Leader: Kiran Gajwani
Team: Danielle Doyle, Jeffrey Miron
Across the University, we face situations where the fundamental challenge is allocating individuals to a limited number of slots based on individuals’ preferences, seniority, prerequisites, concentrations, affiliations, and so on. Our innovation is to create an automated, efficient, and flexible computer tool for these allocation challenges.
Examples of allocation challenges at Harvard are: allocating TFs based on TF and instructor preferences; assigning students to one course in a series, when one can’t use the Sectioning tool; creating course-specific enrollment lotteries for oversubscribed courses; assigning work to committees based on committee members’ preferences; assigning individuals to groups or choosing individuals for slot-constrained events, based on preferences or other attributes; and more.
Our goal is to create a user-friendly, freely available, web-based allocation tool that will efficiently and effectively handle the large variety of allocation problems that exist across the University. Our tool will be flexible to the user’s choice of allocation method for their particular problem.
Optimal allocation problems are complex and depend on how one thinks about individuals’ well-being. Thus, we will use research on allocation algorithms and welfare maximization to create a tool that will produce fair outcomes while saving time and eliminating errors.
Achievements: The Automated Allocation Innovation (AAI) team successfully built a tool that will be launching to the general public in August 2017. AAI will garner feedback from users to improve functionality and efficiency over the next several months. AAI will partner with HUIT and my.harvard to determine the best way to incorporate the tool into the University’s existing systems.