Beyond Rational Illusion: Behaviorally Realistic Strategic Classification
The paper titled 'Beyond Rational Illusion: Behaviorally Realistic Strategic Classification' introduces a new framework for strategic classification that accounts for cognitive biases in decision-making. The authors propose the Prospect-Guided Strategic Framework (Pro-SF) to model agents' strategic manipulations that deviate from strict rationality. This approach aims to bridge the gap between machine learning and behavioral economics for more reliable applications in real-world scenarios.
- ▪The research identifies a limitation in existing strategic classification frameworks that assume agents are strictly rational.
- ▪The proposed Pro-SF framework incorporates mechanisms inspired by prospect theory to capture behaviorally realistic strategic responses.
- ▪Experiments demonstrate that Pro-SF effectively addresses the behaviorally realistic strategic classification problem.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.19674 (cs) [Submitted on 19 May 2026] Title:Beyond Rational Illusion: Behaviorally Realistic Strategic Classification Authors:Xinpeng Lv, Yunxin Mao, Renzhe Xu, Chunyuan Zheng, Yikai Chen, Haoxuan Li, Yang Shi, Jinxuan Yang, Zhouchen Lin, Yuanlong Chen, Yuanxing Zhang, Shaowu Yang, Wenjing Yang, Haotian Wang View a PDF of the paper titled Beyond Rational Illusion: Behaviorally Realistic Strategic Classification, by Xinpeng Lv and 13 other authors View PDF HTML (experimental) Abstract:Strategic classification(SC) studies the interaction between decision models and agents who strategically manipulate their features for favorable outcomes. Existing SC frameworks typically rely on the idealized assumption that agents are strictly rational.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
