Heuristic and Linear Models of Judgment: Matching Rules and Environments

Hogarth, Robin M.; Karelaia, Natalia

doi:10.1093/acprof:oso/9780199744282.003.0013

Cited by 76 publications

(77 citation statements)

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“…Particularly, there was no significant difference between participants' and objective coefficients for the leadership attribute, t(71) = −2.38, p = 0.02 1 , the market-condition attribute, t(71) = 1.81, p = 0.075, and the competition-strength attribute, t(71) = 1.74, p = 0.087. However, the participants seemed to fail to learn the exact intercept coefficient, t(71) = 5.83, p < .001, and the larger weight assigned to the technology attribute, t(71) = −14.9, p < .001, which is consistent with past multiple-cue probability learning studies [7] in which people were found to have difficulties in learning non-uniform weights.…”

Section: Results Of the Probability Judgment Stagesupporting

confidence: 80%

“…Research that focuses on the judgment stage typically employs a multiple-cue probability learning (MCPL) paradigm [7], in which each event outcome is probabilistically associated with several task cues and the participants need to learn the probabilistic associations and predict the outcome in the test trials. Research that focuses on the decision making stage usually presents the participants with small gambles in which each trial has a safe option and a risky option from which to choose.…”

Section: The Design Of the Experimentsmentioning

confidence: 99%

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Designing Information for Remediating Cognitive Biases in Decision-Making

Zhang

Bellamy

Kellogg

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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Software is playing an increasingly important role in supporting human decision-making. Previous HCI research on decision support systems (DSS) has improved the information visualization aspect of DSS information design, but has somewhat overlooked the cognitive aspect of decision-making, namely that human reasoning is heuristic and reflects systematic errors or cognitive biases. We report on an empirical study of two cognitive biases: conservatism and loss aversion. Two remediation techniques recommended by previous research were tested: the expected return method, an actuarial-inspired approach presenting objective metrics; and bootstrapping, a technique successful in improving judgment consistency. The results show that the two biases can occur simultaneously and can have a huge impact on decision-making. The results also show that the two debiasing techniques are only partly effective. These findings suggest a need for more research on debiasing, and indicate some directions for exploring debiasing techniques and building decision support systems.

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Section: Results Of the Probability Judgment Stagesupporting

confidence: 80%

Section: The Design Of the Experimentsmentioning

confidence: 99%

Designing Information for Remediating Cognitive Biases in Decision-Making

Zhang

Bellamy

Kellogg

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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“…) Our perspective also speaks to the predictive accuracy of some heuristic decision processes that typically ignore information and involve simple decision rules (Gigerenzer & Gaissmaier, 2011). Successful heuristics exploit two key features of the environment: how information is aggregated and redundancy (Hogarth & Karelaia, 2007). As such, they operate in the intersection of L and T. For example, when people employ the recognition heuristic to select one of two alternatives (Goldstein & Gigerenzer, 2002), they base their judgments on information available in memory that happens to be correlated with what they are trying to predict.…”

Section: Features Of Wickednessmentioning

confidence: 99%

The Two Settings of Kind and Wicked Learning Environments

Hogarth

Lejarraga

Soyer

2015

Curr Dir Psychol Sci

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131

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Inference involves two settings: In the first, information is acquired (learning); in the second, it is applied (predictions or choices). Kind learning environments involve close matches between the informational elements in the two settings and are a necessary condition for accurate inferences. Wicked learning environments involve mismatches. This conceptual framework facilitates identifying sources of inferential errors and can be used, among other things, to suggest how to target corrective procedures. For example, structuring learning environments to be kind improves probabilistic judgments. Potentially, it could also enable economic agents to exhibit maximizing behavior.

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“…A difference between the two studies was that in the former the true utility of options was assumed to equal the value of a linear combination of attribute values, while in the latter the true utility was exogenously given. Further analyses have uncovered mathematical reasons for the success of lexicographic heuristics [59], provided the attributes are sorted correctly before applying sequential elimination of alternatives. For example, the heuristics are optimal if and only if attribute validities are very far apart [60], or there exists a cumulatively dominating option.…”

Section: The Behavioral Model: Noncompensatory Heuristicsmentioning

confidence: 99%