Diagnostic accuracy under accumulated pressure

• Applicants: Professor Dr. Mirko Kremer; Professor Francis de Véricourt, Ph.D.
• Subject Area: Management and Marketing ; Production Systems, Operations Management, Quality Management and Factory Planning
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 494799258

This project aims at understanding how time pressure in the form of congestion and task accumulation affect the accuracy and cost of diagnostic decisions. The management of diagnostic processes requires decision makers to dynamically balance the benefit of acquiring more diagnostic information against the cost of doing so. Many important decisions correspond to this type of problem including managing research projects, assessing the quality of remanufacturing goods and of course performing medical diagnoses. Recent epidemiologic publications, for instance, have suggested to run imperfect covid-19 tests multiple times on suspected cases so as to increase overall accuracy.Diagnostic and search problems such as these, however, are subject to high levels of congestion and delays. Yet, little is known about the effect of this prevalent form of time pressure on the behavior of decision makers and the overall performance of diagnostic processes.This project aims to fill this gap by studying the effect of congestion on diagnostic decisions using a combination of mathematical modelling and controlled experiments. Specifically, we seek to 1) uncover specific cognitive biases regarding how much diagnostic information decision makers collect, and what diagnoses they make based on that information, in systems that are prone to congestion, 2) measure the effect of these biases on the resulting diagnostic accuracy and cost, and 3) propose possible mechanisms to mitigate these biases. To address these research questions, we plan to run a series of controlled experiments in which subjects face a stream of diagnostic tasks that accumulate over time. We will also derive a normative benchmark to help uncover possible decision biases in our experiment. In addition, we intend to develop and provide the decision maker with different decision support tools, and evaluate to which extent these tools mitigate (or propagate) these decision making biases.

• DFG Programme: Research Grants