Maximize Your Earnings: Why Reputation is Key in Codatta

The community generally recognizes and appreciates the Points system on our platform, but we have also identified issues with data quality and some users manipulating the system through technical means. From the inception of our data protocol, we introduced the reputation mechanism to ensure the sustainable and healthy development of our data protocol and community. It is essential to emphasize the importance of reputation.

In essence, Reputation not only describes the quality of the data you contribute but also directly impacts your earnings. The rewards for data contribution (including airdrop allocations and subsequent revenue sharing) can be calculated using a simple formula: rewards = points x reputation. Below, we will delve into the role of reputation, its components, and relevant case studies.

Why Reputation Matters?

Codatta is an innovative decentralized data protocol designed to facilitate data creation and aggregation. Through community co-building and multi-party collaboration, Codatta provides high-quality metadata services. In decentralized data services and Web3 protocols, a reputation system is a crucial component for building trust, ensuring data quality, and incentivizing positive behaviors.

In the early stages of the project, we conducted a simulation experiment by randomly mixing users of varying expertise levels into a tagging community. These users were then asked to perform semantic annotations for different crypto addresses based on transaction behaviors. The experiment aimed to determine the number of votes required to achieve usable data quality in the protocol, such as data accuracy exceeding 90%, and to assess whether weighted voting could enhance data precision. We made two interesting discoveries:

1. Even with a low proportion of experts in the group, as long as the number of voters reached a certain threshold, the accuracy of the data labels could meet professional standards. This aligns with the case of estimating the weight of an ox in “The Wisdom of Crowds.”
2. Combining the reputation voting system allowed us to achieve usable data accuracy with fewer votes. Additionally, in subsequent adversarial experiments, we found that the reputation voting system had a stronger capability to withstand attacks. Through real-time adjustments based on behavior, we demoted or even excluded malicious actors.

As the number of protocol participants increases, ensuring the reliability and accuracy of data becomes crucial. The reputation system records and evaluates the behavior and contributions of participants, providing a reliable way for the community to identify and reward high-quality contributors while punishing low-quality or malicious behavior. This not only helps to enhance overall data quality but also fosters trust and collaboration among community members, laying a solid foundation for the long-term health and development of the decentralized ecosystem.

Understanding Reputation in Codatta

In the Codatta protocol, reputation is a crucial metric for assessing the quality of data provided by participants (contributors and validators). The reputation system evaluates participants’ performance in data services and feedback through four sub-dimensions: data contribution, staking, identity verification, and annotation skill level.

These dimensions are combined into a unified reputation score, determining a node’s weight and reward allocation within the protocol. High-reputation nodes receive more incentives and have greater influence in community governance. Conversely, malicious behavior results in lower reputation scores, and offenders may even be excluded from the community. This approach ensures data quality and reliability in Codatta, encourages active participation from community members, and ultimately promotes the healthy development of the entire ecosystem.

The Reputation Score in Codatta is based on four aspects: credential strength, platform skill level, contribution records, and staking. The diagram above illustrates the corresponding importance of each aspect. Data contribution includes submitting, reviewing, and the quality of the corresponding data. Staking, available before the TGE, aims to enhance data contributors’ sense of responsibility towards data and the entire community. Identity information encompasses but is not limited to email, social platform bindings, and robust authentication actions such as KYC and KYB. Platform skill level reflects users’ abilities, activity levels, and success rates in various data annotation tasks on the platform.

The calculation and updating of the Reputation Score promptly reflect user behaviors. Each dimension’s score depends on the completeness and quality of the provided information. Hence, providing more comprehensive information and ensuring higher data quality will result in higher scores in the corresponding dimensions. The backend monitors and handles malicious behavior, with penalties including downgrading or even resetting scores for continuous low-quality or malicious submissions. In the future, token-holding community members will have the authority to maintain, propose, and modify the respective coefficients.

To ensure the data activity within the entire protocol, we have introduced a decay factor specifically for data contributions. The decay factor considers data submission behavior and platform engagement. The approximate calculation logic for the decay factor is: reputation-score-contribution’ = d * reputation-score-contribution, where d is a Gaussian function, exemplified by the yellow strip in the two-dimensional coordinate diagram.

Real-Life Reputation User Cases

Case #1：

• High reputation score with excellent performance across various metrics.
• The scores for identity verification and staking are relatively low, considering that currently, identity verification only supports email binding and the staking feature has not yet been launched.

Case #2：

• Low reputation score.
• Despite the large amount of data submission and verification, most of it is incorrect, and the user has had very few login days, indicating a high likelihood of spamming data.

Case #3：

• Medium reputation score.
• Frequently login into the platform, primarily to complete quests. Although the quantity of data contribution is not high, the quality is fairly good.

Reputation Update Mechanism

1. The reputation earned from quest incentives will take effect in real time.
2. Additionally, the overall reputation, which considers all user information, will be updated daily.

Conclusion

Codatta is an innovative decentralized data protocol designed to provide high-quality metadata services through community co-building and multi-party collaboration. In decentralized data services and Web3 protocols, a reputation system is a crucial component for building trust, ensuring data quality, and incentivizing positive behaviors. Through simulation experiments, we found that even with a low proportion of experts, as long as the number of voters reaches a certain threshold, the accuracy of data labels can meet professional standards. Combined with a reputation-based voting system, we can achieve high-precision data with fewer votes and enhance resistance to attacks.

In the Codatta protocol, reputation is a crucial metric for assessing the quality of data provided by participants (contributors and validators). The reputation system evaluates participants’ performance across four dimensions: data contribution, staking, identity verification, and annotation skill level. High-reputation nodes receive more incentives and have greater influence on community governance, while malicious behavior will be penalized. This ensures data quality and reliability, promotes active participation from community members, and ultimately drives the healthy development of the entire ecosystem.

Overall, Codatta’s reputation system provides a solid foundation for decentralized data services, ensuring data reliability and participant engagement, and laying the groundwork for the sustained healthy development of the Web3 ecosystem.

About Codatta

Codatta is a universal annotation and labeling platform that seeks to turn human intelligence into AI.

We are the first decentralized data protocol building foundational infrastructure for developers, protocols, and AI, pioneering AI-driven collaboration for blockchain metadata, making data universally accessible and transparent. We have assembled a sizable network of contributors to enhance data confidence through AI-verified evidence, multi-party cross-referencing, and staking-as-confidence. Developers can create Web3-native applications powered by machine learning models in areas such as criminal-resistant networks, on-chain advertisement, dApp-agnostic recommendation systems, and credit-based lending products. Our innovation extends to converting on-chain activities into portable profiles, empowering users to easily access monetary value.

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