# āBenefits of Reputation-Based Governance

Traditional DAO Governance VS Reputation-Based DAO Governance

### Introduction

As explained previously, existing DAO governance models are based on capital, while EtherScore develop new paradigms based on onchain reputations to **empower users to govern the app they use**.

In 1-token-1-vote DAOs, anyone can buy votes and entities are fighting each other to accumulate voting power, while in EtherScore, your legitimacy and contributions will give you voting power.

In traditional DAO governance models, the total sum of voting power is fixed and based on the maximum supply of tokens. In the case of EtherScore, this voting power evolves in proportion to the number of users, resulting in a continuous dilution of the voting power held by existing users. Unlike traditional models, where large capital owners can maintain their percentage of voting power, EtherScore imposes a dynamic where maintaining the percentage of voting power requires continuous activity from users to accumulate badges. In EtherScore's model, the percentage of voting power a user represents is subject to an upper bound determined by the maximum number of badges they can obtain. Additionally, unlike traditional models, the accumulation of voting power in EtherScore increases the total sum dynamically rather than concentrating it. To maintain their voting power percentage, users need to remain active by regularly claiming badges.

The percentage of voting power plays a crucial role, often used as the minimum threshold (quorum) for proposing or blocking votes in DAOs. For example, in platforms such as Uniswap, a certain percentage of the total supply of tokens is required to initiate a vote. In the event of a malicious attempt to acquire this quorum and potentially attack the DAO, traditional solutions often involve blocking the DAO, veto rights, updating the code or issuing new tokens to increase supply and thus raise the required quorum. In EtherScore, the evolutive distribution of voting power according to the total number of users makes this process fairer and more resilient, reducing the risk of exploitation by malicious actors.

Here are a few formulas to help illustrate the effectiveness of our new model of governance:

### Maximal Gap Between 2 voters:

#### Traditional DAOs:

In existing DAO models, 1 token = 1 vote, and there is generally no minimum voting requirement. Let $P_{max}$ and $P_{min}$ be the maximum and minimum voting powers respectively, $S$ the maximum supply of tokens, and $d$ the number of token decimal places:

The theoretical maximum gap between two voters, $G_{max}$, is given by:

Traditional governance tokens, based mainly on the ERC20 standard, have 18 decimal places. The maximum theoretical difference between the smallest and largest voter is therefore close to the total number of tokens available, without considering financial aspects, circulating supply, etc.

#### EtherScore Model:

In the EtherScore DAO, 1 vote=1 badge, and the minimum to vote is equal to 1 badge. Let $P_{max}$ and $P_{min}$ be the maximum and minimum voting powers respectively, and $B_{max}$ the maximum number of badges a user can claim in EtherScore:

The theoretical maximum gap between two voters, $G_{max}$, is given by:

EtherScore's approach favors a distribution of voting power that reduces the initial concentration of power among a few large holders, which is common in traditional models based on representation proportional to the number of tokens held.

**Concentration and Voting Power Distribution:**

**Concentration and Voting Power Distribution:**

#### Traditional DAO models:

Total voting power $P_{MT}$ remains constant, determined by the initial supply of tokens. Each user contributes to the total voting power according to the quantity of tokens they hold, and this percentage does not vary according to the number of users or platform activity:

The total sum of voting power $P_{MT}$ is fixed and based on the maximum supply of tokens $S$.

Each user $i$ in $MT$ has a voting power $p_i^{MT}$.

The percentage of total voting power that user $i$ represents is given by $\frac{p_i^{MT}}{P_{MT}}$.

This formulation highlights the fundamental difference with EtherScore, where voting power evolves dynamically according to the number of badges accumulated, offering a more flexible and adaptive approach to decentralized governance.

#### EtherScore DAO Model:

Each user $j$ holds a number of badges $B_j$.

The voting power $p_j^{ES}$ of a user $j$ is equal to the number of badges they hold: $P_j^{ES} = B_j$.

The total voting power $P_{ES}$ in EtherScore is the sum of all badges held by users: $P_{ES} = \sum_{j=1}^{N} B_j$ where $N$ is the total number of users.

The percentage of total voting power that a user $j$ represents is then: $\frac{p_j^{ES}}{P_{ES}} = \frac{B_j}{\sum_{j=1}^{N} B_j}$.

This formula shows that in EtherScore, each user contributes to the total voting power in proportion to the number of badges they hold. This dynamic model allows for a continuous evolution of total voting power as a function of user activity, making the governance process more inclusive and resilient to attempts at manipulation or excessive concentration of power.

### Future improvements: Empirical analysis of major DAOs

To assess the effectiveness of our model, we will conduct an empirical analysis on various DAOs using the formulas described above. While we have already performed basic comparisons regarding the maximal gap and the concentration of top voters, further refinements are needed on datasets to account for different factors such as delegations, circulating supply, and votable supply.

However, our research already indicates that **reputation-based governance models exhibit significantly lower percentages of voting power concentration and a smaller maximal gap between voters**.

Last updated