Oh, I didnt realize it was stated in the onchain vote that the deilverable would be 5-6 months later… (it wasnt). But rather on the forum here after several months of silence.
Even better. UNI holders should not payout on this proposal. Is there another vote? Who decides this?
This is wandering into MuskRat territory. Assuming a contract was formed (paperwork?) then performance is required on both sides
These are all basic project contract management … the delegates can play a key role in whistle-blowing (in which case can incentivise by give a portion of clawback).
We’ve centuries of experience to codify into smart contracts …
… unless you get the supreme court to grant absolute immunity for lawlessness.
Analyzing incremental TVL per USD spent, we found that pools with low impermanent loss consistently delivered the best ROI during the campaign.
Our analysis comparing TVL gains vs. volume gains per USD spent reveals distinct patterns among pool types.
Adjusting for market conditions, we analyzed how TVL evolved after incentives ended across scoped chains.
Main insights:
If we split the incentivized pools between Stable (e.g. USDC/USDT), Pseudo-stable (e.g. wstETH/ETH) and Volatile (e.g. USDC/WETH), we notice that the only sticky TVL is on volatile pools. The additional TVL gained by incentivizing safer pools is the one with the most short-lived post campaign impact.
Main insights:
cbETH/ETH 0.05% in favor of cbETH/ETH 0.01% and WETH/USDT 0.05%We notice the same trends than Arbitrum with regards to the post-campaign impacts split by pool type.
Both Scroll and Blast faced challenges in sustaining TVL post-campaign, with declines influenced by broader market dynamics.
We’re excited to announce that the Uniswap Impact Terminal is almost ready. Development is wrapping up, and we’ll be launching the interactive data terminal next week. This will provide the community with deep insights into the impact of analyzed incentives.
We want to ensure our analysis provides the most valuable insights to the Uniswap community. Drop your feedback below—we’d love to refine our work based on community insights! 
Congrats on delivering the results and updating the DAO (I wish more of the UF grantees were as diligent) …
Given your extensive analytics, are you
a) able to predict the uptake of UniChain, or at least identify early if incentive is likely to fail and terminate operations on that pool;
b) postulate metrics or indicators that detect migration away from existing chains rather than new pools (eg use of bridges or intermediate wallets of yield farmers)
c) using data to adjust for inflation, eg constant 2020 dollars (or other fiat) to figure out how AMMs respond to macro supply/demand shocks
Hi @drllau_LexDAO,
Thank you for your comment!
a) We can use all the data we have to build a predictive model that estimates the likelihood of success of an incentive program on any given pool. However, we want to clarify that our current scope does not include this, especially not for UniChain. We can consider expanding to include UniChain and wider scopes in the future if the DAO provides such direction for Forse.
b) We can estimate which defectors leave the chain or the protocol by looking at bridging activity and balances. We can already see the trends between protocol and chain exodus post-campaign from competitor comparisons that we showed in the presentation. DAO members will soon be able to interact with the dashboard to examine these trends.
c) This is something we can do; adjusting values for CPI and/or total crypto market cap can provide additional insights. However, all chains apart from Arbitrum are very young, and most pools there are less than 1 year old, so the insights gained from such adjustments might be limited.
Our understanding is that @Gauntlet has already designed incentive campaigns that are currently approved for Uniswap v4 (including Unichain) based on a similar analysis. Instead, we see a potential expansion of Forse’s work within this scope if additional multi-million dollar incentives are to be deployed to other chains.
StableLab has completed our Forse Uniswap Incentive Analysis!
Hi @drllau_LexDAO,
Our Data Team produced further analysis specifically regarding point (a):
We’ve successfully developed a preliminary predictive model leveraging historical incentive campaign data. Although still limited by the available dataset, early results demonstrate a promising ability to estimate the likelihood of success of individual incentive campaigns. With additional data, we expect the accuracy and usefulness of this predictive capability to improve significantly.
In conclusion, this proof-of-concept highlights clear opportunities to leverage existing incentive data to better inform future decisions for Uniswap’s incentive strategies. Should the DAO find value in exploring this further, we’re open to discussing how we might support the community in enhancing these predictive insights, potentially as part of future analytics engagements.
Regarding (b), as mentioned earlier, you can already review migration trends directly in the newly released terminal on the Forse Platform.
As for (c), our opinion remains the same—while adjusting for macroeconomic factors such as CPI or total crypto market cap is feasible, current data limitations—specially in newer chains—mean these adjustments would likely provide limited insights at this time.
Thanks again for your questions, and let us know if there’s anything else we can clarify!
This doc presents the results of a proof of concept run by Forse to predict the performance of incentivization campaigns on Uniswap pools. Our goal was to identify patterns in the pre-campaign data of liquidity pools that could help predict the effectiveness of launching an incentivization campaign on those pools.
We created a model that predicts the likelihood of success for incentivization campaigns on a per-pool basis, using Machine Learning.
We assess the success of a campaign by measuring the post-campaign TVL compared to pre-campaign: if the TVL growth of an incentivized pool significantly outpaces the TVL growth of the Uniswap protocol, we consider the campaign as successful.
The amount of training data used is very limited, the model would require additional training inputs to guarantee high accuracy and performance. Nevertheless, this proof-of-concept demonstrates that there are opportunities to leverage existing campaign data to increase the success of future UNI campaigns.
Post-campaign TVL split by model predictions
By comparing the pool-by-pool TVL change after the campaign vs before the campaign, we can see that although some top performers are missed, it performs well to anticipate which pools will yield good post-campaign results.
