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18 - Heuristic decision-making

Published online by Cambridge University Press:  05 June 2014

Michael D. Lee
Affiliation:
University of California, Irvine
Eric-Jan Wagenmakers
Affiliation:
Universiteit van Amsterdam
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Summary

Take-the-best

The take-the-best (TTB) model of decision-making (Gigerenzer & Goldstein, 1996) is a simple but influential account of how people choose between two stimuli on some criterion, and a good example of the general class of heuristic decision-making models (e.g., Gigerenzer & Todd, 1999; Gigerenzer & Gaissmaier, 2011; Payne, Bettman, & Johnson, 1990). TTB addresses decision tasks like “which of Frankfurt or Munich has the larger population?”, “which of a catfish and a herring is more fertile?”, and “which of these two professors has the higher salary?”.

TTB assumes that all stimuli are represented in terms of the presence or absence of a common set of cues. In the well-studied German cities data set, this means cities are represented in terms of nine cues, including whether or not they have an international airport, whether they have hosted the Olympics, and whether they have a football team in the Bundesliga. Associated with each cue in TTB is a “cue validity.” This validity measures the proportion of times that, for those pairs of stimuli where one has the cue and the other does not, the cue belongs to the stimulus that has the greater criterion value. For example, the cue “Is the city the national capital?” is highly valid because the capital city, Berlin, is also the most populous city.

The TTB model assumes that, when people decide which is the larger of two cities, they search the cues from highest to lowest validity, stopping as soon as a cue is found that one city has but the other does not. At this point, TTB says simply that people choose the city that has the cue. If all of the cues are exhausted, TTB assumes people guess.

Type
Chapter
Information
Bayesian Cognitive Modeling
A Practical Course
, pp. 224 - 236
Publisher: Cambridge University Press
Print publication year: 2014

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