PP-Gym: Unifying Environments, Learning, and Evaluation for Performative Prediction

Performative prediction (PP) studies learning when deployed decisions reshape subsequent data, as prices change demand. Existing implementations often tie algorithms to particular response models and feedback assumptions, making reuse and comparison across settings depend on custom integration. We introduce PP-Gym, a unified framework that separates environments, observations, learning algorithms and evaluation through a common deployment–feedback interface. It supports reusable algorithm implementations and comparisons with controlled information access, population responses and deployment counts. Across 13 tasks spanning immediate and gradual responses and individual-record or aggregate feedback, we evaluate 26 methods and variants. Building on this shared interface, we introduce the Trust-Region Cross-Loss Surrogate (TR-CLS), a simple learner that models deployment-dependent loss and selects new decisions from individual records or aggregate feedback. Under the shared protocol, TR-CLS significantly lowers average loss during learning on nine tasks relative to baselines selected on development data, including a 14.3% increase in revenue in simulated ten-product pricing. These results demonstrate the value of studying shared learning strategies across PP settings; PP-Gym provides the experimental infrastructure to develop them and test where their gains persist.