📌 Token Distribution Models Summary
Token distribution models are methods used to decide how digital tokens are given out to participants in a blockchain or cryptocurrency project. These models outline who gets tokens, how many they receive, and when they are distributed. Common approaches include airdrops, sales, mining rewards, or allocations for team members and investors. The chosen model can affect the fairness, security, and long-term success of a project.
🙋🏻♂️ Explain Token Distribution Models Simply
Imagine a group of friends baking a cake together and deciding how to share it. Token distribution models are like the different ways they could slice and hand out the cake, such as giving everyone an equal piece or rewarding those who helped the most. The way the cake is divided can influence how happy everyone is and whether they want to bake together again.
📅 How Can it be used?
A project could use a token distribution model to reward early adopters and contributors while ensuring fair access for new users.
🗺️ Real World Examples
The Ethereum network used an initial coin offering (ICO) to distribute its Ether tokens. Early supporters could buy tokens before the network launched, helping raise funds for development while distributing tokens widely among users and investors.
Uniswap, a decentralised exchange, distributed its UNI governance tokens through an airdrop to anyone who had used the platform before a certain date, rewarding early users and encouraging ongoing community participation.
✅ FAQ
📚 Categories
🔗 External Reference Links
Token Distribution Models link
👏 Was This Helpful?
If this page helped you, please consider giving us a linkback or share on social media!
📎 https://www.efficiencyai.co.uk/knowledge_card/token-distribution-models-2
Ready to Transform, and Optimise?
At EfficiencyAI, we don’t just understand technology — we understand how it impacts real business operations. Our consultants have delivered global transformation programmes, run strategic workshops, and helped organisations improve processes, automate workflows, and drive measurable results.
Whether you're exploring AI, automation, or data strategy, we bring the experience to guide you from challenge to solution.
Let’s talk about what’s next for your organisation.
💡Other Useful Knowledge Cards
3D Printing Tech
3D printing technology is a manufacturing process that creates three-dimensional objects by building them layer by layer from digital designs. It uses materials like plastics, metals, or resins, which are deposited or solidified according to precise instructions. This method allows for rapid prototyping, customisation, and small-scale production without the need for traditional moulds or tools.
Regression Sets
Regression sets are collections of test cases used to check that recent changes in software have not caused any existing features or functions to stop working as expected. They help ensure that updates, bug fixes, or new features do not introduce new errors into previously working areas. These sets are usually run automatically and are a key part of quality assurance in software development.
Agent Scaling Strategies
Agent scaling strategies refer to methods used to increase the number or capability of software agents, such as chatbots or automated assistants, so they can handle more tasks or users at once. These strategies might involve distributing agents across multiple servers, optimising their performance, or coordinating many agents to work together efficiently. The goal is to ensure that as demand grows, the system remains reliable and responsive.
Data Validation Framework
A data validation framework is a set of tools, rules, or processes that checks data for accuracy, completeness, and format before it is used or stored. It helps make sure that the data being entered or moved between systems meets specific requirements set by the organisation or application. By catching errors early, a data validation framework helps prevent problems caused by incorrect or inconsistent data.
Fairness-Aware Machine Learning
Fairness-Aware Machine Learning refers to developing and using machine learning models that aim to make decisions without favouring or discriminating against individuals or groups based on sensitive characteristics such as gender, race, or age. It involves identifying and reducing biases that can exist in data or algorithms to ensure fair outcomes for everyone affected by the model. This approach is important for building trust and preventing unfair treatment in automated systems used in areas like hiring, lending, and healthcare.