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A Hierarchical Bayesian Modeling Approach to Searching and Stopping in Multi‐Attribute Judgment
Author(s) -
Ravenzwaaij Don,
Moore Chris P.,
Lee Michael D.,
Newell Ben R.
Publication year - 2014
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12119
Subject(s) - heuristics , computer science , bayesian probability , inference , bayesian inference , task (project management) , machine learning , artificial intelligence , population , german , simple (philosophy) , philosophy , demography , management , archaeology , epistemology , sociology , economics , history , operating system
In most decision‐making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the names of the cities, but they were able to collect different kinds of cues for both response alternatives (e.g., “Does this city have a university?”) before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, [Newell, B. R., 2011]) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take–the–best or the Weighted Additive heuristics.