Overconfidence over the lifespan

Prims, Julia P.

doi:10.1017/s1930297500005222

Cited by 33 publications

(15 citation statements)

References 63 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although we suspect that the basic principles (e.g., the inverse U-shaped relationship between mean judgments and variance) will generalize across different populations, more research should be performed to confirm this. Also, because the calibration and bias (e.g., overconfidence) of probability judgments have been shown to vary depending on both culture (Yates, 2010) and age (Prims & Moore, 2017), we can expect these differences to be expressed as differences in sample size and prior distribution (for the Bayesian sampler) or noise probability (for the PTN model). We have no reason to believe that the results depend on other characteristics of the participants, materials, or context.…”

Section: Constraints On Generalitymentioning

confidence: 99%

A unified explanation of variability and bias in human probability judgments: How computational noise explains the mean–variance signature.

Sundh¹,

Zhu²,

Chater³

et al. 2023

Journal of Experimental Psychology: General

View full text Add to dashboard Cite

Human probability judgments are both variable and subject to systematic biases. Most probability judgment models treat variability and bias separately: a deterministic model explains the origin of bias, to which a noise process is added to generate variability. But these accounts do not explain the characteristic inverse U-shaped signature linking mean and variance in probability judgments. By contrast, models based on sampling generate the mean and variance of judgments in a unified way: the variability in the response is an inevitable consequence of basing probability judgments on a small sample of remembered or simulated instances of events. We consider two recent sampling models, in which biases are explained either by the sample accumulation being further corrupted by retrieval noise (the Probability Theory + Noise account) or as a Bayesian adjustment to the uncertainty implicit in small samples (the Bayesian sampler). While the mean predictions of these accounts closely mimic one another, they differ regarding the predicted relationship between mean and variance. We show that these models can be distinguished by a novel linear regression method that analyses this crucial mean-variance signature. First, the efficacy of the method is established using model recovery, demonstrating that it more accurately recovers parameters than complex approaches. Second, the method is applied to the mean and variance of both existing and new probability judgment data, confirming that judgments are based on a small number of samples that are adjusted by a prior, as predicted by the Bayesian sampler. Public Significance StatementHuman probability judgments play a crucial role in everyday reasoning and decision-making. But it can be difficult to distinguish between different theoretical models of the mental processes determining such judgments. This study introduces a new method which uses the relationships between the mean and the variance of probability judgments to discriminate between theoretical models. Applying the method provides new evidence for a theory based on mental sampling coupled with a Bayesian adjustment of the sampled proportions, as well as a simple and accurate way to estimate model parameters for individuals. This sheds new light on many of the reoccurring biases in human probability judgment.

show abstract

Section: Constraints On Generalitymentioning

confidence: 99%

A unified explanation of variability and bias in human probability judgments: How computational noise explains the mean–variance signature.

Sundh¹,

Zhu²,

Chater³

et al. 2023

Journal of Experimental Psychology: General

View full text Add to dashboard Cite

show abstract

“…Possibly, even if individuals solved one or a few training question(s) with high confidence, this success might merely be a coincidence and they do not have enough knowledge to solve further questions. Furthermore, many studies have also demonstrated that people sometimes overestimate their chance of success or actual abilities regarding the domain of the current tasks (i.e., overconfidence [23][24][25][26][27][28][29][30][31]). Based on these considerations, even if the accuracy of group judgments made by individuals with high confidence in one or a few training question(s) is very high, this may decrease for the test questions (see also [32]).…”

Section: Subjective Confidence In "Training" Questions As a Cue For P...mentioning

confidence: 99%

“…Possibly, individuals with extremely high confidence in some question(s) could merely "by chance" solve these question(s) and are in fact, unfamiliar with further questions. In other words, the regression to the mean may occur from training performance to test performance, or individuals with high confidence may make false judgments or overestimate their own abilities, which is known as overconfidence [23][24][25][26][27][28][29][30][31]. Therefore, when a group comprises only individuals with high confidence (called the "highconfidence group"), its training accuracy is unlikely to predict its test accuracy.…”

Section: Mixing Various Confidence Levels In One Group: a Possible St...mentioning

confidence: 99%

Can individual subjective confidence in training questions predict group performance in test questions?

Shirasuna

Honda

2023

PLoS ONE

View full text Add to dashboard Cite

When people have to solve many tasks, they can aggregate diverse individuals’ judgments using the majority rule, which often improves the accuracy of judgments (wisdom of crowds). When aggregating judgments, individuals’ subjective confidence is a useful cue for deciding which judgments to accept. However, can confidence in one task set predict performance not only in the same task set, but also in another? We examined this issue through computer simulations using behavioral data obtained from binary-choice experimental tasks. In our simulations, we developed a “training-test” approach: We split the questions used in the behavioral experiments into “training questions” (as questions to identify individuals’ confidence levels) and “test questions” (as questions to be solved), similar to the cross-validation method in machine learning. We found that (i) through analyses of behavioral data, confidence in a certain question could predict accuracy in the same question, but not always well in another question. (ii) Through a computer simulation for the accordance of two individuals’ judgments, individuals with high confidence in one training question tended to make less diverse judgments in other test questions. (iii) Through a computer simulation of group judgments, the groups constructed from individuals with high confidence in the training question(s) generally performed well; however, their performance sometimes largely decreased in the test questions especially when only one training question was available. These results suggest that when situations are highly uncertain, an effective strategy is to aggregate various individuals regardless of confidence levels in the training questions to avoid decreasing the group accuracy in test questions. We believe that our simulations, which follow a “training-test” approach, provide practical implications in terms of retaining groups’ ability to solve many tasks.

show abstract

“…In other words, having exaggerated or inflated self-efficacy beliefs, i.e. operating under the influence of the Dunning–Kruger effect, may result in overconfidence regarding a specific skill or ability (Canady and Larzo, 2022; Prims and Moore, 2017). People who have a high level of self-efficacy beliefs are less likely to allocate a significant amount of effort to a given task.…”

Section: Literature Reviewmentioning

confidence: 99%

Problem-solving abilities of managers: inflated self-efficacy beliefs

Koç

Yürür

Özşahin

2022

JHTI

View full text Add to dashboard Cite

PurposeThis study compared the results of self-report and ability-based tests of problem-solving abilities of 144 hospitality managers working at hotels and restaurants through an online survey. In the first stage of the study, the managers were asked to fill in the self-report problem-solving ability scale by Tesone et al. (2010). In the second stage of the study, the managers were asked to respond to questions in a case-study-based problem-solving test.Design/methodology/approachProblem-solving is a key aspect of business process management. This study aims to investigate and compare hospitality managers' actual and claimed (self-report) problem-solving abilities. A lack of unawareness of the actual level of skills may be an important problem as managers who tend to have inflated self-efficacy beliefs are less likely to allocate resources, e.g. time, money and effort, to develop a particular skill or ability they lack. They are also more likely to take risks regarding that skill or ability.FindingsThe results of the study showed that there was a major difference between the results of the self-report test and the actual test. This meant that the managers who participated in the study had inflated self-efficacy beliefs regarding their problem-solving abilities, i.e. they operated under the influence of the Dunning–Kruger effect. The study showed that self-report tests that are commonly used in businesses in recruitment and promotion may not provide a correct level of people's abilities. In general, managers who have inflated self-efficacy beliefs are less likely to be interested in developing a particular skill due to the overconfidence arising from their inflated self-efficacy beliefs. The study showed that managers were less likely to allocate resources, e.g. time, money and effort, to develop a particular skill they lack and are more likely to take risks regarding that particular skill.Practical implicationsManagers in the hospitality industry appear to lack problem solving-abilities. While the hospitality managers assigned high marks for their problem-solving abilities in a self-report problem-solving scale and appeared to be performing significantly good overall in problem-solving, they performed poorly in an actual problem solving exercise. It is recommended that businesses rather than depending on self-report problem-solving scales, they should resort to ability-based scales or exercises that actually measure managers' problem-solving abilities. Also, as managers who had formal tourism and hospitality education performed poorly, tourism and hospitality programme managers at universities are recommend to review their syllabi and curriculum so as to help support their graduates' problem-solving abilities.Originality/valueThe study is original as no previous study compared managers' problem-solving abilities by using self-report and ability-based tests. The study has implications for researchers in terms of developing knowledge, ability and skill-based scales in the future. The study has also significant practical implications for the practitioners.

show abstract

Overconfidence over the lifespan

Cited by 33 publications

References 63 publications

A unified explanation of variability and bias in human probability judgments: How computational noise explains the mean–variance signature.

A unified explanation of variability and bias in human probability judgments: How computational noise explains the mean–variance signature.

Can individual subjective confidence in training questions predict group performance in test questions?

Problem-solving abilities of managers: inflated self-efficacy beliefs

Contact Info

Product

Resources

About