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  • 0. Overview: reward drivers for delegators
  • 1. Reward distribution for delegators
  • 2. Example

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  1. FLOCK TOKENOMICS
  2. Network Participation
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Delegator

PreviousValidatorNextDelegation Pool Rewards Explainer

Last updated 3 months ago

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Delegators are responsible for delegating stake to training nodes and/ or validators.

0. Overview: reward drivers for delegators

Reward for the delegator depends on:

  1. quality of the training nodes/ validators selected, as measured by the scores/ ranks training nodes/ validators received based on the quality of their work

  2. amount of stake delegator delegated

1. Reward distribution for delegators

If we consider fif_ifi​ to the the rewards distributed to training nodes and their delegators as explained , then the reward for delegator to this given training node is:

fiβ‹…(1βˆ’Οƒ)β‹…tdtn+tdf_i \cdot (1-\sigma)\cdot\tfrac{ t_d }{t_n + t_d}fi​⋅(1βˆ’Οƒ)β‹…tn​+td​td​​

Here, tdt_dtd​ refers to the amount the delegator delegated to the given training node, whereas tnt_ntn​ is the stake amount from the same training node.

Similarly, if a delegator delegated FLCOK to a validator, the reward for this delegator is:

fiβ‹…(1βˆ’Οƒ)β‹…Β sdsv+sdf_i\cdot (1 - \sigma) \cdot \frac{\ s_d}{s_v+ s_d}fi​⋅(1βˆ’Οƒ)β‹…sv​+sd​ sd​​

2. Example

Note that rewards in delegation pools are time-weighted to balance fairness for long-term participants and incentivize new delegations. As pools grow, rewards stabilize, promoting sustained engagement. Also, delegators must maintain their stake for at least 24 hours before un-delegating. This ensures meaningful contributions and prevents exploitative behaviors.

Here, fif_ifi​ refers to the rewards earned by validators and their delegators as explained . sds_dsd​ is the delegated amount from delegator to this validator, whereas svs_vsv​ refers to the stake amount from the validator.

Note that in the front-end, you will see a β€œreward-sharing ratio”, which refers to (1βˆ’Οƒ)(1 - \sigma)(1βˆ’Οƒ), which means when reward-sharing ratio is 60%, Οƒ\sigmaΟƒ is 0.4. This ratio is set by training nodes and validators permissionlessly.

Let's assume rewards for a given training node and its delegator ( fi)f_i)fi​) is 58,084, Οƒ\sigmaΟƒ to be 4, the training node stakes 3,000 and the delegator delegate 1,000. Consider Ο΅\epsilon Ο΅ to be 1 in this example. The reward for the delegator alone (excluding that to the training node) is:

fiβ‹…(1βˆ’Οƒ)β‹…tdtn+td=58,084Γ—(0.6Γ—10003000+1000)β€…β€Šβ‰ˆβ€…β€Š8,712.6f_i \cdot (1-\sigma)\cdot\tfrac{t_d}{t_n + t_d} = 58,084 \times \Bigl(0.6 \times \tfrac{1000}{3000 + 1000}\Bigr) \;\approx\; 8{,}712.6fi​⋅(1βˆ’Οƒ)β‹…tn​+td​td​​=58,084Γ—(0.6Γ—3000+10001000​)β‰ˆ8,712.6

Parameters like reward splits ( Ξ³\gammaΞ³) are fine-tuned through DAO voting. This democratized control keeps the ecosystem adaptive and equitable.

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