Gaming Helps! Learning from Strategic Interactions in Natural Dynamics

Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, Juba Ziani

Research output: Contribution to journalConference articlepeer-review

Abstract

We consider an online regression setting in which individuals adapt to the regression model: arriving individuals are aware of the current model, and invest strategically in modifying their own features so as to improve the predicted score that the current model assigns to them. Such feature manipulation has been observed in various scenarios-from credit assessment to school admissions-posing a challenge for the learner. Surprisingly, we find that such strategic manipulations may in fact help the learner recover the meaningful variables-that is, the features that, when changed, affect the true label (as opposed to non-meaningful features that have no effect). We show that even simple behavior on the learner's part allows her to simultaneously i) accurately recover the meaningful features, and ii) incentivize agents to invest in these meaningful features, providing incentives for improvement.

Original languageAmerican English
Pages (from-to)1234-1242
Number of pages9
JournalProceedings of Machine Learning Research
Volume130
StatePublished - 2021
Event24th International Conference on Artificial Intelligence and Statistics, AISTATS 2021 - Virtual, Online, United States
Duration: 13 Apr 202115 Apr 2021

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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