, such as staking yields, token inflation, and lock-up periods, influence consumer behavior. Research studies reveal that the most significant factor for a token's attractiveness isn’t its functionality, but its past price performance. This underscores the impact of speculative factors. Tokens that have shown previous price increases are preferred over those with more beneficial economic features.

Understanding Behavioral Tokenomics

Understanding User Motivations

The design of a cryptocurrency token can significantly influence user behavior by leveraging common cognitive biases and decision-making processes. For instance, the concept of "scarcity" can create a perceived value increase, prompting users to buy or hold a token in anticipation of future gains. Similarly, "loss aversion," a foundational principle of behavioral economics, suggests that the pain of losing is psychologically more impactful than the pleasure of an equivalent gain. In token design, mechanisms that minimize perceived losses (e.g. anti-dumping measures) can encourage long-term holding.

Incentives and Rewards

Behavioral economics also provides insight into how incentives can be structured to maximize user participation. Cryptocurrencies often use tokens as a form of reward for various behaviors, including mining, staking, or participating in governance through voting. The way these rewards are framed and distributed can greatly affect their effectiveness. For example, offering tokens as rewards for achieving certain milestones can tap into the 'endowment effect,' where people ascribe more value to things simply because they own them.

Social Proof and Network Effects

Social proof, where individuals copy the behavior of others, plays a crucial role in the adoption of tokens. Tokens that are seen being used and promoted by influential figures within the community can quickly gain traction, as new users emulate successful investors. The network effect further amplifies this, where the value of a token increases as more people start using it. This can be seen in the rapid growth of tokens like Ethereum, where the broad adoption of its smart contract functionality created a snowball effect, attracting even more developers and users.

Token Utility and Behavioral Levers

The utility of a token—what it can be used for—is also crucial. Tokens designed to offer real-world applications beyond mere financial speculation can provide more stable value retention. Integrating behavioral economics into utility design involves creating tokens that not only serve practical purposes but also resonate on an emotional level with users, encouraging engagement and investment. For example, tokens that offer governance rights might appeal to users' desire for control and influence within a platform, encouraging them to hold rather than sell.

Understanding Behavioral Tokenomics

Intersection of Behavioral Economics and Tokenomics

Behavioral economics examines how psychological influences, various biases, and the way in which information is framed affect individual decisions. In tokenomics, these factors can significantly impact the success or failure of a cryptocurrency by influencing user behavior towards investment

Influence of Psychological Factors on Token Attraction

A recent study observed that the attractiveness of a token often hinges more on its historical price performance than on intrinsic benefits like yield returns or innovative economic models. This emphasizes the fact that the cryptocurrency sector is still young, and therefore subject to speculative behaviors

The Effect of Presentation and Context

Another interesting finding from the study is the impact of how tokens are presented. In scenarios where tokens are evaluated separately, the influence of their economic attributes on consumer decisions is minimal. However, when tokens are assessed side by side, these attributes become significantly more persuasive. This highlights the importance of context in economic decision-making—a core principle of behavioral economics. It’s easy to translate this into real-life example - just think about the concept of staking yields. When told that the yield on e.g. Cardano is 5% you might not think much of it. But, if you were simultaneously told that Anchor’s yield is 19%, then that 5% seems like a tragic deal.

Implications for Token Designers

The application of behavioral economics to the design of cryptocurrency tokens involves leveraging human psychology to encourage desired behaviors. Here are several core principles of behavioral economics and how they can be effectively utilized in token design:

Leveraging Price Performance

Studies show clearly: “price going up” tends to attract users more than most other token attributes. This finding implies that token designers need to focus on strategies that can showcase their economic effects in the form of price increases. This means that e.g. it would be more beneficial to conduct a buy-back program than to conduct an airdrop.

Scarcity and Perceived Value

Scarcity triggers a sense of urgency and increases perceived value. Cryptocurrency tokens can be designed to have a limited supply, mimicking the scarcity of resources like gold. This not only boosts the perceived rarity and value of the tokens but also drives demand due to the "fear of missing out" (FOMO). By setting a cap on the total number of tokens, developers can create a natural scarcity that may encourage early adoption and long-term holding.

Initial Supply Considerations

The initial supply represents the number of tokens that are available in circulation immediately following the token's launch. The chosen number can influence early market perceptions. For instance, a large initial supply might suggest a lower value per token, which could attract speculators. Data shows that tokens with low nominal value are highly volatile and generally underperform. Understanding how the initial supply can influence investor behavior is important for ensuring the token's stability.

Managing Maximum Supply and Inflation

A finite maximum supply can safeguard the token against inflation, potentially enhancing its value by ensuring scarcity. On the other hand, the inflation rate, which defines the pace at which new tokens are introduced, influences the token's value and user trust.

Investors in cryptocurrency markets show a notable aversion to deflationary tokenomics. Participants are less likely to invest in tokens with a deflationary framework, viewing them as riskier and potentially less profitable. Research suggests that while moderate inflation can be perceived neutrally or even positively, high inflation does not enhance attractiveness, and deflation is distinctly unfavorable.

Source: Behavioral Tokenomics: Consumer Perceptions of Cryptocurrency Token Design

These findings suggest that token designers should avoid high deflation rates, which could deter investment and user engagement. Instead, a balanced approach to inflation, avoiding extremes, appears to be preferred among cryptocurrency investors.

Loss Aversion

People tend to prefer avoiding losses to acquiring equivalent gains; this is known as loss aversion. In token design, this can be leveraged by introducing mechanisms that protect against losses, such as staking rewards that offer consistent returns or features that minimize price volatility. Additionally, creating tokens that users can "earn" through participation or contribution to the network can tap into this principle by making users feel they are safeguarding an investment or adding protective layers to their holdings.

Social Proof

Social proof is a powerful motivator in user adoption and engagement. When potential users see others adopting a token, especially influential figures or peers, they are more likely to perceive it as valuable and trustworthy. Integrating social proof into token marketing strategies, such as showcasing high-profile endorsements or community support, can significantly enhance user acquisition and retention.

Mental Accounting

Mental accounting involves how people categorize and treat money differently depending on its source or intended use. Tokens can be designed to encourage specific spending behaviors by being categorized for certain types of transactions—like tokens that are specifically for governance, others for staking, and others still for transaction fees. By distinguishing tokens in this way, users can more easily rationalize holding or spending them based on their designated purposes.

Endowment Effect

The endowment effect occurs when people value something more highly simply because they own it. For tokenomics, creating opportunities for users to feel ownership can increase attachment and perceived value. This can be done through mechanisms that reward users with tokens for participation or contribution, thus making them more reluctant to part with their holdings because they value them more highly.

Conclusion

By considering how behavioral factors influence market perception, token engineers can create much more effective ecosystems. Ensuring high demand for the token, means ensuring proper funding for the project in general.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

FAQ

How does the initial supply of a token influence its market perception?

  • The initial supply sets the perceived value of a token; a larger supply might suggest a lower per-token value.

Why is the maximum supply important in token design?

  • A finite maximum supply signals scarcity, helping protect against inflation and enhance long-term value.

How do investors perceive inflation and deflation in cryptocurrencies?

  • Investors generally dislike deflationary tokens and view them as risky. Moderate inflation is seen neutrally or positively, while high inflation is not favored.