First Impressions Matter: A Model of Confirmatory Bias

Rabin, Matthew; Schrag, Joel L.

doi:10.1162/003355399555945

Cited by 793 publications

(505 citation statements)

References 52 publications

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“…This would suggest that if individuals were to observe the outlier behaving normally the next day, they should give even less weight to the outlier's previous behavior now that there is confirmation of its unreliability. However, previous research suggests that first impressions are remarkably strong (Kelley, 1950;Nickerson, 1998;Rabin & Schrag, 1999). This suggests that individuals may not dismiss the outlier as quickly as would be rational, because the outlying first impression continues to hold disproportionate weight in the general perception of the group.…”

Section: Studymentioning

confidence: 93%

Social norm perception in groups with outliers.

Dannals

Miller

2017

Journal of Experimental Psychology: General

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Social outliers draw a lot of attention from those inside and outside their group and yet little is known about their impact on perceptions of their group as a whole. The present studies examine how outliers influence observers' summary perceptions of a group's behavior and inferences about the group's descriptive and prescriptive norms. Across four studies (N = 1739) we examine how observers perceive descriptive and prescriptive social norms in groups containing outliers of varying degrees. We find consistent evidence that observers overweight outlying behavior when judging the descriptive and prescriptive norms, but overweight outliers less as they become more extreme, especially in perceptions of the prescriptive norm. We find this pattern across norms pertaining to punctuality (Studies 1-2, 4) and clothing formality (Study 3) and for outliers who are both prescriptively and descriptively deviant (e.g. late arrivers), as well as for outliers who are only descriptive deviants (e.g. early arrivers). We further demonstrate that observers' perceptions of the group shift in the direction of moderate outliers.

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Section: Studymentioning

confidence: 93%

Social norm perception in groups with outliers.

Dannals

Miller

2017

Journal of Experimental Psychology: General

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“…The paper proposes an investor's behaviour model that is built upon the psychological evidence on confirmation bias. This behavioural bias suggests that people tend to misinterpret information in a way that supports their current beliefs (Rabin and Schrag, 1999). Because value stocks are generally more financially distressed than glamour stocks (Fama and French, 1995) This makes the distribution of observations across F Score heavily tilted toward larger values 4 , which is very similar to the US evidence presented in Piotroski (2000).…”

Section: Hypothesis Developmentmentioning

confidence: 72%

“…The literature on psychological biases in economic behaviour presents strong evidence of confirmation bias (e.g. Rabin and Schrag, 1999) and argues that people tend to misinterpret new information to support their prior beliefs. In this context thus, pessimistic value investors can be expected to be more biased when processing good information, and optimistic glamour investors to be more biased when processing bad information.…”

Section: Introductionmentioning

confidence: 99%

How Value–Glamour Investors Use Financial Information: UK Evidence of Investor’s Confirmation Bias

2010

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Abstract:The paper investigates investor's behaviour in the context of value-glamour investing and fundamental analysis, and provides a direct test of the confirmation bias by bringing together the evidence from several strands of literature into a well-defined framework of investor behaviour. The empirical evidence presented is in line with a model of investor's asymmetric reaction to good and bad news due to confirmation bias. Pessimistic value investors typically under-react to good financial information, but they process bad information rationally or over-confidently. On the contrary, glamour investors are often too optimistic to timely update prices following bad financial information, but they are likely to fairly price or even over-react when receiving good information.

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“…People have a tendency to interpret new information in a way that confirms their preexisting beliefs or theories (Rabin & Schrag, 1999).…”

Section: Confirmation Biasmentioning

confidence: 99%

A Social Machine for Transdisciplinary Research

G Lebow

2018

InformingSciJ

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Aim/Purpose: This paper introduces a Social Machine for collaborative sensemaking that the developers have configured to the requirements and challenges of transdisciplinary literature reviews. Background: Social Machines represent a promising model for unifying machines and social processes for a wide range of purposes. A development team led by the author is creating a Social Machine for activities that require users to combine pieces of information from multiple online sources and file types for various purposes. Methodology: The development team has applied emergent design processes, usability testing, and formative evaluation in the execution of the product road map. Contribution: A major challenge of the digital information age is how to tap into large volumes of online information and the collective intelligence of diverse groups to generate new knowledge, solve difficult problems, and drive innovation. A Transdisciplinary Social Machine (TDSM) enables new forms of interactions between humans, machines, and online content that have the potential to (a) improve outcomes of sensemaking activities that involve large collections of online documents and diverse groups and (b) make machines more capable of assisting humans in their sensemaking efforts. Findings: Preliminary findings suggest that TDSM promotes learning and the generation of new knowledge. Recommendations for Practitioners: TDSM has the potential to improve outcomes of literature reviews and similar activities that require distilling information from diverse online sources. Recommendation for Researchers: TDSM is an instrument for investigating sensemaking, an environment for studying various forms of human and machine interactions, and a subject for further evaluation. Impact on Society: In complex areas such as sustainability and healthcare research, TDSM has the potential to make decision-making more transparent and evidence-based, facilitate the production of new knowledge, and promote innovation. In education, TDSM has the potential to prepare students for the 21st century information economy. Future Research: Research is required to measure the effects of TDSM on cross-disciplinary communication, human and machine learning, and the outcomes of transdisciplinary research projects. The developers are planning a multiple case study using design-based research methodology to investigate these topics.

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First Impressions Matter: A Model of Confirmatory Bias

Cited by 793 publications

References 52 publications

Social norm perception in groups with outliers.

Social norm perception in groups with outliers.

How Value–Glamour Investors Use Financial Information: UK Evidence of Investor’s Confirmation Bias

A Social Machine for Transdisciplinary Research

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