arXiv:2605. 19674v2 Announce Type: replace Abstract: Strategic classification(SC) studies the interaction between decision models and agents who strategically manipulate their features for favorable outcomes.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2602. 11439v3 Announce Type: replace Abstract: Strategic classification studies the problem where self-interested individuals or agents manipulate their response to obtain favorable decision outcomes made by classifiers, typically turning to dishonest actions when they are less costly than genuine efforts.
arXiv:2606. 30136v1 Announce Type: new Abstract: Humans facing algorithmic decision systems have been found to ``game'' them by altering their input data (at a cost to them) in order to favorably change the algorithmic outcomes they receive (at a cost to the algorithm).
arXiv:2510. 10813v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly applied to domains that require reasoning about other agents' behavior, such as negotiation, policy design, and market simulation.
arXiv:2605. 19662v2 Announce Type: replace Abstract: Tabular foundation models based on pretrained prior-data fitted networks~(PFNs) have shown strong generalization on diverse tabular tasks, but they are typically designed for \emph{non-strategic} settings where data distributions are independent of deployed classifiers.
arXiv:2606. 00827v1 Announce Type: cross Abstract: Strategic classification (SC) investigates scenarios where agents manipulate their features to obtain favorable decisions from predictive models.
arXiv:2607. 11632v1 Announce Type: new Abstract: Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality.