Categorization with Limited Resources: A Family of Simple Heuristics

Martignon, Laura; Katsikopoulos, Konstantinos V.; Woike, Jan K.

doi:10.1093/acprof:oso/9780199744282.003.0014

Cited by 43 publications

(71 citation statements)

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“…These results are in line with previous simulation results for non-compensatory, lexicographic heuristics in pair-comparison [21] and classification [22] tasks. This study also supports the claim that the study of strategies (here, strategies for estimation) cannot be separated from the study of environments in which these strategies are applied [23].…”

Section: Discussionsupporting

confidence: 92%

Estimating Quantities: Comparing Simple Heuristics and Machine Learning Algorithms

Woike

Hoffrage

Hertwig

2012

Artificial Neural Networks and Machine Learning – ICANN 2012

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Abstract. Estimating quantities is an important everyday task. We analyzed the performance of various estimation strategies in ninety-nine real-world environments drawn from various domains. In an extensive simulation study, we compared two classes of strategies: one included machine learning algorithms such as general regression neural networks and classification and regression trees, the other two psychologically plausible and computationally much simpler heuristics (QEst and Zig-QEst). We report the strategies' ability to generalize from training sets to new data and explore the ecological rationality of their use; that is, how well they perform as a function of the statistical structure of the environment. While the machine learning algorithms outperform the heuristics when fitting data, Zig-QEst is competitive when making predictions out-of-sample.

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Section: Discussionsupporting

confidence: 92%

Estimating Quantities: Comparing Simple Heuristics and Machine Learning Algorithms

Woike

Hoffrage

Hertwig

2012

Artificial Neural Networks and Machine Learning – ICANN 2012

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“…Martignon et al (2008) tested the accuracy of fast-and-frugal trees in 30 classification problems from fields such as medicine, sports, and economics. They reported that complex benchmark strategies including logistic regression excelled in data fitting, but fast-and-frugal trees were close or identical to these strategies in their predictive accuracy.…”

Section: Fast-and-frugal Treesmentioning

confidence: 99%

Heuristic Decision Making

Martignon

Gaissmaier

2011

Annu. Rev. Psychol.

2,784

1,740

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As reflected in the amount of controversy, few areas in psychology have undergone such dramatic conceptual changes in the past decade as the emerging science of heuristics. Heuristics are efficient cognitive processes, conscious or unconscious, that ignore part of the information. Because using heuristics saves effort, the classical view has been that heuristic decisions imply greater errors than do "rational" decisions as defined by logic or statistical models. However, for many decisions, the assumptions of rational models are not met, and it is an empirical rather than an a priori issue how well cognitive heuristics function in an uncertain world. To answer both the descriptive question ("Which heuristics do people use in which situations?") and the prescriptive question ("When should people rely on a given heuristic rather than a complex strategy to make better judgments?"), formal models are indispensable. We review research that tests formal models of heuristic inference, including in business organizations, health care, and legal institutions. This research indicates that (a) individuals and organizations often rely on simple heuristics in an adaptive way, and (b) 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. The big future challenge is to develop a systematic theory of the building blocks of heuristics as well as the core capacities and environmental structures these exploit.

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“…Finally, the doctor is asked to consider if there are any other symptoms from a short additional pre-specified list -if so, the patient is categorised as high risk; if not, she is categorised as low risk. The tree is said to be 'fast and frugal' because it uses just a few pieces of information, not all of which are always used (Martignon, Katsikopoulos and Woike, 2008). It can out-perform logistic regression, which exploits all of the information, in assigning patients correctly.…”

Section: Box 1: Fast-and-frugal Treesmentioning

confidence: 99%

“…There are, however, two reasons opposing this argument. First, if While intuitive approaches may be very useful, it is also interesting to consider how FFTs for assessing bank vulnerability may be constructed via an algorithm (see also Martignon et al, 2008 andLuan, Schooler and. To do this, we take the same three / four individual indicators used above but order the cues according to their loss for the function 0.…”

mentioning

confidence: 99%