Decision by sampling.

By Neil Stewart, N. Chater, Gordon D. A. Brown. Cognitive Psychology, 2006

716 citations recorded by the source corpus.

Subfields: Economics and Econometrics, General Decision Sciences, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/822fe4aac99ef8daac590b67426082e402a21693

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Citations in the corpus

The ordinal nature of emotions (2017, background): “[12], extended theories of relative judgment to economic decision making.”
What Comes to Mind (2010, neutral): “Stewart et al. (2006) study how agents form preferences over choices by sampling their past experiences, Osborne and Rubinstein (1998) study the determination of equilibrium in strategic settings where players sample the performance of different actions.”
The influence of training and experience on rater performance in scoring spoken language (2016, background): “…and behavioral economics which have asserted that the basic psychological process of magnitude judgment is one of comparison, where the item being judged is compared with other similar items located in the immediate environment or in memory (Laming, 1997; Stewart, Chater, and Brown, 2006).”
One and Done? Optimal Decisions From Very Few Samples (2014, background): “Stewart, Chater, and Brown (2006) suggested that a policy of making decisions through binary preference judgments among alternatives sampled from memory can account for an assortment of human judgment and decision-making errors.”
How to measure time preferences: An experimental comparison of three methods (2013, neutral): “This is not a particularly novel finding, as theories and findings of constructed preference (Johnson, Haubl, & Keinan, 2007; Stewart et al., 2006; Weber et al., 2007) and coherent arbitrariness (Ariely et al.”
Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates. (2012, background): “For example, in the psychology literature, Stewart, Chater, and Brown (2006) have proposed that the utility of a choice item is quantified as the probability that this item would be judged preferable to a randomly selected comparison item (see also Kornienko, 2010).”
Précis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning (2009, methodology): “In further work on matching bias, Yama (2001) devised a crucial experiment to contrast the matching bias and the information gain accounts.”
Rational approximations to rational models: alternative algorithms for category learning. (2010, background): “, 2008, in press), and the field of decision-making already has many sampling-based theories (Lee & Cummins, 2004; Ratcliff, 1978; Stewart et al., 2006; Vickers, 1979), but other possibilities exist.”