Information Avoidance

Golman, Russell; Hagmann, David; Loewenstein, George

doi:10.1257/jel.20151245

Cited by 565 publications

(258 citation statements)

References 173 publications

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“…Research on epistemic beliefs and epistemic monitoring is consistent with a growing body of literature in both psychology (e.g., Golman et al, 2017) and economics (e.g., Babcock, Loewenstein, Issacharoff, & Camerer, 1995) on the vulnerability of humans to various forms of cognitive bias (i.e., an umbrella term covering a diverse typology of systematic errors in judgment and decision-making that are prevalent in all human beings; Kahneman, Slovic, & Tversky, 1992). Table 1 describes several forms of cognitive bias that researchers have identified and investigated.…”

Section: Information Overloadsupporting

confidence: 79%

“…In general, readers evaluate information in texts that is consistent with their beliefs as more plausible than information that is inconsistent with their beliefs (Richter, 2011). They also tend to value evidence that supports what they are motivated to believe and discredit evidence that contradicts what they believe (Golman, Hagmann, & Loewenstein, 2017).…”

Section: Information Overloadmentioning

confidence: 99%

“…People use diverse tactics to avoid obtaining information, either intentionally or unintentionally (Golman et al, 2017).…”

Section: Active and Passive Information Avoidancementioning

confidence: 99%

“…Group think Individuals adopt the prevailing opinion of a group as their own, at the expense of creativity and individual responsibility (Golman et al, 2017).…”

Section: Confirmation Biasmentioning

confidence: 99%

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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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Section: Information Overloadsupporting

confidence: 79%

Section: Information Overloadmentioning

confidence: 99%

“…People use diverse tactics to avoid obtaining information, either intentionally or unintentionally (Golman et al, 2017).…”

Section: Active and Passive Information Avoidancementioning

confidence: 99%

“…Group think Individuals adopt the prevailing opinion of a group as their own, at the expense of creativity and individual responsibility (Golman et al, 2017).…”

Section: Confirmation Biasmentioning

confidence: 99%

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A Social Machine for Transdisciplinary Research

G Lebow

2018

InformingSciJ

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“…Incentives to acquire or avoid information may include 1) whether the revealed information increases utility; 2) whether the information is instrumental to make consequent decisions; and 3) whether it simply reduces uncertainty (Golman et al 2017;Golman & Loewenstein 2015;Elias & Schotter, 2010;Burger 1992& Skinner 1995, in Prinder 2014. Curiosity is a constituent of our cognition-a desire to learn what is unknown, which may arise from a gap between the information we know and what we want to know.…”

Section: Social Curiositymentioning

confidence: 99%

Social Preferences and Social Curiosity

Tasch

Houser²

2018

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Social Preferences and Social Curiosity AbstractOver the last two decades social preferences have been implicated in a wide variety of key economic behaviors. Here we investigate connections between social preferences and the demand for information about others' economic decisions and outcomes, which we denote "social curiosity." Our analysis is within the context of the inequality aversion model of Fehr and Schmidt (1999). Using data from laboratory experiments with sequential public goods games, we estimate social preferences at the individual level, and then correlate social preferences with one's willingness to pay to make visible others' contribution decisions. Our investigation enables us to shed light on how costs to knowing others' economic decisions and outcomes impact decisions among people with different social preferences, and in particular the extent to which such costs impact the willingness for groups to cooperate. JEL-Codes: C910, H410.

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Curiosity in news consumption

Qiu,

Golman

2024

Applied Cognitive Psychology

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We analyze how curiosity drives news consumption. We test predictions of the information‐gap theory of curiosity using over 100,000 WeChat news articles, applying NLP methods to construct measures of salience, importance, and surprisingness associated with news headlines, experimentally validating these measures, and using them to predict clicks. Our findings confirm that people tend to consume news when: the headline sparks a salient question; the content appears more important (e.g., emphasized by the headline's position on the webpage or an exclamation mark); the headline refers to more surprising topics (measured as the KL‐divergence from a baseline topic distribution); and the headline has lower valence. Information‐gap theory helps predict aggregate news consumption. Yet our data also reveal a small negative correlation between the number of clicks and the ratio of likes to clicks, suggesting that while inducing curiosity can drive short‐term news consumption, it doesn't necessarily enhance long‐term reader engagement.

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Information Avoidance

Cited by 565 publications

References 173 publications

A Social Machine for Transdisciplinary Research

A Social Machine for Transdisciplinary Research

Social Preferences and Social Curiosity

Curiosity in news consumption

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