[Proposal] Developing a sustainable, objective-based incentive optimization framework

Feedback
1

Our Proposal

As a member of the Radiant DAO and co-founder of OpenBlock Labs, I am submitting a proposal to build an objective-based framework for incentive distribution. To maximize the productivity of rewards received from the Arbitrum airdrop, OpenBlock Labs will craft a robust, tailor-made incentive engine that quantifies weekly and monthly reward budgets on a per-pool basis. This incentive engine will leverage sophisticated econometric models to ensure that the DAO is cost-effective in its emission spend, while still appropriately rewarding high-quality dLPs that lock RDNT.

Key Objectives

OpenBlock aims to accomplish the following objectives:

  1. Data-Driven Incentive Distribution: Develop a robust model to improve liquidity mining by replacing subjective decision-making with data-driven methods for incentive distribution. This approach will ensure a more effective and sustainable rewards system for the Arbitrum airdrop.
  2. Real-Time Incentive Dashboard: Employ a first principles methodology in assessing LP + borrow rates and liquidity objectives in accordance with market demands, delivering instantaneous budgetary suggestions grounded in this data.

A Novel Liquidity Incentive Engine

The protocol has received ~3.4M ARB, which will become a substantial budget to manage for the DAO. RFP-18 has allocated 70% of the tokens to be distributed to dLP lockers, and 30% for strategic use. This proposal aims to utilize a portion of the strategic reserve for R&D on improving the incentive distribution system, bolstering an incentive mechanism that stands the test of time. A blanket approach to incentives will simply not scale, as the net expenditure can heavily offset revenue earned by the protocol. If managed incorrectly, the Arbitrum airdrop can become an expensive liability, as mercenary liquidity providers will abandon the network when rewards dry up––the new pools may never reach escape velocity, and future LPs will pay the price.

With the advent of dynamic liquidity, LPs can lock at least 5% of their total deposit value in RDNT token to be eligible for LP emissions. The introduction of a variable APR based on the duration of the dLP lock aims to incentivize long-term engagement from loyal network participants, rather than ephemeral yield farmers. OpenBlock will conduct a deep-dive analysis on this mechanism, verifying if the DAO is over- or under-spending based on a mean-variance analysis across yields in the Arbitrum ecosystem. OpenBlock will display these insights on a real-time dashboard for Radiant, with an intuitive interface to consume budget recommendations. OpenBlock is committed to working with Radiant’s pre-existing data frameworks, and iterating with the relevant contributors to leverage insights from previous experiments.

Specification

Phase 1: Systems Analytics Dashboard

  • Identify leading pools across Arbitrum by assessing lend/borrow data and determining optimal growth points for Radiant to win market share.
  • Deploy advanced econometric methods to quantitatively project the specific impacts of incentive allocation on both pool borrows and total value locked (TVL).
  • Analyze granular transaction data to discern supply and borrow variations across pools, thereby informing the most efficient incentive allocation strategies.
  • Develop a data-visualized, interactive dashboard offering the DAO real-time pool performance metrics and precise incentive budget recommendations.

Phase 2: Execute recommendations

  • Using the results from Phase 1, derive a series of data-backed strategies aimed at increasing supplies + borrows in identified pools by incentivizing liquidity.
  • Analyze the efficacy of current incentive distribution mechanisms and work with relevant contributors to make any necessary adjustments.

Phase 3: Continuous optimizations

  • Provide persistent incentive management support, aimed at nurturing capital-efficient, sustainable growth across new market venues.
  • Institute a data-driven mechanism for regularly updating incentive strategies, designed to swiftly respond to real-time changes in market conditions and liquidity needs. This mechanism will be powered by predictive modeling, employing advanced machine learning algorithms to forecast market conditions and adjust incentive strategies accordingly.
  • Implement a regimen of regular monitoring of key performance indicators and market conditions, yielding consistently updated, data-driven recommendations adaptive to market evolution and pool performance dynamics.

Cost: To execute this project, OpenBlock Labs requests 60k ARB per month. This will fund a dedicated team comprising 2-3 data scientists, 1-2 quantitative analysts, 1 frontend engineer, 1 product designer, and 1 project manager. Paid monthly.

About OpenBlock

OpenBlock Labs is an R&D firm that empowers decentralized organizations with on-chain data insights for incentive optimization. OpenBlock is backed by notable figures in the crypto space, including Foundation Capital, Electric Capital, Circle Ventures, AlleyCorp, and others.

The team has backgrounds from Stanford University, a16z, Carnegie Mellon University, Meta, Palantir, and other top-tier institutions; the highly technical background of our team makes us confident that OpenBlock is uniquely positioned to tackle a problem of this nature.

Final Thoughts

OpenBlock looks forward to assisting in the design of a sustainable rewards model for Radiant. This work will be a great first step towards solidifying a productive relationship for both communities. We look forward to hearing the community’s thoughts, and answering any questions. Thank you for your consideration.

2

Are you the same co-founder of Delta one?

1 Like
3

Previous experience shows us that paying “monthly” or by any measure of time opens the dao up to manipulation and draining by the projects they employ. It’s more efficient to pay by the project, job, or tranche. At 60k Arb per month there is no incentive for you or your team to finish the job and since we know how quickly things can get delayed in this space it’s also unreasonable to expect you to finish within a certain timeframe.

This is not an accusation of you or your team, just past experience being shared. However, by agreeing to a monthly salary, we have to trust that you will stay on some sort of schedule, while also realizing the inevitably of delays. It will definitely create friction between the two groups. If we agree to pay per job or tranche of your progress, your team is incentivized to complete the project while the DAO can be reassured that the job is handled properly.

To be as clear as possible, my opinion on the matter is that the proposal be broken up into items which we will pay an agreed amount on when certain progress is made. The first payment could be up front and from that point as your team closes out each goal, the dao will pay your team.

7 Likes
4

I should have specified that outside of what I wrote above, I have no issue with the proposal. It’s a great idea paired with a well written proposal and seems as if you all know what you’re doing which I can appreciate.

2 Likes
5

I am exactly of your opinion this proposal is magnificent but I also prefer to pay in several installments and to the work done!

2 Likes
6

Thank you for your input, @acidhoe @_arradosvki!

We are open to alternative compensation models. Here is another approach:

60k ARB up-front and 180k ARB upon completion.

We look forward to receiving additional feedback on this proposal and will proceed to an on-chain vote once the community is ready.

2 Likes
7

Sorry, but for 60k per month, you need to do more to convince me to vote for this. For me it’s always a red flag when people (over-)use words like “robust”, “regimen”, “deep-dive” and “novel”. Sound a lot like what over-paid consultants present in their ppts.

3 Likes
8

Against. Just a temporary cost that will have zero value after 52 weeks. If the team has not learned anything from V1 and V2 … then what are they doing and why are they still getting paid? I would have thought the ARB emissions testing would have been complete by now.

Drip the coins and let people swap, sell or hodl them… it’s easy. And it doesn’t cost almost $20,000 USD a month.

5 Likes
9

Ridiculous compensation that you guys are asking for and essentially not something that can help grow Radiant exponentially. Sounds like it’s just a plan to ask for $10k - $20k compensation per month per employee. Voting against this for sure!

3 Likes
10

I’m guessing part of this is operating on the assumption that radiant will be eligible for a retroactive grant, similar to what Optimism has done. Meaning it would have value past the expiration of current incentives but there’s no way to know right now what that grant looks like, what size it is, what restrictions are on it, if Radiant will definitely get it, if there are multiple rounds, or how long it would last.

2 Likes
11

Yeah that is fine, and I wont pretend to know what exactly you’re talking about… but I’m just taking this at face value - which I get, is very informal skeleton. So, I just don’t see the value of what we’re actually getting at the end of the day that costs what… $75-100k? If this does make it to an actual vote (hope not) I sure hope they can provide more details, screenshots etc. because all my limited mind can see right now frankly is paying a fancy front-end website that gamifies our ARB tokens. Alas, we pretty much have that already. Still no idea what the team cannot complete even the first task of emission testing. This is all moot until they figure out if that can happen.

4 Likes
12

I don’t really see the need for anything of that, especially at this cost.

And as far as I know, the team get already some % to pay for the cost / salary as it was voten previously.

3 Likes
13

And over the 3.4m, only 70%, so around 2.4m will be allocated for the dlp locker, that mean if they take around 240k as they propose, they want 10% of the total which is far too much IMHO.

3 Likes
15

The proposal didn’t provide even its website and its previous contribution is not reliable. The “co-foounder” is anon. No experience. No reputation. Abused fancy words.
It’s the most ridiculous proposal I have seen in this space so far.
A big NO from me.

2 Likes
16

"I’m GUESSING part of this is operating on the assumption … " - with respect: how did you even come up with this?? It’s not even relevant to the topic.

Big NO for me. I hope it doesn’t even go to a vote.

1 Like
17

Would these optimisations clearly demonstrate a better value for money than distributing 240k ARB to the DLPs instead?

How efficient can payments really become other than simple time-based or TVL-based metrics? I feel 80% of the value can be found if the distribution is simply matched to fees generated in the last week. Incentivising the pools that generate the highest fees is good for everyone. Unsure what other metrics are worth more.

1 Like
18

Thank you all for your valuable comments and feedback. We have noticed that many of you have raised concerns about the price being the primary limitation when considering the DAO’s investment in a data-driven incentive engine (re: @Ishkur @Maskedash @kin0 @_gwr @roccobarocco73). Specifically, the community is looking for an improvement of over 10% in the current reward distribution in order to justify the cost of this proposal. Although it is worth mentioning that this framework would also optimize future reward spend, it is challenging for community members to quantify that benefit. We are confident that this improvement is achievable and would like to propose the following:

To better align incentives and minimize initial risk for the DAO, we suggest a 45-day trial period with an upfront cost of 20k ARB. We believe this cost is tolerable given the ~1M ARB set aside for strategic use. The community can evaluate the performance of our work during the trial and, if successful, provide additional retroactive funding. While we are a relatively new provider in this space, we have demonstrated our value by serving a few leading protocols, which you can learn more about on our website here.

2 Likes