by 0xd11a019a70986bd607cbc1c1f9ae221c78581f49 (Yemel)
The Decentraland platform emphasizes user-generated content, such as wearables and scenes. This high-quality content is what attracts and retains users, stimulates the marketplace, and ultimately increases revenue for the DAO. Therefore, it’s crucial that the DAO incentivizes the creation of exceptional content to maintain this cycle.
Currently, the DAO operates a grant program to stimulate the development of in-world experiences. Beyond this initial support, however, there are no direct incentives for creators to continue enhancing their scenes. Scene creators often monetize their creations through methods such as selling NFTs, issuing their own tokens, renting space to third parties, or selling the scene code for others to deploy on their land.
A critical question for game developers arises: Why should they build their game on Decentraland instead of other platforms or distribution channels? Besides the project’s strong values and initial financial support, there seems to be a long-term economic benefit that has not been fully defined yet.
This proposal suggests a distribution of MANA to scene creators based on the number of users their scenes attract. The user addresses and their locations are publicly available information, disclosed by the protocol and accessible via DCL Metrics and Atlas Corporation. The Decentraland Foundation also maintains a record of this data.
There are, however, numerous questions about how such a distribution should operate, particularly concerning its vulnerability to hacking or bot attacks. The purpose of this proposal is to gather community sentiment on the idea and to start discussions about how to design such a system.
The Decentraland Foundation could initiate a pilot program, taking responsibility for calculating the winners, filtering bots, and distributing MANA as compensation to creators. Subsequently, any community member could request a grant to keep this program operational.
Questions & Proposed Answers:
What scenes participate in the engagement contest?
All scenes in Genesis City can participate in the contest, excluding plazas and roads.
How much MANA should be distributed?
The amount should be adjusted based on the revenue generated by the DAO from marketplace fees. For instance, over the last 30 days, there was an income of 30k USD.
How should the MANA be distributed among the scene creators?
Prizes should be distributed monthly. All scenes will be ranked by an engagement metric using data from the past 30 days. The top 20 scenes receive a part of the prize based on the following scheme:
- Top 1: 25% = $7,500
- Top 2: 15% = $4,500
- Top 3: 10% = $3,000
- Top 4 to 10: 4% = $1,285
- Top 11 to 20: 2% = $600
The total prize is based on a 30k USD amount.
How is the engagement of a scene measured?
Several metrics should be defined to strengthen the algorithm. The key metrics will be calculated using public traffic data reported by the catalyst nodes. The proposed metrics include:
- AVG DAILY UNIQUE WEB3 VISITORS: Number of unique web3 visitors for the month divided by the days of the month.
- AVG DAILY WEB3 VISITS: Number of times web3 users have entered the scene within the month divided by the days of the month.
- AVG SCENE RETENTION: The average of days passed between the first and last appearance of a web3 user in a scene within the month for all web3 users that visited the scene more than once in that month.
- AVG RETURNING: Average number of times a web3 user visits the same scene within the month.
- AVG STAY MINS: Average time spent by web3 users in the scene, in minutes.
All metrics are calculated over web3 users (non-guest users).
How is a ranking produced based on the key engagement metrics?
The process involves creating five different scene rankings, one for each key metric. These rankings are then combined using Borda’s count method using the formula:
scene_score = value(position_ranking_1) + … + value(position_ranking_5)
The value of each position in the rankings is calculated using numbers derived from different percentiles of a normal distribution. This method ensures that ranking in the top positions is more rewarding.
What does this ranking look like for June engagement metrics?
You can find the dataset with the example at this link.
How are bots prevented from manipulating the ranking?
To prevent fraudulent activity in the engagement metrics, the Decentraland Foundation flags and removes from the analysis web3 users who display suspicious activity. This activity is currently flagged based on the number of sessions connected from the same IP and user agent. The list of suspicious users can be published along with the dataset for auditing purposes.
- Yes
- No
- Invalid question/options