Is Adaptation Worth the Cost? Economics-Guided Test-Time Adaptation under Budget Constraints

Test-Time Adaptation (TTA) enables models to dynamically adjust to non-stationary test distributions through online updates during inference. However, when computational resources are limited, existing methods with pre-defined update schedules may waste budget on low-value or unreliable batches, leading to suboptimal overall utility. This raises a critical yet overlooked question: under a limited test-time budget, which adaptations are truly worth performing? To address this issue, we propose **ECO-TTA**, an **ECO**nomics-guided **TTA** framework that reformulates adaptation as a cost-aware investment process. Specifically, ECO-TTA defines a multi-level action space and treats each candidate action as a purchasable asset, whose price is its adaptation cost and value is its performance payoff. To estimate this payoff without labels, ECO-TTA derives an efficient value estimator that transfers realized adaptation gains from statistically similar historical batches to predict the expected return of the current asset. It further designs an adaptive budget controller based on the shadow price, which quantifies the opportunity cost of current budget consumption and adjusts the cost penalty along budget consumption trajectories. Together, these designs establish a unified cost-benefit criterion for online decision-making, enabling an adaptive trade-off between limited budgets and high-value updates. Our extensive experiments demonstrate the consistent superiority of ECO-TTA across diverse shift types, TTA scenarios, and budget settings. Code will be publicly available.