RealT DAO PowerVoting Models: Community Feedback Request
Introduction
Dear RealT DAO community,
A new tool has been developed to help us visualise and experiment with different PowerVoting models for REG in concentrated liquidity pools. As you know, the current model gives liquidity providers a 4x voting power multiplier for their REG tokens and a 2x multiplier for the REG equivalent of other tokens in liquidity pools. However, we're exploring alternative models that could better align incentives with the DAO's needs.
We're seeking your feedback to help select the best PowerVoting model(s) to implement in advance of a proposal change to the existing approach
About the Tool
The visualisation tool created allows one to:
- View liquidity positions for the REG token across SushiSwap V3 pools
- Analyse position distributions relative to the current price
- Model different PowerVoting calculations with various multiplier formulae
- Visualise how different models impact voting power distribution
- Compare models side-by-side
The tool uses real-time data from SushiSwap's subgraph and allows experiment with custom PowerVoting formulae.
PowerVoting Models Under Consideration
Several example models have been defined to showcase what's possible. These are primarily to demonstrate the tool's capabilities - I'm particularly interested in your creative ideas for new models. Here are six examples:
1. Current Model: Default 4x/2x Multiplier
Description: REG tokens in liquidity pools receive a 4x voting power multiplier, while the REG equivalent of other tokens (e.g., USDC, WXDAI) receives a 2x multiplier
Equation: reg_amount * 4 + reg_equivalent * 2
Characteristics:
- Simple to understand and implement
- Treats all liquidity equally regardless of price range
- Different weighting for REG vs. other tokens
- No special incentive for positions at or near the current price
2. Active Positions Only
Description: Only positions that include the current price (active positions) receive the multipliers.
Equation: reg_amount * 4 * is_active + reg_equivalent * 2 * is_active
Characteristics:
- Incentivises liquidity providers to maintain positions that include the current price
- Positions outside the current price range get no bonus (1x)
- Creates a "step function" at range boundaries
- May encourage narrower ranges around the current price
3. Simple Linear Decay Model
Description: Voting power decays linearly with distance from the current price, from a maximum of 4x for REG and 2x for equivalents at the current price, to 1x for positions far from the current price.
Equation: reg_amount * (4 - 3 * min(relative_distance, 1.0)) + reg_equivalent * (2 - min(relative_distance, 1.0))
Characteristics:
- Higher rewards for positions closer to the current price
- Smooth transition as positions move away from current price
- Positions at ≥100% relative distance get minimal 1x multiplier
- Incentivises tighter ranges but still values wider positions
4. Simple Exponential Decay Model
Description: Voting power decays exponentially with distance from the current price, with a sharper drop-off than the linear model.
Equation: reg_amount * (1 + 4 * pow(0.37, 5 * relative_distance)) + reg_equivalent * (1 + 2 * pow(0.37, 5 * relative_distance))
Characteristics:
- Highest rewards for positions very close to the current price
- Rapid decline in multiplier as positions move away
- More aggressive incentive for providing liquidity at the current price
- May result in concentrated liquidity exactly where it's needed most
5. Broader Exponential Decay Model
Description: Voting power decays exponentially but over a wider price range, providing higher multipliers further from the current price.
Equation: reg_amount * (1 + 3 * pow(0.5, relative_distance/2)) + reg_equivalent * (1 + pow(0.5, relative_distance/2))
Characteristics:
- More gradual decay than standard exponential model
- Maintains higher multipliers at greater distances
- Balances concentrated liquidity with range width
6. Rational Decay Model
Description: Uses a rational function for decay that provides very high multipliers near the current price but maintains reasonable values at moderate distances.
Equation: reg_amount * (3 * (1 - (relative_distance / (relative_distance + 1.5))) + 1) + reg_amount * ( (1 - (relative_distance / (relative_distance + 1.5))) + 1)
Characteristics:
- Alternative to exponential decay with different mathematical properties
- Approaches asymptote more gradually
- High value near current price without dropping too quickly
What We're Looking For
If you can, grab the tool and self run here for experimentation.
We'd really value your input on:
- Which model(s) approaches do you prefer and why?
- What new model designs can you suggest? (Please share your equation and a brief description - I can respond with the visualisation)
- What trade-offs do you see between different models?
- How might these models affect your liquidity provision strategy?
Next Steps
Based on community feedback and discussion between the Powervoting Committee we'll create a shortlist of models for formal governance proposals. If you share interesting model ideas, I can run them through the tool and post the visualisations in this thread.
Since the tool is currently running locally, I can't provide direct access, but I'm happy to process any model suggestions you provide and share the results.
Thank you for your contributions to improving RealT DAO's governance!
Currently available variables:
reg_amount: Actual amount of REG in the position
reg_equivalent: REG equivalent of other token (e.g., USDC/WXDAI) in the position
relative_distance: Distance from current price as % of current price
price_distance: Absolute distance from current price
is_active: 1 if position is active, 0 if not
Note: The visualisations above show how each model affects the voting power of real positions in liquidity pools. The red dotted line represents the multiplier value at different price points, while the bars show the actual REG tokens in each position.
