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.
OpenBlock aims to accomplish the following objectives:
- 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.
- 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.
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.
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.
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.