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Strategyproof Decision-Making in Panel Data Settings and Beyond
Keegan Harris · Anish Agarwal · Chara Podimata · Steven Wu

We consider the classical problem of decision-making using panel data, in which a decision-maker gets noisy, repeated measurements of multiple units (or agents). We consider a setup where there is a pre-intervention period, when the principal observes the outcomes of each unit, after which the principal uses these observations to assign a treatment to each unit. Unlike this classical setting, we permit the units generating the panel data to be strategic, i.e. units may modify their pre-intervention outcomes in order to receive a more desirable intervention. The principal's goal is to design a strategyproof intervention policy, i.e. a policy that assigns units to their correct interventions despite their potential strategizing. We first identify a necessary and sufficient condition under which a strategyproof intervention policy exists, and provide a strategyproof mechanism with a simple closed form when one does exist. Along the way, we prove impossibility results for strategic multiclass classification, which may be of independent interest. When there are two interventions, we establish that there always exists a strategyproof mechanism, and provide an algorithm for learning such a mechanism. For three or more interventions, we provide an algorithm for learning a strategyproof mechanism if there exists a sufficiently large gap in the principal's rewards between different interventions. Finally, we empirically evaluate our model using real-world panel data collected from product sales over 18 months. We find that our methods compare favorably to baselines which do not take strategic interactions into consideration, even in the presence of model misspecification.

Author Information

Keegan Harris (Carnegie Mellon University)
Anish Agarwal (Amazon)
Chara Podimata (Harvard University)

I'm a rising fifth year PhD student in the EconCS group at Harvard University, where I am advised by Professor Yiling Chen. My research interests lie mostly on the intersection of Theoretical Computer Science, Economics, and Machine Learning and specifically on learning under the presence of strategic agents, online learning, and mechanism design. During the summer of 2019 and spring of 2020, I had the pleasure of being an intern at Microsoft Research in New York City, mentored by Jennifer Wortman Vaughan and Alex Slivkins respectively. Before joining Harvard, I was an intern for Google in Athens, Greece. I received my Diploma from the National Technical University of Athens, where I was advised by Professor Dimitris Fotakis.

Steven Wu (Carnegie Mellon University)

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