Beyond Rational Illusion: Behaviorally Realistic Strategic Classification

Researchers have proposed a new framework for behaviorally realistic strategic classification, addressing the limitations of existing frameworks that assume strict rationality in decision-making.

Existing strategic classification (SC) frameworks rely on the assumption that agents are strictly rational, but real-world decision-making is often influenced by cognitive biases, deviating from pure rationality^[1]. To formalize this limitation, the behaviorally realistic strategic classification problem has been defined. The Prospect-Guided Strategic Framework (Pro-SF) is a principled framework grounded in prospect theory, designed to model and learn under behaviorally realistic strategic responses. Pro-SF incorporates three key mechanisms inspired by prospect theory, including the asymmetry between benefits and costs, different subjective reference points, and non-rational probability distortion^[1]. Strategic classification typically assumes the classifier is public knowledge, but regulation may require disclosure of only some information, allowing the learner to reveal a set or range of possible classifiers^[2]. Researchers have developed efficient algorithms for computing best-responses and training under this framework^[2]. Experiments on synthetic and real-world datasets have established Pro-SF as a behaviorally grounded approach to strategic classification, bridging machine learning and behavioral economics for more reliable deployment in the real world^[1].