{"pk":29425,"title":"Loss Functions Modulate the Optimal Bias-Variance Trade-off","subtitle":null,"abstract":"Prediction problems vary in the extent to which accuracy isrewarded and inaccuracy is penalized—i.e., in their loss func-tions. Here, we focus on a particular feature of loss functionsthat controls how much large errors are penalized relative tohow much precise correctness is rewarded: convexity. Weshow that prediction problems with convex loss functions (i.e.,those in which large errors are particularly harmful) favor sim-pler models that tend to be biased, but exhibit low variability.Conversely, problems with concave loss functions (in whichprecise correctness is particularly rewarded) favor more com-plex models that are less biased, but exhibit higher variabil-ity. We discuss how this relationship between the bias-variancetrade-off and the shape of the loss function may help explainfeatures of human psychology, such as dual-process psychol-ogy and fast versus slow learning strategies, and inform statis-tical inference.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Judgment; decision-making; dual-process theory;statistics"}],"section":"Complex Dynamics","is_remote":true,"remote_url":"https://escholarship.org/uc/item/0cw501c4","frozenauthors":[{"first_name":"Adam","middle_name":"","last_name":"Bear","name_suffix":"","institution":"Harvard University","department":""},{"first_name":"Fiery","middle_name":"","last_name":"Cushman","name_suffix":"","institution":"Harvard University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2020-01-01T10:00:00-08:00","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/29425/galley/19285/download/"}]}