Less-is-more effect
Knowing or using less can sometimes yield more accurate decisions.
What it means
The counterintuitive finding that having less information, recognizing fewer options, or using a simpler rule can produce better judgments than knowing or computing more. It arises in several ways: a person who recognizes only some objects can apply the recognition heuristic where a fully knowledgeable person cannot; and a frugal rule that ignores most cues can out-predict a complex model by avoiding overfitting. The effect is not a license for ignorance but a precise consequence of how partial knowledge and the environment's structure interact — it occurs when the recognized subset is more valid than chance, or when data are scarce and noisy. It is a cornerstone of the fast-and-frugal research program and a direct challenge to the assumption that more information and more computation always help. It matters for forecasting, expertise, and the design of decision aids that may be smarter for being simpler.
Examples
In sports forecasting studies, people with moderate knowledge sometimes beat experts, because they can exploit recognition where the experts recognize every team and lose that cue.
Asked which is bigger, San Diego or San Antonio, German students who had only heard of San Diego outscored American students who knew both cities well.
A hiring rule that ranks applicants on one strong predictor can beat a model weighting twelve traits, because the twelve weights are fitted to the quirks of past hires.
First described in Goldstein & Gigerenzer (2002); Gigerenzer & Brighton (2009).