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Abstract:
Choice architecture and recommender systems both address information overload but have developed largely independently of each other and make strong assumptions about decision- makers’ unobserved preferences. In this paper, we introduce cognitive information filters as an algorithmic approach to choice architecture that mitigates information overload in a more principled and effective manner: our method combines machine learning with a cognitive model of choice behavior to solve the economic problem of nudging or persuading decision-makers by tailoring information to their revealed preferences and cognitive constraints. We first develop a rational-inattention model of multi-attribute choice to describe the behavior of a consumer (receiver) facing information costs. We then use reinforcement learning to solve the information design problem of a sender choosing which options and attributes are accessible to the receiver. Observing only the receiver’s choices, the sender learns from repeated interactions which information is most effective in attaining desirable behavioral outcomes. By inferring preferences from boundedly rational behavior, our methodology can optimize for revealed welfare and hence promises to be (1) less paternalistic than traditional nudging and (2) less susceptible to misalignment than recommender systems optimizing for imperfect welfare proxies such as engagement. This has implications beyond economics and marketing, for example for digital platforms and alignment research in artificial intelligence.
