Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/251899 
Year of Publication: 
2022
Series/Report no.: 
ESMT Working Paper No. 22-01
Publisher: 
European School of Management and Technology (ESMT), Berlin
Abstract: 
To study the effect of congestion on the fundamental trade-off 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 shows that decision makers (DMs) stop diagnostic processes too quickly at low congestion levels where information acquisition is relatively cheap. But 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 trade-off.
Subjects: 
Congestion
Diagnostic accuracy
Experiments
Partially Observable Markov Decision Process
Path-dependent Decision Making
Undertesting
Task Completion Bias
JEL: 
D43
D86
L42
K21
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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