Applied decision making with fast-and-frugal heuristics

By Sebastian Hafenbrädl, Daniel Waeger, Julian N. Marewski, Gerd Gigerenzer. 2016

109 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, General Decision Sciences, Sociology and Political Science, Game & Decision Theory, Cognitive Science & Psychology

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, methodology): “These simple decision rules have been used to model and aid human decisions in numerous tasks with possible moral implications, for example, in medical diagnosis (Hafenbrädl et al., 2016) or classification of oncoming traffic at military checkpoints as hostile or friendly (Keller & Katsikopoulos, 20”
Citizens Versus the Internet: Confronting Digital Challenges With Cognitive Tools (2019, background): “Already in use in a variety of domains, including medicine, finance, law, and management, FFTs provide comprehensive prescriptive guides for real-world decision-making (Hafenbrädl et al., 2016).”
A systematic review of algorithm aversion in augmented decision making (2019, methodology): “For example, although the decision tree model advocated by Hafenbrädl et al. (2016) is indeed transparent and interpretable for a user, this type of algorithm is only suited to aid in binary classification tasks.”