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Hen­rik Arnes­jö

PhD fellow

Topics
Microeconomics Economics Artificial intelligence Machine learning Admission

Primary research areas

Market Design and Matching Markets

I study how assignment rules match people with institutions, such as students with universities. My research examines how preferences and institutional constraints form efficiency, strategic incentives and fairness.

Random Assignment and Group Comparisons

I focus on evaluating how institutions compare uncertain groups of students or workers using individual rankings based on criteria such as grades or job requirements. I use different ways to compare these groups to evaluate randomized allocation procedures.

Machine Learning in Matching

I explore how machine learning can support the design of matching mechanisms. My research examines whether neural models such as RegretNet preserve the information needed to adequately assess efficiency, fairness and strategic incentives, and how learned mechanisms compare with established allocation rules.

I study how to allocate life-changing opportunities better

How should universities allocate their seats, or how should employers fill jobs? I study how assignment rules can help institutions use limited capacity effectively, while taking people’s preferences into account. I aim to improve how opportunities such as access to education are allocated.

My work focuses on matching markets and randomized assignments. I investigate how institutions evaluate the groups of students or workers they receive, and how these evaluations affect the performance of such assignment mechanisms.

I support this by exploring how machine learning can help design allocation rules for specific assignment problems; whether these methods can offer effective, computationally practical solutions while meeting prescribed requirements.

Outside activities

I have no outside employments or activities