Gerd Gigerenzer (b. 1947) is a German psychologist who offers the most influential alternative to the Kahneman-Tversky heuristics and biases framework. Rather than viewing heuristics as sources of systematic error, Gigerenzer argues that simple heuristics are adaptive tools that evolved to make good decisions in uncertain, real-world environments. His "fast and frugal" heuristics program demonstrates that simple decision rules can match or outperform complex statistical models, particularly when information is limited, time is short, and the future is uncertain.
Fast and Frugal Heuristics
Gigerenzer and colleagues have identified and formalized specific heuristics: the recognition heuristic (if one option is recognized and the other is not, infer the recognized one has the higher value), the take-the-best heuristic (look up cues in order of validity and choose the option favored by the first cue that discriminates), and the 1/N heuristic (allocate resources equally across options). These heuristics work well because they exploit the structure of natural environments — they are "ecologically rational" rather than logically rational.
Gigerenzer's most provocative finding is the "less-is-more" effect: in many real-world prediction tasks, simple heuristics that use only one or a few cues outperform multiple regression and other complex models. This occurs because complex models overfit noisy data, capturing random variation that does not generalize to new cases. Simple heuristics, by ignoring information, are more robust to noise and generalize better. This finding challenges the assumption that more information and more computation always lead to better decisions.