"Take-the-best" and other simple strategies: Why and when they work "well" with binary cues

By Robin M. Hogarth, Natalia Karelaia. 2006

101 citations recorded by the source corpus.

The effectiveness of decision rules depends on characteristics of both rules and environments. A theoretical analysis of environments specifies the relative predictive accuracies of the lexicographic rule 'take-the-best' (TTB) and other simple strategies for binary choice. We identify three factors: how the environment weights variables; characteristics of choice sets; and error. For cases involving from three to five binary cues, TTB is effective across many environments. However, hybrids of equal weights (EW) and TTB models are more effective as environments become more compensatory. In the presence of error, TTB and similar models do not predict much better than a naive model that exploits dominance. We emphasize psychological implications and the need for more complete theories of the environment that include the role of error.

View Paper: https://www.semanticscholar.org/paper/0a28a20a19d9bda1f09e8d7bbd458e48aedff28e

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Citations in the corpus

Rationality for Mortals: How People Cope with Uncertainty (2008, unknown): “Noncompensatory information is a special case of large variability in cue weights, which also favors take-the-best relative to linear models and is discussed in Hogarth and Karelaia 2005 and 2006.”
Environments That Make Us Smart (2007, unknown): “In such environments, Take The Best can outperform the weighted additive model, particularly when generalizing to new decisions (Gigerenzer et al., 1999; Hogarth & Karelaia, 2006).”
On dual processing and heuristic approaches to moral cognition (2008, unknown): “We then discuss the educational implications of System 1 theories and conclude with some thoughts about the implications of this work for resetting the boundary between ethical theory and moral psychology.”
The recognition heuristic: A decade of research (2011, unknown): “…compensatory processes, and vice versa (e.g., higher cue redundancy favors noncompensatory processing whereas higher independence between cues favors compensatory processing; see Gigerenzer & Brighton, 2009; Hogarth & Karelaia, 2006; Katsikopoulos & Martignon, 2006; Martignon & Hoffrage, 2002).”
Fast and frugal forecasting (2009, unknown): “…as soon as a cue discriminates, (c) choose the alternative this cue favors Cue validities vary highly, moderate to high redundancy, scarce information (Hogarth & Karelaia, 2005, 2006; Martignon & Hoffrage, 1999, 2002) Made predictions equally or more accurate than regression (Czerlinski,…”
Why Heuristics Work (2008, unknown): “Cue validities vary little, low redundancy (Hogarth & Karelaia, 2005, 2006).”
Homo Heuristicus: Why Biased Minds Make Better Inferences (2009, background): “The bias–variance dilemma is one of several principles that characterize the ecological rationality of heuristics (Gigerenzer et al., 1999; Hogarth & Karelaia, 2005, 2006; Katsikopoulos & Martignon, 2006; Todd et al., in press).”
Lessons for artificial intelligence from the study of natural stupidity (2019, background): “…to negative ones (losses), causing risk seeking in this domain. environment in which tallying and take-the-best do and do not perform well 66,67 , and have demonstrated that these heuristics too might have a rational formalization—in this case, as Bayesian regression with extreme priors 68 .”
Why recognition is rational: Optimality results on single-variable decision rules (2010, unknown): “1 Hogarth and Karelaia (2006) conducted a related analysis using binary rather than continuous cues (predictors ).”