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Identifying the Effects of Unjustified Confidence versus Overconfidence: Lessons Learned from Two Analytic Methods.

ABSTRACT: One of the most common findings in behavioral decision research is that people have unrealistic beliefs about how much they know. However, demonstrating that misplaced confidence exists does not necessarily mean that there are costs to it. This paper contrasts two approaches toward answering whether misplaced confidence is good or bad, which we have labeled the overconfidence and unjustified confidence approach. We first consider conceptual and analytic issues distinguishing these approaches. Then, we provide findings from a set of simulations designed to determine when the approaches produce different conclusions across a range of possible confidence-knowledge-outcome relationships. Finally, we illustrate the main findings from the simulations with three empirical examples drawn from our own data. We conclude that the unjustified confidence approach is typically the preferred approach, both because it is appropriate for testing a larger set of psychological mechanisms as well as for methodological reasons.

SUBMITTER: Parker AM

PROVIDER: S-EPMC4191861 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Identifying the Effects of Unjustified Confidence versus Overconfidence: Lessons Learned from Two Analytic Methods.

Parker Andrew M AM Stone Eric R ER

Journal of behavioral decision making 20140401 2

One of the most common findings in behavioral decision research is that people have unrealistic beliefs about how much they know. However, demonstrating that misplaced confidence exists does not necessarily mean that there are costs to it. This paper contrasts two approaches toward answering whether misplaced confidence is good or bad, which we have labeled the overconfidence and unjustified confidence approach. We first consider conceptual and analytic issues distinguishing these approaches. Th ...[more]

PMID: 25309037

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Project description:BackgroundWorkflow automation, which involves identifying sequences of tasks that can be streamlined by using technology and modern computing, offers opportunities to address the United States health care system's challenges with quality, safety, and efficiency. Other industries have successfully implemented workflow automation to address these concerns, and lessons learned from those experiences may inform its application in health care.ObjectiveOur aim was to identify and synthesize (1) current approaches in workflow automation across industries, (2) opportunities for applying workflow automation in health care, and (3) considerations for designing and implementing workflow automation that may be relevant to health care.MethodsWe conducted a targeted review of peer-reviewed and gray literature on automation approaches. We identified relevant databases and terms to conduct the searches across sources and reviewed abstracts to identify 123 relevant articles across 11 disciplines.ResultsWorkflow automation is used across industries such as finance, manufacturing, and travel to increase efficiency, productivity, and quality. We found automation ranged from low to full automation, and this variation was associated with task and technology characteristics. The level of automation is linked to how well a task is defined, whether a task is repetitive, the degree of human intervention and decision-making required, and the sophistication of available technology. We found that identifying automation goals and assessing whether those goals were reached was critical, and ongoing monitoring and improvement would help to ensure successful automation.ConclusionUse of workflow automation in other industries can inform automating health care workflows by considering the critical role of people, process, and technology in design, testing, implementation, use, and ongoing monitoring of automated workflows. Insights gained from other industries will inform an interdisciplinary effort by the Office of the National Coordinator for Health Information Technology to outline priorities for advancing health care workflow automation.

Dataset Information

Identifying the Effects of Unjustified Confidence versus Overconfidence: Lessons Learned from Two Analytic Methods.

Publications

Identifying the Effects of Unjustified Confidence versus Overconfidence: Lessons Learned from Two Analytic Methods.

Similar Datasets

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