Compare outcomes across departments over repeated cycles: Which evidence should resolve the uncertainty?
Track department-level outcomes over repeated cycles; aggregate improvement can coexist with a persistent self-reinforcing disparity.
The question
A university research office uses an assistant to prioritize grant-support requests. Historical priority labels favored departments with greater staffing, and those departments consequently received faster assistance, generating more complete future submissions. The office wants evidence that a proposed mitigation interrupts this feedback loop, but current aggregate response times look improved. Which evidence should resolve the uncertainty?
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- Ask department administrators whether prioritization feels more balanced.Administrator perceptions may identify concerns but cannot measure repeated allocation outcomes or feedback-loop persistence reliably.
- Use synthetic requests modeled on historical labels for stress testing.Synthetic requests may preserve historical bias and cannot by themselves establish changed outcomes in actual request cycles.
- Report the overall average response time after mitigation.An average can improve while disparities and reinforcement patterns persist across departments.
- Compare outcomes across departments over repeated cycles after mitigation. ✓Repeated, department-level outcome measurements can reveal whether historical advantage continues to reproduce itself despite improved aggregate timing.
The trap
For feedback loops, measure repeated downstream outcomes by affected groups, not only current aggregate performance. How to remember it
Track department-level outcomes over repeated cycles; aggregate improvement can coexist with a persistent self-reinforcing disparity.
How many of these would you get right?
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