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Categorization with limited resources: A family of simple heuristics

Categorization with limited resources: A family of simple heuristics
Categorization with limited resources: A family of simple heuristics
In categorization tasks where resources such as time, information, and computation are limited, there is pressure to be accurate, and stakes are high–as when deciding if a patient is under high risk of having a disease or if a worker should undergo retraining–, it has been proposed that people use, or should use, simple adaptive heuristics. We introduce a family of deterministic, noncompensatory heuristics, called fast and frugal trees, and study them formally. We show that the heuristics require few resources and are also relatively accurate. First, we characterize fast and frugal trees mathematically as lexicographic heuristics and as noncompensatory linear models, and also show that they exploit cumulative dominance (the results are interpreted in the language of the paired comparison literature). Second, we show, by computer simulation, that the predictive accuracy of fast and frugal trees compares well with that of logistic regression (proposed as a descriptive model for categorization tasks performed by professionals) and of classification and regression trees (used, outside psychology, as prescriptive models).
0022-2496
352-361
Martignon, Laura
2f1ca835-34fb-4cea-948c-20c7e06f1259
Katsikopoulos, Konstantinos V.
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Woike, Jan K.
749bf729-eee7-4b6f-9620-d084b7ce1698
Martignon, Laura
2f1ca835-34fb-4cea-948c-20c7e06f1259
Katsikopoulos, Konstantinos V.
b97c23d9-8b24-4225-8da4-be7ac2a14fba
Woike, Jan K.
749bf729-eee7-4b6f-9620-d084b7ce1698

Martignon, Laura, Katsikopoulos, Konstantinos V. and Woike, Jan K. (2008) Categorization with limited resources: A family of simple heuristics. Journal of Mathematical Psychology, 52 (6), 352-361. (doi:10.1016/j.jmp.2008.04.003).

Record type: Article

Abstract

In categorization tasks where resources such as time, information, and computation are limited, there is pressure to be accurate, and stakes are high–as when deciding if a patient is under high risk of having a disease or if a worker should undergo retraining–, it has been proposed that people use, or should use, simple adaptive heuristics. We introduce a family of deterministic, noncompensatory heuristics, called fast and frugal trees, and study them formally. We show that the heuristics require few resources and are also relatively accurate. First, we characterize fast and frugal trees mathematically as lexicographic heuristics and as noncompensatory linear models, and also show that they exploit cumulative dominance (the results are interpreted in the language of the paired comparison literature). Second, we show, by computer simulation, that the predictive accuracy of fast and frugal trees compares well with that of logistic regression (proposed as a descriptive model for categorization tasks performed by professionals) and of classification and regression trees (used, outside psychology, as prescriptive models).

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e-pub ahead of print date: 4 June 2008
Published date: December 2008

Identifiers

Local EPrints ID: 415450
URI: https://eprints.soton.ac.uk/id/eprint/415450
ISSN: 0022-2496
PURE UUID: 92bfc1c6-8577-4ae1-8e0b-9f84897cf4e4

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Date deposited: 10 Nov 2017 17:30
Last modified: 19 Jul 2019 17:51

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