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This document captures the decision package for issue #125: evaluate referred-user incentives for depositors and choose a rollout path that balances growth, revenue quality, and implementation risk.

Scope

In scope:
  • Quantitative comparison of options A-E.
  • Go/no-go recommendation with explicit acceptance gates.
  • Implementation-ready direction for follow-up tickets.
Out of scope:
  • Mainnet deployment.
  • Live token emissions.
  • Contract/frontend incentive implementation in this ticket.

Weighted Scoring Rubric

The weighted scoring model used by scripts/referrals/evaluate-incentives.ts:
CriterionWeightHow it is measured
Referral conversion lift35%Projected uplift vs baseline referred deposit rate
Net protocol revenue impact25%Incremental fee value minus incentive cost
Engineering complexity/risk20%Effort + delivery risk normalization
Abuse resistance10%Effective risk after controls coverage
Time-to-market10%Estimated delivery timeline score

Baseline Inputs

  • Baseline referred deposit rate: 12.00%
  • Average referred deposit size: $1,800
  • Average referred hold time: 52 days
  • Fee-derived value per referred user (flat regime): computed in script output
  • Fee-derived value per referred user (volatile regime): computed in script output
  • Staker reward sensitivity: reported per option in scenario CSV (stakerRewardSensitivityBps)

Scenario Design

All options are simulated across:
  • Adoption bands: low, base, high
  • Market regimes: flat, volatile
This yields 30 deterministic rows in:
  • scratch/KaironAI/ticket-125-scenarios.csv

Simulation outcome summary

Average across all six scenario cells per option:
RankOptionAvg weighted scoreAvg net revenue impactAvg conversion liftGate pass (conversion / revenue / abuse)
1C (performance fee discount)56.78+$1,738.47+15.45%3/6, 6/6, 6/6
2E (points)48.42+$966.16+6.76%0/6, 6/6, 0/6
3D (fixed USDC bonus)45.20+$109.06+12.56%1/6, 3/6, 6/6
4B (staking boost)41.49+$462.53+11.59%1/6, 6/6, 6/6
5A (vested esATLAS)40.32-$1,454.01+18.35%5/6, 0/6, 6/6
Interpretation:
  • Option C is the only path that combines top weighted score with positive net revenue and fully passing revenue/abuse gates.
  • Option A has strong conversion potential but fails revenue guardrails in this model due emission cost.
  • Option E is operationally light but fails conversion and abuse readiness gates for launch economics.

Abuse and Policy Checks Applied

The simulator includes these controls in the abuse-resistance pass/fail model:
  • featureFlag gate required for runtime kill-switching
  • treasuryCap for hard exposure limits
  • minQualifyingDeposit to reduce dust farming
  • holdPeriodRequired where relevant
  • freezeCode administrative freeze control
P1 abuse vectors are counted when hard controls are missing and/or effective risk exceeds threshold.

Acceptance Gates

A strategy should satisfy:
  • Referral conversion uplift >= +15%
  • Revenue drawdown cap <= 10%
  • Unresolved P1 abuse vectors = 0

Recommendation

Phase 1 default

Adopt Option C (performance fee discount) behind:
  • Feature flag
  • Hard treasury drawdown cap
  • Minimum qualifying deposit requirement
  • Abuse monitoring with freeze controls
Why:
  • Strong weighted performance in this evaluation.
  • Fastest practical path to tangible depositor value.
  • No token emission pressure.
  • In the base/high adoption scenarios, it meets conversion gates while preserving revenue and abuse constraints.

Phase 2 optional campaign

Conditionally run Option A (capped esATLAS vesting) only if Phase 1 proves sustained uplift above gate thresholds and budget controls remain intact. Why:
  • Useful as a growth campaign lever.
  • Emissions and implementation complexity are higher, so it should remain gated.

Not primary for initial launch

  • Option B: viable fallback if fee discount is blocked by fee-path complexity, but staker dilution risk is non-trivial.
  • Option D: can work as a temporary campaign but scales poorly and requires constant budget operations.
  • Option E: fast to ship but weak immediate economic pull and higher gaming risk per user value delivered.

How to reproduce

From repository root:
npx ts-node scripts/referrals/evaluate-incentives.ts
This rewrites:
  • scratch/KaironAI/ticket-125-scenarios.csv
Run twice and compare hashes to confirm determinism.