Categorization with limited resources: A family of simple heuristics

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

doi:10.1016/j.jmp.2008.04.003

Cited by 170 publications

(146 citation statements)

References 39 publications

Supporting

Mentioning

142

Contrasting

Unclassified

Order By: Relevance

“…This is in line with recent research using experimental methodology showing that compensatory judgment models (e.g., linear models) may outperform F&F models [15,[22][23][24] when applied to behavioral judgment data or that the validity of F&F models may have been overestimated [25].…”

Section: Discussionsupporting

confidence: 90%

“…This finding was paralleled by a superior predictive accuracy of some F&F-models, particularly the take the best heuristic [5]. Martignon et al [15] found evidence for over-fitting in the LR models when comparing LR to two F&F models in a crossvalidation study.…”

Section: Methodsmentioning

confidence: 99%

“…In studies where cross-validation was used, the LR models have used all the cues in the model [15]. Using all the cues in the model leads to an artificial lowering of predictive accuracy after cross-validation because the LR-model is over-fitted to the original data.…”

Section: Number Of Cues In the Modelmentioning

confidence: 99%

“…Empirical results are seemingly contradictory of whether F&F models provide higher predictive accuracy than LR models [6,10,[12][13][14][15][16]. Two groups of studies can be discerned that may be denoted as ecological and judgment studies, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Two groups of studies can be discerned that may be denoted as ecological and judgment studies, respectively. The former group of studies concern ecological relations such as cities being state capitals or not depending on having a soccer team or not [7,13,15]. Thus, in these studies not only the independent variables, but also the dependent variables denoted ecological or actual facts, rather than human judgments or decisions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Statistical and Psychological Models of Doctors’ Judgments of Heart Patients

Kerimi¹,

Bäcklund²,

Skånér³

et al. 2017

J Clin Exp Cardiolog

View full text Add to dashboard Cite

Using participant data from the medical domain, the robustness of logistic regression (LR) with different cue inclusion levels and two fast and frugal (F&F) models in terms of predictive accuracy and frugality were tested. Two data sets based on judgments of verbally described patients were used: Heart failure (66 analysts), and Hyperlipidemia (38 analysts). In both data sets, when the models were cross-validated, there was a significant decrease in predictive accuracy for all models, especially when all cues were used in LR. The other models had about equal predictive accuracy, also when comparisons were made with actual diagnoses, with a slight advantage for LR in the Heart failure study. LR using the 5% inclusion level was more frugal than F&F. These results emphasize the importance of using cross-validation and of choosing the proper significance levels for cue inclusion and when comparing different judgment models in medical decision making and other fields.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Methodsmentioning

confidence: 99%

Section: Number Of Cues In the Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Statistical and Psychological Models of Doctors’ Judgments of Heart Patients

Kerimi¹,

Bäcklund²,

Skånér³

et al. 2017

J Clin Exp Cardiolog

View full text Add to dashboard Cite

show abstract

Discriminant Analysis with Biomedical Examples

Wernecke

Lüttke

Multmeier

et al. 2021

Wiley StatsRef: Statistics Reference Online

View full text Add to dashboard Cite

This article describes discriminant analysis. First, a brief overview is provided followed by exploratory data analysis, discussions of allocation rules and their error rates, Bayes allocation rules, discrimination using normal models, nonparametric discrimination, logistic discriminant analysis, discrimination using artificial networks, tree‐based models, further methods of discrimination, and judging reliability. Finally, a section on software packages concludes the article. Numerous examples from medical applications support the understanding of the procedures presented.

show abstract

Decoding human behavior with big data? Critical, constructive input from the decision sciences

Katsikopoulos

Canellas

2022

AI Magazine

Self Cite

View full text Add to dashboard Cite

Big data analytics employs algorithms to uncover people's preferences and values, and support their decision making. A central assumption of big data analytics is that it can explain and predict human behavior. We investigate this assumption, aiming to enhance the knowledge basis for developing algorithmic standards in big data analytics. First, we argue that big data analytics is by design atheoretical and does not provide process-based explanations of human behavior; thus, it is unfit to support deliberation that is transparent and explainable. Second, we review evidence from interdisciplinary decision science, showing that the accuracy of complex algorithms used in big data analytics for predicting human behavior is not consistently higher than that of simple rules of thumb. Rather, it is lower in situations such as predicting election outcomes, criminal profiling, and granting bail. Big data algorithms can be considered as candidate models for explaining, predicting, and supporting human decision making when they match, in transparency and accuracy, simple, process-based, domain-grounded theories of human behavior. Big data analytics can be inspired by behavioral and cognitive theory.

show abstract

Categorization with limited resources: A family of simple heuristics

Cited by 170 publications

References 39 publications

Statistical and Psychological Models of Doctors’ Judgments of Heart Patients

Statistical and Psychological Models of Doctors’ Judgments of Heart Patients

Discriminant Analysis with Biomedical Examples

Decoding human behavior with big data? Critical, constructive input from the decision sciences

Contact Info

Product

Resources

About