Time-weighted staking rewards scale allocation by both stake size, dampened by a square root, and duration staked. This defeats just-in-time staking, where capital enters right before a reward snapshot and exits right after, ensuring rewards accrue to committed participants rather than mercenary capital.
Time-weighting without an exit design only defers the mercenary capital problem. Pair duration incentives with rolling unlocks so exits spread across time instead of hitting one cliff.
How it works
Time-weighted rewards condition each participant's share on two variables at once: stake size and how long that stake has been held. Duration is the key. A pure stake-size model pays by balance at a single snapshot, which invites capital to arrive just before the snapshot and leave right after. Time-weighting closes that by requiring holders to stay through the measurement window to earn proportional credit.
The common formulation weights allocation as (stake_size)^0.5 multiplied by time_staked, or a continuous integral of stake over time. The square root dampening on size reduces the dominance of the largest stakers and raises the relative value of duration for smaller ones, encouraging long-term retention even among holders who cannot build large positions.
Design consequence
The effect is a shift in the staker base. Without time-weighting, mercenary capital, the yield farmers who chase current rates, captures an outsized share by optimizing entry and exit timing. With it, fresh capital earns a low rate at entry that rises over time, making brief opportunistic participation unprofitable while committed holders accrue an increasing advantage.
Example
Vote-escrowed tokenomics applies the same logic to governance and boosted yield access instead of direct reward distribution. Lock tokens for four years and you receive a maximum multiplier on rewards and governance weight; lock for one month and you get a fraction. The insight is identical: duration signals genuine alignment, and the reward structure should amplify that signal.
See Tokenomics Design for how this applies in practice.
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