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Optimizing Token Emissions for SushiSwap

Increasing trading volume through dynamic reward distribution

Gauntlet is partnering with SushiSwap to optimize SUSHI emissions in Onsen pools. Despite the enormous success in TVL and trading volume, SushiSwap Onsen pools have headroom to be more capital efficient. In particular, Gauntlet will improve capital acquisition (liquidity) costs through allocation points (token emissions) distributions. Gauntlet’s simulation platform will benchmark existing incentives and then provide recommendations to improve the effectiveness of Onsen rewards. The overarching aim is to increase trading volume for SushiSwap pools — during and after their time in the Onsen.

The effectiveness of token emissions in the first two rounds of SushiSwap Onsen has been opaque at best. Do rewards based on market cap make the most sense? Do these joint liquidity mining partnerships align incentives properly? Do the token emissions provided drive value to the protocol long term? Are the current incentives setting pools up for success after Onsen? SushiSwap’s hard cap token supply and diminishing monthly block reward make this optimization problem even more urgent.

“It is a pleasure to bring onboard the galaxy brains of Gauntlet to help us direct the flow of fresh sushi to our amazing guests” — 0xMaki

Gauntlet’s simulation platform solves optimization problems that SushiSwap and other decentralized exchanges face around trading fees and rewards. Gauntlet will employ agent-based simulation models in conjunction with a Feedback Control model for existing and upcoming Onsen pools. Rounds 1 and 2 of Onsen, where Gauntlet has yet to provide recommendations, will supply the simulation platform ample data to back-test and perform historical validation. These simulation runs will create the foundation for the Core allocation point (AP) model. Combining the Core AP model with AP Spend Efficiency and LP/Trader models forms the Feedback Control model.

AP = Allocation Points which determine SUSHI emission rates

The Feedback Control model updates AP recommendations and uses the resulting market share as an input to close the loop. This cycle is continuous, adaptive, and is crucial to get the most bang (trading volume) for the buck (SUSHI).

Going Forward

Gauntlet has begun preparing updated AP recommendations for existing Onsen pools with recommendations for subsequent rounds to follow. SushiSwap community members will find Gauntlet active in the forums and Discord to communicate and clarify the recommendations made by our simulations.

Rewards optimization is not the only mechanism to improve capital efficiency. Newly published research on borrowing liquidity provider shares should prove valuable to SushiSwap’s Kashi in the future. Gauntlet looks forward to rolling this partnership forward! 🍣




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Nicholas Cannon

Nicholas Cannon

now: Growth @Gauntlet | past: operations, fintech, poker pro

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