The anchoring bias reflects rational use of cognitive resources

Lieder, Falk; Griffiths, Thomas L.; Huys, Quentin J. M.; Goodman, Noah D.

doi:10.3758/s13423-017-1286-8

Cited by 167 publications

(150 citation statements)

References 108 publications

(212 reference statements)

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“…Ethical considerations should never be overlooked when putting prediction models into practice. Previous studies on anchoring effects suggested that the process of human decision making is easily influenced by an initial piece of information (LACE index or HOSPITAL score in the current study), which is hard to avoid [43]. Moreover, a certain portion (10% to 20%) of patients with high risk for readmission would be falsely stratified as low risk using either the LACE index or HOSPITAL score model.…”

Section: Discussionmentioning

confidence: 73%

Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan

Wang

Chen

et al. 2020

IJERPH

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The LACE index and HOSPITAL score models are the two most commonly used prediction models identifying patients at high risk of readmission with limited information for home care patients. This study compares the effectiveness of these two models in predicting 30-day readmission following acute hospitalization of such patients in Taiwan. A cohort of 57 home care patients were enrolled and followed-up for one year. We compared calibration, discrimination (area under the receiver operating curve, AUC), and net reclassification improvement (NRI) to identify patients at risk of 30-day readmission for both models. Moreover, the cost-effectiveness of the models was evaluated using microsimulation analysis. A total of 22 readmissions occurred after 87 acute hospitalizations during the study period (readmission rate = 25.2%). While the LACE score had poor discrimination (AUC = 0.598, 95% confidence interval (CI) = 0.488-0.702), the HOSPITAL score achieved helpful discrimination (AUC = 0.691, 95% CI = 0.582-0.785). Moreover, the HOSPITAL score had improved the risk prediction in 38.3% of the patients, compared with the LACE index (NRI = 0.383, 95% CI = 0.068-0.697, p = 0.017). Both prediction models effectively reduced readmission rates compared to an attending physician's model (readmission rate reduction: LACE, 39.2%; HOSPITAL, 43.4%; physician, 10.1%; p < 0.001). The HOSPITAL score provides a better prediction of readmission and has potential as a risk management tool for home care patients.

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Section: Discussionmentioning

confidence: 73%

Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan

Wang

Chen

et al. 2020

IJERPH

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“…Over the years this idea has been rejected by many investigators (e.g., Epley & Gilovich, 2005;Kahneman, 2011, ch. 11) but we argue that this rejection is premature (see Lieder, Griffiths, Huys, & Goodman, 2018;Newell & Shanks, 2014, for fuller discussion). Evidence that anchoring is an automatic (or System 1; Kahneman, 2011) process has come, for instance, from demonstrations that it is largely immune to increased motivation to be accurate, induced by financial incentives (Chapman & Johnson, 2002), but more recent work has led to revision of this conclusion (Simmons, LeBoeuf, & Nelson, 2010).…”

Section: Discussionmentioning

confidence: 93%

“…Just as with studies on incentives, several early reports suggested no effect of expertise (e.g., Northcraft & Neale, 1987), but more recent research challenges this conclusion (Smith, Windschitl, & Bruchmann, 2013). Lieder et al (2018) showed that many of the benchmark properties of anchoring can be explained by a deliberative, rational resource model.…”

Section: Discussionmentioning

confidence: 98%

Do Incidental Environmental Anchors Bias Consumers’ Price Estimations?

Shanks

Barbieri-Hermitte

Vadillo

2020

Collabra: Psychology

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It is well-established that decision makers bias their estimates of unknown quantities in the direction of a salient numerical anchor. Some standard anchoring paradigms have been shown to yield pervasive biases, such as Tversky and Kahneman's (1974) classic 2-step task which includes a comparative question followed by an estimation question. In contrast there is much less evidence for the claim that incidental environmental anchors can produce assimilative effects on judgments, such as the amount people are willing to pay for a meal being greater at a restaurant called Studio 97 compared to one called Studio 17. Three studies are reported in which the basic incidental environmental anchoring method of Critcher and Gilovich (2008) is employed to measure consumer price estimations. No statistically significant evidence of incidental anchoring was obtained. In contrast, robust standard anchoring effects were found. The results suggest that anchoring is limited to situations which require explicit thinking about the anchor.

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“…The limited resources lead to the metareasoning problem, which concerns the optimal deployment of the available computational power (7,8,74,75). It is a decision problem about which of the various options to evaluate internally ( Figure 2).…”

Section: Metareasoningmentioning

confidence: 99%

A Formal Valuation Framework for Emotions and Their Control

Huys

Renz

2017

Biological Psychiatry

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Computational psychiatry aims to apply mathematical and computational techniques to help improve psychiatric care. To achieve this, the phenomena under scrutiny should be within the scope of formal methods. As emotions play an important role across many psychiatric disorders, such computational methods must encompass emotions. Here, we consider formal valuation accounts of emotions. We focus on the fact that the flexibility of emotional responses and the nature of appraisals suggest the need for a model-based valuation framework for emotions. However, resource limitations make plain model-based valuation impossible and require metareasoning strategies to apportion cognitive resources adaptively. We argue that emotions may implement such metareasoning approximations by restricting the range of behaviors and states considered. We consider the processes that guide the deployment of the approximations, discerning between innate, model-free, heuristic, and model-based controllers. A formal valuation and metareasoning framework may thus provide a principled approach to examining emotions.

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The anchoring bias reflects rational use of cognitive resources

Cited by 167 publications

References 108 publications

Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan

Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan

Do Incidental Environmental Anchors Bias Consumers’ Price Estimations?

A Formal Valuation Framework for Emotions and Their Control

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