G
Gerd Gigerenzer
Researcher at Max Planck Society
Publications - 548
Citations - 56176
Gerd Gigerenzer is an academic researcher from Max Planck Society. The author has contributed to research in topics: Heuristics & Rationality. The author has an hindex of 94, co-authored 533 publications receiving 52356 citations. Previous affiliations of Gerd Gigerenzer include University of Konstanz & Ludwig Maximilian University of Munich.
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Simple Heuristics That Make Us Smart
Gerd Gigerenzer,Peter M. Todd +1 more
TL;DR: Fast and frugal heuristics as discussed by the authors are simple rules for making decisions with realistic mental resources and can enable both living organisms and artificial systems to make smart choices, classifications, and predictions by employing bounded rationality.
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Reasoning the fast and frugal way: models of bounded rationality.
TL;DR: The authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making, and found that these fast and frugal algorithms violate fundamental tenets of classical rationality: they neither look up nor integrate all information.
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Heuristic Decision Making
TL;DR: Research indicates that individuals and organizations often rely on simple heuristics in an adaptive way, and ignoring part of the information can lead to more accurate judgments than weighting and adding all information, for instance for low predictability and small samples.
Journal Article
Bounded rationality: The adaptive toolbox
Gerd Gigerenzer,Reinhard Selten +1 more
TL;DR: In this article, the concept of adaptive toolboxes is used to describe a set of fast and frugal rules for decision making under uncertainty, and the strategies in the adaptive toolbox dispense with optimization and, for the most part, with calculations of probabilities and utilities.
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How to Improve Bayesian Reasoning Without Instruction: Frequency Formats
Gerd Gigerenzer,Ulrich Hoffrage +1 more
TL;DR: By analyzing several thousand solutions to Bayesian problems, the authors found that when information was presented in frequency formats, statistically naive participants derived up to 50% of all inferences by Bayesian algorithms.