Stabilizing Performative Feedback Loops with Minimal Model Deployments

• URL: http://arxiv.org/abs/2609.14065v1
• Date: Sat, 12 Sep 2026 17:27:42 GMT
• Title: Stabilizing Performative Feedback Loops with Minimal Model Deployments
• Abstract summary: We show that it is possible to efficiently learn a prediction rule that is optimal for the distribution that it induces.<n>A core challenge in learning a performatively stable predictor is that, unlike supervised learning where distributions are fixed, the learner must deploy different predictors.<n>We present a new algorithmic procedure that finds a performatively stable model in nearly the minimum number of model deployments.
• Score: 32.283056647528845
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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