Status: Accepted Date: 2026-07
Comparing workqueue strategies needs a simulation, and simulations invite two failure modes: modeling precision the process does not have, and comparing strategies across different random outcomes so the "winner" is partly luck.
A day-step model (not continuous discrete-event simulation) with common random numbers: each (account, touch_number) pair maps to a seeded RNG stream, so every strategy sees the identical draw for the identical touch.
- AR follow-up is genuinely daily-batched: analysts work a queue that refreshed overnight, and capacity is expressed in touches per analyst-day. An event-calendar simulator would add machinery to model intra-day timing nobody staffs against.
- Common random numbers are the standard variance-reduction technique for paired comparisons, and here they carry the whole argument: with independent draws, a strategy could "win" because its accounts happened to roll well. Keying draws by (account, touch) makes outcome differences attributable to ordering alone, and the determinism tests pin this property.
- The write-off mechanic (timely filing deadlines) is what makes the question non-trivial. Without deadlines every strategy collects everything eventually and the comparison is vacuous; with them, ordering decides which dollars die.
- Recovery decay (1.5% per day of age, floored) and the 7-day payer response cycle are stylized parameters, stated in one place, and the right way to use this tool is sensitivity analysis around them, not belief in any single run.
- Results are point estimates from one seeded corpus per run; the experiment accepts a seed precisely so multi-seed replication is a shell loop, which the README demonstrates.
- The accounting identity (collected + written off + still open equals corpus total) must hold for every strategy and is asserted in CI as a conservation check on the simulator itself.