The systems-theoretic perspective on algorithms can be extended to address the challenge of performative prediction by considering the feedback loop between the algorithm and the environment. In performative prediction, the decisions made by the algorithm impact the data it receives, leading to a dynamic and evolving data-generating process. By viewing this interaction as a feedback system, similar to control systems, we can analyze how the algorithm's actions influence the environment and how the environment, in turn, affects the algorithm's performance. This perspective allows for a deeper understanding of the interplay between the algorithm and the data it operates on, enabling the development of more robust and adaptive algorithms in scenarios where performative prediction is a concern.