Look-Ahead Reasoning on Learning Platforms
This work addresses strategic interactions in learning platforms, offering insights into user coordination and alignment, but it is incremental as it builds on existing frameworks like strategic classification and performative prediction.
The paper tackles the problem of strategic user behavior on learning platforms by introducing look-ahead reasoning, which accounts for coupled user actions and their impact on future predictions, showing that while level-k thinking accelerates convergence, it does not change the equilibrium, and collective reasoning reveals alignment between learner and user utilities as a key concept.
On many learning platforms, the optimization criteria guiding model training reflect the priorities of the designer rather than those of the individuals they affect. Consequently, users may act strategically to obtain more favorable outcomes, effectively contesting the platform's predictions. While past work has studied strategic user behavior on learning platforms, the focus has largely been on strategic responses to a deployed model, without considering the behavior of other users. In contrast, look-ahead reasoning takes into account that user actions are coupled, and -- at scale -- impact future predictions. Within this framework, we first formalize level-$k$ thinking, a concept from behavioral economics, where users aim to outsmart their peers by looking one step ahead. We show that, while convergence to an equilibrium is accelerated, the equilibrium remains the same, providing no benefit of higher-level reasoning for individuals in the long run. Then, we focus on collective reasoning, where users take coordinated actions by optimizing through their joint impact on the model. By contrasting collective with selfish behavior, we characterize the benefits and limits of coordination; a new notion of alignment between the learner's and the users' utilities emerges as a key concept. We discuss connections to several related mathematical frameworks, including strategic classification, performative prediction, and algorithmic collective action.