There are risks that are known, and others that are unknown. In worlds of known risks, statistical thinking can provide the optimal course of action. In worlds of unknown risks, however, understanding statistics does not suffice. In a world characterized by uncertainty, simple rules — known as heuristics — often lead to better decisions than complex algorithms. Heuristics can find smart solutions by focusing on only a few cues and ignoring the rest. This makes them not only more efficient but also easier to understand and apply. Heuristics are embodied in the sense that they can exploit capacities of the human mind (such as recognition memory), which facilitates quick judgments. They are anchored in the environment in the sense that they can exploit statistical or social structures (such as signal-to-noise ratio).

The study of heuristic decision-making is both descriptive and prescriptive. The descriptive part analyzes the adaptive toolbox, the repertoire of heuristics available to individuals,  organizations, or species. The study of the ecological rationality of heuristics is prescriptive and examines how they leverage the structure of the environment to achieve effective outcomes. The results of these studies are used for engineering, that is, intuitive design, such as to enable better, safer, and faster decision-making in applied fields. Examples include fast-and-frugal trees for doctors to allocate patients to intensive care units or for central banks to identify vulnerable banks, heuristics that use a single data point to predict the flu better than Google’s big data algorithms, and transparent credit scoring models that replace unnecessarily complicated and opaque models. It provides a novel and general account of decision-making to understand when and why more information, more time and more thinking, are not always better, and when and why less can be more.