Strategyproof Decision-Making in Panel Data Settings and Beyond
This addresses the challenge of ensuring fair and effective decision-making in strategic environments like policy or product interventions, offering a novel approach to mitigate manipulation, though it is incremental in extending strategyproofness to panel data contexts.
The paper tackles the problem of designing strategyproof intervention policies in panel data settings where units can strategically modify pre-intervention outcomes to influence treatment assignments, establishing conditions for existence and providing algorithms for learning such policies. It shows that strategyproof mechanisms always exist for two interventions and can be learned for three or more under certain conditions, with empirical evaluation on real-world data demonstrating favorable performance compared to non-strategic baselines.
We consider the 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 utility-maximizing 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.