Revisiting Algorithmic Bounded Rationality With Potential Solutions: How Recommendation Systems Amplify Cognitive Biases in Consumer Decision‐Making

ABSTRACT Algorithm‐driven platforms have become central to modern consumer decision‐making. While consumers have always been subject to bounded rationality, recent discussion suggests that personalized recommendation systems can further constrain consumer choices and, in turn, reinforce various cognitive biases. This article explores the theoretical underpinnings of bounded rationality in the context of algorithmically curated content, outlines key biases that are exacerbated by these systems, and discusses the societal implications of algorithmic confinement—from political polarization to consumer rights concerns. Finally, it proposes potential solutions, including policy‐based interventions, forced diversity mechanisms, and consumer education approaches that can mitigate the adverse effects of algorithm‐driven recommendation systems.