Look-Ahead Reasoning on Learning Platforms

Haiqing Zhu, Tijana Zrnic, Celestine Mendler-Dünner

Keywords: performative prediction, strategic classification, collective action, performative shift, performativity

TL;DR: We analyze how collective strategic behavior influences predictive models, introducing level-$k$ reasoning and showing how user coordination affects equilibrium outcomes.

Abstract:

Predictive models are often designed to minimize risk for the learner, yet their objectives do not always align with the interests of the users they affect. Thus, as a way to contest predictive systems, users might act strategically in order to achieve favorable outcomes. While past work has studied strategic user behavior on learning platforms, the focus has largely been on strategic responses to the deployed model, without considering the behavior of other users, or implications thereof for the deployed model. 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 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.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 26007