A new multi-stage sequential model with selective classifiers is proposed to characterize agent actions and design sequences that incentivize genuine improvement rather than gaming in strategic classification.
arXiv preprint arXiv:2011.01956 , year=
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6representative citing papers
Introduces IFSC framework modeling peer imitation in individual fairness-aware strategic classification to improve fairness consistency under interdependent manipulations.
Defines behaviorally realistic strategic classification and proposes Pro-SF, a prospect-theory-based framework to model non-rational agent manipulations in strategic classification.
The paper proposes Strategic Prior-data Fitted Network (SPN), an inference-time framework that adapts pretrained tabular foundation models (PFNs) to strategic manipulation by aligning predictions with approximated post-manipulation distributions via strategic in-context examples.
In a linear strategic-classification model where manipulation can genuinely improve outcomes, the optimal strategic classifier is a parallel shift of the Bayes boundary, and it is a provably better proxy for the improvement-aware objective than the Bayes classifier.
Introduces partial fairness awareness (PFA) and a belief-guided mechanism allowing strategic agents to align beliefs with a hidden grounding fairness constraint via iterative interaction.
citing papers explorer
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Sequential Strategic Classification with Multi-Stage Selective Classifiers
A new multi-stage sequential model with selective classifiers is proposed to characterize agent actions and design sequences that incentivize genuine improvement rather than gaming in strategic classification.
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Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation
Introduces IFSC framework modeling peer imitation in individual fairness-aware strategic classification to improve fairness consistency under interdependent manipulations.
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Beyond Rational Illusion: Behaviorally Realistic Strategic Classification
Defines behaviorally realistic strategic classification and proposes Pro-SF, a prospect-theory-based framework to model non-rational agent manipulations in strategic classification.
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When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
The paper proposes Strategic Prior-data Fitted Network (SPN), an inference-time framework that adapts pretrained tabular foundation models (PFNs) to strategic manipulation by aligning predictions with approximated post-manipulation distributions via strategic in-context examples.
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Linear Strategic Classification with Endogenous Improvements
In a linear strategic-classification model where manipulation can genuinely improve outcomes, the optimal strategic classifier is a parallel shift of the Bayes boundary, and it is a provably better proxy for the improvement-aware objective than the Bayes classifier.
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Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents
Introduces partial fairness awareness (PFA) and a belief-guided mechanism allowing strategic agents to align beliefs with a hidden grounding fairness constraint via iterative interaction.