Mismanaging Diagnostic Accuracy Under Congestion

To study the effect of congestion on the fundamental tradeoff between diagnostic accuracy and speed, we empirically test the predictions of a formal sequential testing model in a setting where the gathering of additional information can improve diagnostic accuracy but may also take time and increase congestion as a result. The efficient management of such systems requires a careful balance of congestion-sensitive stopping rules. These include diagnoses made based on very little or no diagnostic information and the stopping of diagnostic processes while waiting for information. We test these rules under controlled laboratory conditions and link the observed biases to system dynamics and performance. Our data show that decision makers (DMs) stop diagnostic processes too quickly at low congestion levels where information acquisition is relatively cheap. However, they fail to stop quickly enough when increasing congestion requires the DM to diagnose without testing or diagnose while waiting for test results. Essentially, DMs are insufficiently sensitive to congestion. As a result of these behavioral patterns, DMs manage the system with both lower-than-optimal diagnostic accuracy and higher-than-optimal congestion cost, underperforming on both sides of the accuracy/speed tradeoff.

History: This paper has been accepted for the Operations Research Special Issue on Behavioral Queueing Science.

Funding: This research was partially funded by the Deutsche Forschungsgemeinschaft [Grant VE 897/4-1].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2292.