Two essays in behavioral operations management

PAPER 1 (knapsack problem): Selecting the most valuable projects given a finite budget constraint is a recurring decision challenge in all organizations. The optimization literature has long recognized the mathematical complexities of this knapsack problem. However, these complexities along with real-world data imperfections, such as how to fully determine the “values” of competing projects, have severely limited the adoption of optimization algorithms. Instead, decision makers employ mental heuristics. We explore the nature of these heuristics experimentally in a computer lab, and find them to be biased towards selecting too many small projects. We attribute this bias to a key structural characteristic of the decision makers' search process. Specifically, while they search for value-maximizing combinations of projects, they consistently keep their solutions within the feasible side of the budget boundary. They rarely generate infeasible solutions during their search, and then consider which projects to drop. We test two common strategies to debias decision makers: a problem framing that subtly nudges participants to search more in the infeasible solution space, and direct advice to participants to do so. We find that only the latter one reduces the small-project bias and improves resource allocation decisions. PAPER 2 (health inequalities): When demand for timely access to healthcare exceeds supply, doctors use available clinical information to prioritize patients at highest risk of adverse health outcomes. For many conditions, however, a patient's level of deprivation will also affect their risk. Do doctors take deprivation into account when they make their prioritization decisions? Using a high-volume urological procedure as case study, we provide evidence that patients living in the most deprived neighborhood quintile wait as long as anyone else for surgery, despite having a forty percent higher risk of harmful emergency events. This constitutes an avoidable health inequality. We argue that this health inequality cannot be addressed alone by prioritizing patients better based on existing clinical information, but that deprivation is a risk in its own right for our studied urological procedure. Building on these observations, we develop a queuing model to help administrators account for deprivation when scheduling surgeries.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2023

Author dc:creator

• Pape, Tom

Advisor dc:contributor.advisor

• Scholtes, Stefan

• behavioural operations managment

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/f7a32a44-b4ee-46c0-95be-28de239421e1/download
• http://purl.org/NET/rdflicense/allrightsreserved

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.114784

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/378309