CA Improvement Mechanism

Develop a reward/penalize mechanism for CA to attract people with expertise and desire to contribute to the development of Cardano.

Founder team has varying expertise, namely: Business owner; Digital marketer; Full-stack developer; Project manager; Community manager, Data analysis; Dev team maker and Software product manager. Each with more than 10 years of experience.

[IMPACT]

This proposal introduces a mechanism that increases reward for qualified review and penalizes for poor review based on three criteria, namely:

1. The expertise of the CA versus the main expertise of the proposal. Though CA are anonymous, when we register for being a CA, we are still asked what are our expertise. This answer can be used for this point. It means, Catalyst has a list of expertise, CA can select more than one as his/her expertise. The same is applied to a proposal. By comparing the two, solution can be found.
2. Signs of using bot for evaluation. In this situation, we will use data from not only review for different challenges of the current fund, but also the review from previous fund. Approaching that way, the more time Catalyst Fund moves forward, the better quality the tool. And
3. Quality of the review. This is the major part of this proposal and totally different from the role of vCA. This is a mechanism to attract qualified people coming to Catalyst to become CA. In this proposal, a reward & penalize mechanism for CA review is proposed. This is absolutely different from the current mechanism where CA review is always rewarded, either zero or positive. In our proposal, giving poor or wrong review, CA can be punished (in financial aspect, of course), also. Since the mechanism including penalizing poor review, the use of bots or skimming reviews for quantity and rewards will be minimized. At the same time, the reward for good reviews will be increased to an attractive amount to encourage competent and dedicated CAs to participate in proposals' review.

a) Problem:

• Some CAs evaluate proposals where they do not have project-related expertise with the purpose of having as many reviews as possible for a higher reward. CA evaluates the proposals under the MECHANISM that he/she can either receive or not receive reward (win only) and there is no penalty (no loose) for doing negative affection for Cardano's development. This leads to the existence of poor quality reviews, no good suggestions for the proposers to adjust, no good advice for the Ada holders to vote for or against the proposals.
• For financial purposes, a few CAs use bots to evaluate projects, resulting in poor quality reviews for both proposers and voters.
• The main cause of these problems is that the interests of the CA have not been directly attached to the interests of the proposers and/or the voters.

b) Proposed solution:

Any succeeded financial game needs two faces: (1) Rewards; and (2) Penalties; for serious players to participate. The proposed solution here is developed to have both Reward and Punishment mechanism for CA to attract talented and interested professionals to participate. The mechanism includes 3 major factors:

• The expertise of the CA versus the main expertise of the project.
• Signs of using bot for evaluation. And
• The reward/penalty mechanism that is proportional to the quality (contribution to the project owner, to the voter) of the review.

c) Technical solution:

Parallel to building CA audit tools and plugins. We will be creating a database of CAs and vCAs for many years to come. In addition to the official update from Catalyst through Funds, in the long term, we want to analyze this data source on the basis of bigDATA with simulation algorithms. This will in the future help create a reference source for Cardano's DAO formation and governance.

• Build APIs pool.
• Architecting and building the original database.
• Realization of statistics on the web.
• Functions to review and classify CAs according to established criteria.
• The virtual reward model in the system is almost similar to Catalyst's current reward mechanisms.
• Essential statistics and indicators for Ideascale.

With the vision of the project towards a testing system for the CA process as well as implementing prototypes before the next funding rounds of Catalyst Funding.

The programming languages and tools we used to develop this project include:

• NodeJS, React, Flutter, JavaScript, MySQL, MongoDB,
• Operating system used: Linux, iOS, Android...
• Infrastructure like Amazon Services cloud computing, Softlayer of IBM…

a) Risk identification:

• Funding may arrive later than expected, affecting project progress.
• The evaluation of the proposed CA reward/penalty mechanism may lead to some adjustment of the mechanism, prolonging the time it takes to put the mechanism into practice.
• Inflation and economic recession may affect project team such as epidemics, wars.

b) Risk mitigation:

• Founder team directly develop the project, so the work progress is less dependent on the project funding progress.
• When the 3rd party verifies the proposals on the CA reward/penalty mechanism that need to be adjusted, the proposer team can review, clarify and work directly with the 3rd party to have the best mechanism for Catalyst.
• Founder team is directly involved in the development of the mechanism, so the funding progress, inflation and war has little effect on the project progress. Besides, we are surrounded by a pool of expertise and can be called in action in a very short of time.

[FEASIBILITY]

a) In the next 3 months after approved:

• Developing an application to assess the correlation between CA's expertise and the main expertise of the assessment proposal.
• Developing an application to filter out reviews that show signs of using bots based on the repetition rate of reviews for different proposals.
• Developing a mechanism to pay rewards and penalize for CAs.

b) In the next 6 months after approved:

• The feasibility of reward and penalty payment mechanism for CA verified by a qualified third party.

c) Roadmap:

Month 0-1:

• Develop a theory on correlation between CA's expertise and the main expertise of the assessment proposal.
• Programming the theory to make a software for assessing expertise correlation between CA and proposal.
• QA & testing the software.

Months 1-2:

• Developing a theory on classifying the sign of using bots on a review based on reviews of current fund and previous funds.
• Design and programming a tool for filtering out reviews that show signs of using bot.
• QA & testing the tool.

Months 0-3:

• Developing a theory on the new mechanism for rewarding and penalizing CA for their review.

Months 4-6:

• Select a competent third party that can verified the proposed mechanism.
• Send the proposal mechanism to the selected party for assessment.
• Make modification to the proposed mechanism (if required).

1. Design plugin software to assess the correlation between CA's expertise and the main expertise of the assessment proposal: 50h x ($40/h): $2.000
2. Design plugin software to filter out reviews that show signs of using bots based on the repetition rate of reviews for different proposals: 40h x ($40/h): $1.600
3. QA & Test Plugin: 40h x ($50/h): $2.000
4. Developing a mechanism to pay rewards and penalize for CAs: 100h x ($50/h): $5.000
5. Fee for verifying the mechanism to pay rewards and penalize for CAs by a qualified third party: $10.000
6. Project management fee (4h per week, 25 weeks): 100h x ($50/h): $5.000
7. Contingency fee: $3.000

Requested fund in USD: $29.600

1) Do Manh Hung

Experiences:

• Master of finance
• Community manager
• Plutus Pioneer
• Influence and inspire trainer
• Main lecturer of FIMI's course

Obligation:

• General Advisor

Social channel:

• Telegram: https://t.me/Hung_DoManh
• Telegram: https://t.me/StakingADA

2) Duc Tiger:

Experiences:

• Business owner
• Digital marketing
• Community management
• Data analysis

Obligation:

• Marketing management
• Finance management

Social channel:

• Telegram: https://t.me/DucTIGERpool
• Linkedin: Duc Tiger

3) Trong Nguyen:

Experience:

• An IT specialist who worked for many big IT tech firms in Japan such as GNT, ISFNet, and IBM.
• Experienced in software product management, digital content with 10 years of mobile app development for Android and IOS.
• Mentor for startups in mobile content and blockchain, startup founder, dev team maker. He is also a software engineer with many years of experience in internet-based product development.

Obligation:

• Product management
• Digital content
• Project Schedule Management

Social channel:

• Telegram: https://t.me/alex_trongnguyen
• Linked: Trong Nguyen

4) Viet Anh:

Experience:

• Project manager
• PMP holder
• Business owner

Obligation:

• Overall project manager

Social channel:

• Telegram: https://t.me/tvauce
• Linked: Tran Viet Anh

[AUDITABILITY]

a) In the next 3 months after approved:

• Developing an application to assess the correlation between CA's expertise and the main expertise of the assessment proposal: 100%
• Developing an application to filter out reviews that show signs of using bots based on the repetition rate of reviews for different proposals: 100%
• Developing a mechanism to pay rewards and penalize for CAs: 100%

b) In the next 6 months after approved:

• The feasibility of reward and penalty payment mechanism for CA verified by a qualified third party: 100%

c) Milestones:

Month 0-1:

• Develop a theory on correlation between CA's expertise and the main expertise of the assessment proposal: 100%
• Programming the theory to make a software for assessing expertise correlation between CA and proposal: 100%
• QA & testing the software: 100%

Months 1-2:

• Developing a theory on classifying the sign of using bots on a review based on reviews of current fund and previous funds: 100%
• Design and programming a tool for filtering out reviews that show signs of using bot: 100%
• QA & testing the tool: 100%

Months 0-3:

• Developing a theory on the new mechanism for rewarding and penalizing CA for their review: 100%
• Select a competent third party that can verified the proposed mechanism: 100%

Months 4-6:

• Send the proposal mechanism to the selected party for assessment: 100%
• Make modification to the proposed mechanism (if required): 100%

• Increment in the number of new mechanisms for assessing and scoring proposals
• Increment in the number of excellent reviews.
• Decrement in the number of unqualified / poor reviews.
• The percentage of ADA participating in voting increased compared to the previous Fund.
• The ratio of the number of voting wallets to the total number of wallets has increased compared to the previous Fund.

No yet, this is new one.

SDG Rating

Not at all!