Data-Driven Token Design and Optimization

Outlier Ventures recently released a video that delves into the sustainability issues of the Token ecosystem. The video emphasizes the principles and methods of Token engineering, providing a new perspective for building robust Token systems. It also introduces a range of practical tools, such as agent-based simulation tools and quantitative Token models like (QTM), which can provide valuable insights at different stages of a project to help make informed decisions.

The video content reveals the key role of Token engineering and related tools in adapting projects to changes, which have proven to be powerful weapons in coping with the ever-evolving Token ecosystem. This understanding stems from in-depth research and practice of the Token ecosystem, enabling participants to better understand the dynamics of the ecosystem and make more informed, forward-looking decisions.

Three Stages of Token Design and Optimization

Discovery Phase

Building a successful token ecosystem requires executing several key steps at a macro level:

1. Clearly define the problem and challenges faced.
2. Clarify the flow of value among stakeholders.
3. In-depth discussion on the rationale of the ecosystem and its Token
4. Develop a high-level plan, including the Token usage and various content design schemes.

These steps are crucial for building a successful Token ecosystem.

Design Phase

Parameterization is another key step in the construction of the Token ecosystem, involving the application of quantitative tools such as spreadsheets, simulation tools (cadCAD, Token Spice, Machinations, etc. ). These tools can help obtain optimized and validated models, conduct risk analysis and forecasting, and gain insights into Token supply and valuation trends. Through these quantitative tools, a better understanding of ecosystem operations can be achieved, providing strong support for its design and optimization.

Deployment Phase

The deployment phase puts the preliminary theoretical analysis and design into practice, deploying the ecosystem onto the blockchain. This phase requires the use of various tools, including different programming languages like Solidity, Rust, and deployment environments such as Hardhat. Through this process, a real ecosystem Token or product is ultimately produced, allowing it to truly function and operate on the blockchain.

Token Design Tool

In different stages of discovery, design, and deployment of (, a series of tools need to be used, which vary in focus and type across different fields. These tools are applicable not only in the DeFi space but also in various application projects, infrastructure, gaming, and other areas.

From a qualitative perspective, using market standards is sufficient when looking at ecosystems, and no simulations are needed. Another viewpoint suggests that it is necessary to create digital twins to simulate the entire ecosystem 1:1, as this involves significant financial risks. As we move towards greater precision, the required programming knowledge will increase, along with the demands placed on users.

![Outlier Ventures: Data-Driven Token Design and Optimization])https://img-cdn.gateio.im/webp-social/moments-44a07d89e581fbc1ec48304130f7388d.webp(

There are various tools in the Token ecosystem that can help understand and design the system:

• Spreadsheet models and qualitative tools ) such as problem statements, stakeholder problem statements, stakeholder mapping, etc. (
• AI-driven reasoning ) such as machine learning models drafting initial Token design (
• QTM) Quantitative Token Model (
• Simulation tools ) such as cadCAD etc. (

Choosing the right tools and methods is crucial for the success of startups. Different types of tools can provide valuable information at different stages, helping businesses make informed decisions and promoting the sustainable development of the ecosystem.

) QTM Overview

QTM is a quantitative Token model that uses a fixed simulation period of 10 years, with each time step being one month. The model includes an incentive module, a Token ownership module, an airdrop module, and more. Tokens are distributed from these modules into several meta buckets, followed by a more refined general utility redistribution. The model also takes into account off-chain business aspects, such as funding status, burn or buyback, user adoption rate, and more.

It is important to emphasize that the quality of QTM output depends on the quality of the input. Adequate market research must be conducted beforehand to obtain accurate input information. QTM is regarded as an educational tool for early-stage startups, helping to gain a preliminary understanding of the ecosystem, but no financial advice should be derived from it or solely rely on its results.

( Data Analysis

From a data analysis perspective, different types of data can be extracted:

1. Macroeconomic Market Perspective: Observe the overall market development situation
2. Fundraising Round Indicators: Understand the project's financing situation
3. Participant Behavior Patterns: In-depth Understanding of Investment Habits
4. On-chain data: Obtain metrics such as user growth, TVL, trading volume, etc.
5. Social Media Data: Analyzing information on platforms such as Twitter, Reddit, Discord, and Telegram.

These open and valuable data should be fully utilized to better understand ecosystem parameters and validate models.

![Outlier Ventures: Data-Driven Token Design and Optimization])https://img-cdn.gateio.im/webp-social/moments-d44dc739d197fa1ef14d72cc7b8dd11d.webp###

For example, data on the creation of vesting periods can be analyzed to observe the vesting periods of different stakeholder groups. All transactions in the ecosystem can also be tracked and categorized into specific "token buckets," such as project-related addresses, centralized exchange addresses, and decentralized exchange addresses. In this way, the balance of each stakeholder can be viewed, and the ongoing activities in the entire ecosystem can be observed.

In addition, it is also possible to analyze the behavior of specific addresses to understand the liquidity situation of tokens. For example, when tokens are sent from a staking contract to a specific address, one can observe how the recipient handles these tokens. This information helps to understand the behavior of each stakeholder and can feed the data back into the model for adjustments.

Using this data, predictions can be made, such as forecasting the balance supply situation of different buckets in the ecosystem over the next decade, including the foundation, team, staking distribution, overall circulating supply, and liquidity pools. At the same time, price simulations or forecasts can also be conducted. These predictions help to understand the relationship between supply attribution and Token demand, and to grasp the balance between the two factors.

![Outlier Ventures: Data-Driven Token Design and Optimization]###https://img-cdn.gateio.im/webp-social/moments-a6b71e12aa773a7e432108b8a646dc5e.webp(

) Data-driven model

A new way of thinking about the vesting plan is to introduce an adjusted token vesting mechanism that is not influenced by market demand. The vesting release will be controlled by the controller based on certain predefined key performance indicators, which can include TVL, trading volume, user adoption rate, business profitability, and more.

In the model, three different demand scenarios can be simulated: logical functions, linear functions, and exponential growth. The controller manages different emission levels at different points in time, allowing for observation of the changes in release amounts under each different growth and demand scenario.

When the Token price rises, more Tokens will be released into the ecosystem, which may lead early investors to sell their Tokens, causing the price to drop. Conversely, when the price falls below a preset price, the issuance of Tokens will decrease. Through this control mechanism, the Token price will rise again, ultimately reducing volatility and stabilizing the ecosystem.

In addition, different weighted allocations can be applied to the attributions. For example, during the initial stage, ecosystem incentives may receive a larger allocation of tokens, while the team gets a smaller share. Over time, the situation may change as we aim to establish a sustainable growth model, rather than just relying on token attributions to drive the development of the ecosystem.

![Outlier Ventures: Data-Driven Token Design and Optimization]###https://img-cdn.gateio.im/webp-social/moments-435bd1b0ace052cfa82c2a225a653786.webp(

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