DAO Tokenomics: Incentivizing participation and collaboration


17 May 2023
DAO Tokenomics: Incentivizing participation and collaboration

In the ever-changing world of blockchain technology, Decentralized Autonomous Organizations (DAOs) have risen as influential models for decentralized cooperation and decision-making. By harnessing the transparent and secure nature of blockchain, AI, and cryptocurrencies, DAOs establish self-regulating communities centered around common objectives and values. Tokenomics, which encompasses the creation and application of token-based economic systems within these organizations, lies at the core of DAOs. This article delves into the importance of tokenomics in DAOs and its crucial role in promoting involvement and encouraging cooperation among community members.

Are you interested in DAO security? Be sure to check out our article on The DAO Hack

DAO tokenomics involves using digital tokens to stimulate and reward ecosystem participants for their contributions. These tokens act as both a medium for exchange and a symbol of value, allowing individuals to partake in the governance, decision-making, and development processes within the DAO. By syncing community members' interests with the organization's success, tokenomics in DAOs serve as a potent tool for instigating active engagement and collaboration.

Understanding Tokenomics in DAOs

DAO tokenomics is a critical aspect in the functioning and management of decentralized autonomous organizations. In this segment, we study the core principles of DAO tokenomics, commencing with an examination of DAO tokens. Symbolizing ownership or membership within a DAO, the digital assets called DAO tokens are spread among participants and hold considerable worth in their ecosystem. They act as a governance mechanism for DAOs by giving holders specific rights, tasks, and decision-making authority.

1. Token Distribution

Several crucial factors should be taken into account when discussing DAO tokenomics. Primarily, token distribution is essential. To disseminate tokens among participants, DAOs utilize various techniques such as token sales, airdrops, or reward-based contribution programs. Establishing a fair and inclusive environment hinges on the equitable allocation of tokens, making certain that everyone has equal chances to engage and contribute.

2. Token utility

Token utility is another vital element, pertaining to the use of DAO tokens within their ecosystem. Such tokens can possess multiple functions – they might work as a medium of exchange or provide access to services and features, or they could represent voting rights. By improving the value and usability of tokens, these utility aspects promote their integration into the community.

3. Governance and Voting

Tokenomics also heavily influences governance and voting mechanisms in DAOs. Token-driven governance models permit holders to partake in decision-making procedures, suggest and vote for proposals, and affect the course of the DAO. Different voting systems and decision-making methods can come into play – from straightforward majority votes to delegated voting setups – based on a specific DAO's layout and goals.

4. Incentive Mechanisms

Moreover, incentive structures are central components of DAO tokenomics that encourage active involvement and cooperation within the community. Those who devote their time, resources, or knowledge to a DAO are often rewarded accordingly. Incentivizing active engagement results in a dynamic ecosystem where members are inclined to collaborate and strive toward shared objectives.

Benefits of Effective Tokenomics in DAOs

A variety of advantages arise from efficient tokenomics in DAOs, contributing to the flourishing and expansion of these decentralized entities. Enhanced community involvement and engagement is a notable benefit. DAOs can cultivate a feeling of membership and responsibility among individuals by developing tokenomics that reward substantial contributions and active participation. This encourages members to actively offer their talents, expertise, and assets, knowing they will be acknowledged and compensated. Such intensified involvement results in a thriving and energetic ecosystem where community members join forces, exchange thoughts, and strive towards shared objectives. Moreover, DAO tokenomics allows for effective resource distribution. Moreover, through utilizing tokens as a means for funding and managing resources, DAOs can allocate resources in a transparent, decentralized manner. This guarantees the best use of funds and input, enabling the DAO to carry out projects adeptly, create new features, and foster innovation.

Successful DAO Tokenomics Models

1. MakerDAO

One of the most prominent DAOs in existence, MakerDAO, employs a unique dual-token model that has contributed to its success. The system includes the Maker (MKR) token and the DAI stablecoin.

MKR tokens serve governance purposes, enabling holders to cast votes on proposals, such as adjustments to the system's parameters. Additionally, the tokenomics of MKR aims to promote responsible governance. As the system operates efficiently, MKR holders reap benefits due to a decrease in MKR's total supply through a process known as "burning." Conversely, during times when the system

DAI, on the other hand, is a stablecoin pegged to the US dollar. It's generated by locking up collateral in the form of other crypto assets. This dual-token model has proven successful, ensuring stability in the system and encouraging active participation from its members.

2. Aragon

Aragon is a platform that allows users to create and manage their DAOs. It uses the Aragon Network Token (ANT), a utility token that provides holders with voting rights within the Aragon network.

Aragon's tokenomics model is centered around the concept of decentralization and democracy. ANT token holders can vote on various aspects, such as changes to the network's settings and dispute resolution. This creates a self-sustainable ecosystem where the community directly influences the platform's direction and future development.

3. Compound

Compound is a decentralized lending platform governed by its users through the COMP token. In this DAO, users earn COMP tokens as they interact with the platform, borrowing, or lending assets.

The Compound's tokenomics model has been designed to distribute governance power proportionally to those who use the platform the most. COMP tokens give holders the right to propose and vote on changes to the Compound protocol. This model has been successful because it ensures that those who are most invested in and knowledgeable about the platform have the most significant say in its operation and future direction.

4. Yearn.Finance

Yearn.Finance represents a paradigm shift in the way DAO tokenomics models are structured. This platform aims to simplify the ever-growing DeFi space for investors by automating yield farming strategies. At the core of its governance is the YFI token.

Yearn.Finance's success lies in its unique approach to token distribution, incentives for holding tokens, active community participation, and a founder committed to the platform's success. Its tokenomics model ensures that the platform remains decentralized, democratic, and in the best interest of its most active users. This case study highlights how an innovative approach to DAO tokenomics can lead to a successful, thriving ecosystem in the DeFi space.


To sum up, DAO tokenomics is critical in motivating engagement and cooperation within decentralized autonomous organizations. Through the usage of tokens for value exchange, governance, and incentive structures, DAOs can foster dynamic communities in which members actively participate and pursue shared objectives. The core components of successful DAO tokenomics include token distribution, token utility, governance and voting systems, and incentives. Nevertheless, ongoing challenges such as decentralization, sustainability, and legal concerns must be tackled as this field continues to progress.

Do you need the help of specialists to create a tokenomy? Contact us!


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Applying Game Theory in Token Design

Kajetan Olas

16 Apr 2024
Applying Game Theory in Token Design

Blockchain technology allows for aligning incentives among network participants by rewarding desired behaviors with tokens.
But there is more to it than simply fostering cooperation. Game theory allows for designing incentive-machines that can't be turned-off and resemble artificial life.

Emergent Optimization

Game theory provides a robust framework for analyzing strategic interactions with mathematical models, which is particularly useful in blockchain environments where multiple stakeholders interact within a set of predefined rules. By applying this framework to token systems, developers can design systems that influence the emergent behaviors of network participants. This ensures the stability and effectiveness of the ecosystem.

Bonding Curves

Bonding curves are tool used in token design to manage the relationship between price and token supply predictably. Essentially, a bonding curve is a mathematical curve that defines the price of a token based on its supply. The more tokens that are bought, the higher the price climbs, and vice versa. This model incentivizes early adoption and can help stabilize a token’s economy over time.

For example, a bonding curve could be designed to slow down price increases after certain milestones are reached, thus preventing speculative bubbles and encouraging steadier, more organic growth.

The Case of Bitcoin

Bitcoin’s design incorporates game theory, most notably through its consensus mechanism of proof-of-work (PoW). Its reward function optimizes for security (hashrate) by optimizing for maximum electricity usage. Therefore, optimizing for its legitimate goal of being secure also inadvertently optimizes for corrupting natural environment. Another emergent outcome of PoW is the creation of mining pools, that increase centralization.

The Paperclip Maximizer and the dangers of blockchain economy

What’s the connection between AI from the story and decentralized economies? Blockchain-based incentive systems also can’t be turned off. This means that if we design an incentive system that optimizes towards a wrong objective, we might be unable to change it. Bitcoin critics argue that the PoW consensus mechanism optimizes toward destroying planet Earth.

Layer 2 Solutions

Layer 2 solutions are built on the understanding that the security provided by this core kernel of certainty can be used as an anchor. This anchor then supports additional economic mechanisms that operate off the blockchain, extending the utility of public blockchains like Ethereum. These mechanisms include state channels, sidechains, or plasma, each offering a way to conduct transactions off-chain while still being able to refer back to the anchored security of the main chain if necessary.

Conceptual Example of State Channels

State channels allow participants to perform numerous transactions off-chain, with the blockchain serving as a backstop in case of disputes or malfeasance.

Consider two players, Alice and Bob, who want to play a game of tic-tac-toe with stakes in Ethereum. The naive approach would be to interact directly with a smart contract for every move, which would be slow and costly. Instead, they can use a state channel for their game.

  1. Opening the Channel: They start by deploying a "Judge" smart contract on Ethereum, which holds the 1 ETH wager. The contract knows the rules of the game and the identities of the players.
  2. Playing the Game: Alice and Bob play the game off-chain by signing each move as transactions, which are exchanged directly between them but not broadcast to the blockchain. Each transaction includes a nonce to ensure moves are kept in order.
  3. Closing the Channel: When the game ends, the final state (i.e., the sequence of moves) is sent to the Judge contract, which pays out the wager to the winner after confirming both parties agree on the outcome.

A threat stronger than the execution

If Bob tries to cheat by submitting an old state where he was winning, Alice can challenge this during a dispute period by submitting a newer signed state. The Judge contract can verify the authenticity and order of these states due to the nonces, ensuring the integrity of the game. Thus, the mere threat of execution (submitting the state to the blockchain and having the fraud exposed) secures the off-chain interactions.

Game Theory in Practice

Understanding the application of game theory within blockchain and token ecosystems requires a structured approach to analyzing how stakeholders interact, defining possible actions they can take, and understanding the causal relationships within the system. This structured analysis helps in creating effective strategies that ensure the system operates as intended.

Stakeholder Analysis

Identifying Stakeholders

The first step in applying game theory effectively is identifying all relevant stakeholders within the ecosystem. This includes direct participants such as users, miners, and developers but also external entities like regulators, potential attackers, and partner organizations. Understanding who the stakeholders are and what their interests and capabilities are is crucial for predicting how they might interact within the system.

Stakeholders in blockchain development for systems engineering

Assessing Incentives and Capabilities

Each stakeholder has different motivations and resources at their disposal. For instance, miners are motivated by block rewards and transaction fees, while users seek fast, secure, and cheap transactions. Clearly defining these incentives helps in predicting how changes to the system’s rules and parameters might influence their behaviors.

Defining Action Space

Possible Actions

The action space encompasses all possible decisions or strategies stakeholders can employ in response to the ecosystem's dynamics. For example, a miner might choose to increase computational power, a user might decide to hold or sell tokens, and a developer might propose changes to the protocol.

Artonomus, Github

Constraints and Opportunities

Understanding the constraints (such as economic costs, technological limitations, and regulatory frameworks) and opportunities (such as new technological advancements or changes in market demand) within which these actions take place is vital. This helps in modeling potential strategies stakeholders might adopt.

Artonomus, Github

Causal Relationships Diagram

Mapping Interactions

Creating a diagram that represents the causal relationships between different actions and outcomes within the ecosystem can illuminate how complex interactions unfold. This diagram helps in identifying which variables influence others and how they do so, making it easier to predict the outcomes of certain actions.

Artonomus, Github

Analyzing Impact

By examining the causal relationships, developers and system designers can identify critical leverage points where small changes could have significant impacts. This analysis is crucial for enhancing system stability and ensuring its efficiency.

Feedback Loops

Understanding feedback loops within a blockchain ecosystem is critical as they can significantly amplify or mitigate the effects of changes within the system. These loops can reinforce or counteract trends, leading to rapid growth or decline.

Reinforcing Loops

Reinforcing loops are feedback mechanisms that amplify the effects of a trend or action. For example, increased adoption of a blockchain platform can lead to more developers creating applications on it, which in turn leads to further adoption. This positive feedback loop can drive rapid growth and success.

Death Spiral

Conversely, a death spiral is a type of reinforcing loop that leads to negative outcomes. An example might be the increasing cost of transaction fees leading to decreased usage of the blockchain, which reduces the incentive for miners to secure the network, further decreasing system performance and user adoption. Identifying potential death spirals early is crucial for maintaining the ecosystem's health.

The Death Spiral: How Terra's Algorithmic Stablecoin Came Crashing Down
the-death-spiral-how-terras-algorithmic-stablecoin-came-crashing-down/, Forbes


The fundamental advantage of token-based systems is being able to reward desired behavior. To capitalize on that possibility, token engineers put careful attention into optimization and designing incentives for long-term growth.


  1. What does game theory contribute to blockchain token design?
    • Game theory optimizes blockchain ecosystems by structuring incentives that reward desired behavior.
  2. How do bonding curves apply game theory to improve token economics?
    • Bonding curves set token pricing that adjusts with supply changes, strategically incentivizing early purchases and penalizing speculation.
  3. What benefits do Layer 2 solutions provide in the context of game theory?
    • Layer 2 solutions leverage game theory, by creating systems where the threat of reporting fraudulent behavior ensures honest participation.

Token Engineering Process

Kajetan Olas

13 Apr 2024
Token Engineering Process

Token Engineering is an emerging field that addresses the systematic design and engineering of blockchain-based tokens. It applies rigorous mathematical methods from the Complex Systems Engineering discipline to tokenomics design.

In this article, we will walk through the Token Engineering Process and break it down into three key stages. Discovery Phase, Design Phase, and Deployment Phase.

Discovery Phase of Token Engineering Process

The first stage of the token engineering process is the Discovery Phase. It focuses on constructing high-level business plans, defining objectives, and identifying problems to be solved. That phase is also the time when token engineers first define key stakeholders in the project.

Defining the Problem

This may seem counterintuitive. Why would we start with the problem when designing tokenomics? Shouldn’t we start with more down-to-earth matters like token supply? The answer is No. Tokens are a medium for creating and exchanging value within a project’s ecosystem. Since crypto projects draw their value from solving problems that can’t be solved through TradFi mechanisms, their tokenomics should reflect that. 

The industry standard, developed by McKinsey & Co. and adapted to token engineering purposes by Outlier Ventures, is structuring the problem through a logic tree, following MECE.
MECE stands for Mutually Exclusive, Collectively Exhaustive. Mutually Exclusive means that problems in the tree should not overlap. Collectively Exhaustive means that the tree should cover all issues.

In practice, the “Problem” should be replaced by a whole problem statement worksheet. The same will hold for some of the boxes.
A commonly used tool for designing these kinds of diagrams is the Miro whiteboard.

Identifying Stakeholders and Value Flows in Token Engineering

This part is about identifying all relevant actors in the ecosystem and how value flows between them. To illustrate what we mean let’s consider an example of NFT marketplace. In its case, relevant actors might be sellers, buyers, NFT creators, and a marketplace owner. Possible value flow when conducting a transaction might be: buyer gets rid of his tokens, seller gets some of them, marketplace owner gets some of them as fees, and NFT creators get some of them as royalties.

Incentive Mechanisms Canvas

The last part of what we consider to be in the Discovery Phase is filling the Incentive Mechanisms Canvas. After successfully identifying value flows in the previous stage, token engineers search for frictions to desired behaviors and point out the undesired behaviors. For example, friction to activity on an NFT marketplace might be respecting royalty fees by marketplace owners since it reduces value flowing to the seller.

source: https://www.canva.com/design/DAFDTNKsIJs/8Ky9EoJJI7p98qKLIu2XNw/view#7

Design Phase of Token Engineering Process

The second stage of the Token Engineering Process is the Design Phase in which you make use of high-level descriptions from the previous step to come up with a specific design of the project. This will include everything that can be usually found in crypto whitepapers (e.g. governance mechanisms, incentive mechanisms, token supply, etc). After finishing the design, token engineers should represent the whole value flow and transactional logic on detailed visual diagrams. These diagrams will be a basis for creating mathematical models in the Deployment Phase. 

Token Engineering Artonomous Design Diagram
Artonomous design diagram, source: Artonomous GitHub

Objective Function

Every crypto project has some objective. The objective can consist of many goals, such as decentralization or token price. The objective function is a mathematical function assigning weights to different factors that influence the main objective in the order of their importance. This function will be a reference for machine learning algorithms in the next steps. They will try to find quantitative parameters (e.g. network fees) that maximize the output of this function.
Modified Metcalfe’s Law can serve as an inspiration during that step. It’s a framework for valuing crypto projects, but we believe that after adjustments it can also be used in this context.

Deployment Phase of Token Engineering Process

The Deployment Phase is final, but also the most demanding step in the process. It involves the implementation of machine learning algorithms that test our assumptions and optimize quantitative parameters. Token Engineering draws from Nassim Taleb’s concept of Antifragility and extensively uses feedback loops to make a system that gains from arising shocks.

Agent-based Modelling 

In agent-based modeling, we describe a set of behaviors and goals displayed by each agent participating in the system (this is why previous steps focused so much on describing stakeholders). Each agent is controlled by an autonomous AI and continuously optimizes his strategy. He learns from his experience and can mimic the behavior of other agents if he finds it effective (Reinforced Learning). This approach allows for mimicking real users, who adapt their strategies with time. An example adaptive agent would be a cryptocurrency trader, who changes his trading strategy in response to experiencing a loss of money.

Monte Carlo Simulations

Token Engineers use the Monte Carlo method to simulate the consequences of various possible interactions while taking into account the probability of their occurrence. By running a large number of simulations it’s possible to stress-test the project in multiple scenarios and identify emergent risks.

Testnet Deployment

If possible, it's highly beneficial for projects to extend the testing phase even further by letting real users use the network. Idea is the same as in agent-based testing - continuous optimization based on provided metrics. Furthermore, in case the project considers airdropping its tokens, giving them to early users is a great strategy. Even though part of the activity will be disingenuine and airdrop-oriented, such strategy still works better than most.

Time Duration

Token engineering process may take from as little as 2 weeks to as much as 5 months. It depends on the project category (Layer 1 protocol will require more time, than a simple DApp), and security requirements. For example, a bank issuing its digital token will have a very low risk tolerance.

Required Skills for Token Engineering

Token engineering is a multidisciplinary field and requires a great amount of specialized knowledge. Key knowledge areas are:

  • Systems Engineering
  • Machine Learning
  • Market Research
  • Capital Markets
  • Current trends in Web3
  • Blockchain Engineering
  • Statistics


The token engineering process consists of 3 steps: Discovery Phase, Design Phase, and Deployment Phase. It’s utilized mostly by established blockchain projects, and financial institutions like the International Monetary Fund. Even though it’s a very resource-consuming process, we believe it’s worth it. Projects that went through scrupulous design and testing before launch are much more likely to receive VC funding and be in the 10% of crypto projects that survive the bear market. Going through that process also has a symbolic meaning - it shows that the project is long-term oriented.

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.


What does token engineering process look like?

  • Token engineering process is conducted in a 3-step methodical fashion. This includes Discovery Phase, Design Phase, and Deployment Phase. Each of these stages should be tailored to the specific needs of a project.

Is token engineering meant only for big projects?

  • We recommend that even small projects go through a simplified design and optimization process. This increases community's trust and makes sure that the tokenomics doesn't have any obvious flaws.

How long does the token engineering process take?

  • It depends on the project and may range from 2 weeks to 5 months.