arXiv:2611.06696v1 Announce Type: new Abstract: Recommender systems usually learn what people want from what they have done. Platforms could also ask people directly, but asking is costly: 98.8% of the prompts in our data went unanswered. Theories of statistical prediction imply that a person's own forecast adds little to a recommender algorithm built from many people's behavior. Theories of dispersed knowledge imply that people hold information about their own taste that the data lack. We test the two with the MovieLens Beliefs dataset. In 2023-2024, a homepage prompt asked members how they expected to rate movies they said they had not seen. Of 26,020 such expectations, 551 from 277 members were followed by a rating at least a day later. We compare each expectation with four benchmarks: the platform's own predicted rating, two collaborative-filtering models (matrix factorization and an item neighborhood), and a stack trained on other members' outcomes. The trained models use each member's latest ratings. People predict their own ratings more accurately than all four benchmarks (correlation 0.69, against 0.34 for MovieLens and 0.54 to 0.59 for the trained recommenders). Added to all the machine forecasts and features, the expectation lowers out-of-sample mean squared error by 26%. The gain holds when models are trained on earlier expectations and tested on later ones. A member's earlier expectations about other movies do not replace the current one. In ordering a member's own movies, the trained recommenders are statistically indistinguishable from the person. On old, heavily rated movies, the machines do as well or better. The results reconcile the two theories: the crowd's data suffice where they are rich, and the person adds information about the specific item. We derive four design principles for preference elicitation and recommender evaluation.