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When Online Algorithms Influence the Environment: A Dynamical Systems Analysis of the Unintended Consequences by Lankireddy, Prabhat; Nair, Jayakrishnan; Manjunath, D is a scholarly article available to read on EtoBox.

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We analyze the effect that online algorithms have on the environment that they are learning. As a motivation, consider recommendation systems that use online algorithms to learn optimal product recommendations based on user and product attributes. It is well known that the sequence of recommendations affects user preferences. However, typical learning algorithms treat the user attributes as static and disregard the impact of their recommendations on user preferences. Our interest is to analyze the effect of this mismatch between the model assumption of a static environment, and the reality of an evolving environment affected by the recommendations. To perform this analysis, we first introduce a model for a generic coupled evolution of the parameters that are being learned, and the environment that is affected by it. We then frame a linear bandit recommendation system (RS) into this generic model where the users are characterized by a state variable that evolves based on the sequence of recommendations. The learning algorithm of the RS does not explicitly account for this evolution and assumes that the users are static. A dynamical system model that captures the coupled evolution of

Author

Lankireddy, Prabhat; Nair, Jayakrishnan; Manjunath, D

Published

2024

Language

EN