Algorithmic Reinforcement and the Co-Evolution of User Preferences Through a Mechanistic Analysis of Diversity Loss in Recommender Systems

Abstract

Recommender systems have evolved into adaptive infrastructures that mediate human attention, learning, and decision-making across digital environments. This paper presents a mechanistic analysis of how algorithmic reinforcement processes co-evolve with user preferences, producing a progressive reduction in informational diversity. By conceptualizing recommendation as a coupled dynamical system, the study explains how reinforcement learning architectures internalize behavioral feedback and transform transient user interactions into long-term preference structures. The analysis identifies a recursive mechanism in which both algorithmic policies and user cognition adapt toward equilibrium states that maximize predictability and engagement at the expense of novelty. Empirical findings and theoretical models from recent reinforcement learning research are synthesized to elucidate the dynamics of diversity loss as an emergent property of co-adaptation. The paper proposes a mechanistic framework that integrates stochastic exploration, entropy regularization, and temporal reward shaping to sustain informational variety in reinforcement-driven ecosystems. This approach reconceptualizes recommender systems as co-evolutionary environments where the preservation of diversity is a structural necessity for epistemic resilience, cognitive openness, and sustainable engagement.