How do people select among different strategies to accomplish a given task? Across disciplines, the strategy selection problem represents a major challenge. We propose a quantitative model that predicts how selection emerges through the interplay among strategies, cognitive capacities, and the environment. This interplay carves out for each strategy a cognitive niche, that is, a limited number of situations in which the strategy can be applied, simplifying strategy selection. To illustrate our proposal, we consider selection in the context of 2 theories: the simple heuristics framework and the ACT-R (adaptive control of thought-rational) architecture of cognition. From the heuristics framework, we adopt the thesis that people make decisions by selecting from a repertoire of simple decision strategies that exploit regularities in the environment and draw on cognitive capacities, such as memory and time perception. ACT-R provides a quantitative theory of how these capacities adapt to the environment. In 14 simulations and 10 experiments, we consider the choice between strategies that operate on the accessibility of memories and those that depend on elaborate knowledge about the world. Based on Internet statistics, our model quantitatively predicts people's familiarity with and knowledge of real-world objects, the distributional characteristics of the associated speed of memory retrieval, and the cognitive niches of classic decision strategies, including those of the fluency, recognition, integration, lexicographic, and sequential-sampling heuristics. In doing so, the model specifies when people will be able to apply different strategies and how accurate, fast, and effortless people's decisions will be.

In applied settings, such as aviation, medicine, and finance, individuals make decisions under various degrees of uncertainty, that is, when not all risks are known or can be calculated. In such situations, decisions can be made using fast-and-frugal heuristics. These are simple strategies that ignore part of the available information. In this article, we propose that the conceptual lens of fast-and-frugal heuristics is useful not only for describing but also for improving applied decision making. By exploiting features of the environment and capabilities of the decision makers, heuristics can be simple without trading off accuracy. Because decision aids based on heuristics build on how individuals make decisions, they can be adopted intuitively and used effectively. Beyond enabling accurate decisions, heuristics possess characteristics that facilitate their adaptation to varied settings. These characteristics include accessibility, speed, transparency, and cost effectiveness. Altogether, the article offers an overview of the literature on fast-and-frugal heuristics and their usefulness in diverse applied settings.

What cognitive capabilities allow Homo sapiens to successfully bet on the stock market, to catch balls in baseball games, to accurately predict the outcomes of political elections, or to correctly decide whether a patient needs to be allocated to the coronary care unit? It is a widespread belief in psychology and beyond that complex judgment tasks require complex solutions. Countering this common intuition, in this article, we argue that in an uncertain world actually the opposite is true: Humans do not need complex cognitive strategies to make good inferences, estimations, and other judgments; rather, it is the very simplicity and robustness of our cognitive repertoire that makes Homo sapiens a capable decision maker.

Can less information be more helpful when it comes to making medical decisions? Contrary to the common intuition that more information is always better, the use of heuristics can help both physicians and patients to make sound decisions. Heuristics are simple decision strategies that ignore part of the available information, basing decisions on only a few relevant predictors. We discuss: (i) how doctors and patients use heuristics; and (ii) when heuristics outperform information-greedy methods, such as regressions in medical diagnosis. Furthermore, we outline those features of heuristics that make them useful in health care settings. These features include their surprising accuracy, transparency, and wide accessibility, as well as the low costs and little time required to employ them. We close by explaining one of the statistical reasons why heuristics are accurate, and by pointing to psychiatry as one area for future research on heuristics in health care.

The recognition heuristic makes the strong claim that probabilistic inferences in which a recognized object is compared to an unrecognized one are made solely on the basis of whether the objects are recognized or not, ignoring all other available cues. This claim has been seriously challenged by a number of studies that have shown a clear effect of additional cue knowledge. In most of these studies, either recognition knowledge was acquired during the experiment, and/or additional cues were provided to participants. However, the recognition heuristic is more likely to be a tool for exploiting natural (rather than induced) recognition when inferences have to be made from memory. In our study on natural recognition and inferences from memory, around 85% of the inferences followed recognition information even when participants had learned three cues that contradicted recognition and when some of the contradictory cues were deemed more valid than recognition. Nevertheless, there were strong individual differences in the use of recognition. Whereas about half of the participants chose the recognized object regardless of the number of conflicting cues-suggestive of the hypothesized noncompensatory processing of recognition-the remaining participants were influenced by the additional knowledge. The former group of participants also tended to give higher estimates of recognition's validity. In addition, we found that the use of recognition for an inference may be affected by whether additional cue knowledge has been learned outside or within the experimental setting.