Stabilizing Performative Feedback Loops with Minimal Model Deployments

2026-09-12

AI Summary

本文提出了一种新算法，通过最少的模型部署次数来找到一个在高精度情况下表现稳定的预测模型，解决了由于算法和环境间的反馈循环导致的分布变化问题。

Abstract

When algorithmic predictions inform people's decisions, the models we deploy are performative and actively shape the data we see. This feedback loop between algorithms and their broader environments introduces a challenge in the mechanics of social prediction: If different predictive models induce different distributions, is it possible to efficiently learn a prediction rule that is optimal for the distribution that it induces? Formally, this solution concept is known as performative stability. A core challenge in learning a performatively stable predictor is that, unlike supervised learning where distributions are fixed, the learner must deploy different predictors and observe their induced distributions. The main contribution of our work is a new algorithmic procedure that, in the high-accuracy regime, finds a performatively stable model in nearly the minimum number of model deployments without making any assumptions regarding how predictions shape distributions. In particular, our procedure succeeds at finding a randomized performatively stable predictor using exponentially fewer model deployments than prior approaches. Our second main contribution is a structural result showing how this recent randomized notion of stability achieved by our algorithm can be derandomized into a single predictor satisfying the prior deterministic notion if one is willing to assume that the loss is well-conditioned and that performative effects are weak, as in early work in this area. On a technical level, our results come from building on an underexplored technical connection between performative stability and expected variational inequalities.

Problem

• performative feedback loops
• algorithmic predictions
• data distribution
• performative stability
• predictive models

Innovation

• performative stability
• minimal model deployments
• randomized predictor
• expected variational inequalities