Jennifer Marie Logg
Georgetown University
TraitAlgorithmAssociation (psychology)PsychologyInterpersonal communicationCognitionWork (electrical)Cognitive psychologySocial groupSkepticismSocial dilemmaCognitive biasData sciencePerceptionEmbarrassmentLeverage (negotiation)Face (sociological concept)Advice (complexity)ConstrualsPopularityCompliance (psychology)Human judgmentGroup identificationAffect (psychology)Public relationsComputer scienceOverconfidence effectBig dataIllusory superioritySocial psychologyCollectivism
24Publications
8H-index
351Citations
Publications 21
Newest
#1Jennifer Marie Logg (Georgetown University)H-Index: 8
#2Charles Dorison (NU: Northwestern University)H-Index: 2
Abstract null null In the past decade, the social and behavioral sciences underwent a methodological revolution, offering practical prescriptions for improving the replicability and reproducibility of research results. One key to reforming science is a simple and scalable practice: pre-registration. Pre-registration constitutes pre-specifying an analysis plan prior to data collection. A growing chorus of articles discusses the prescriptive, field-wide benefits of pre-registration. To increase ad...
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Table of Contents 1. Introduction: Data Analytics Needs Psychology a. Current work 2. Data Analytics’ Last Mile Problem 3. The Power of Algorithms a. Algorithmic accuracy b. How people respond to algorithmic advice is an open question. c. Presenting algorithms as a threat rather than a tool. d. Boundaries to algorithmic capabilities. 4. The Psychology of Big Data 5. Theory of Machine a. Input b. Process c. Output 6. Theory of Machine and Decision Context (Prediction, Assessment, Feedback) a. Con...
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#1Joey T. Cheng (York University)H-Index: 16
#2Cameron Anderson (University of California, Berkeley)H-Index: 35
Last. Jennifer Marie Logg (Georgetown University)
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We propose and test the overconfidence transmission hypothesis, which predicts that individuals calibrate their self-assessments in response to the confidence others display in their social group. Six studies that deploy a mix of correlational and experimental methods support this hypothesis. Evidence indicates that individuals randomly assigned to collaborate in laboratory dyads converged on levels of overconfidence about their own performance rankings. In a controlled experimental context, obs...
2 CitationsSource
#1Heather H. J. Yang (MIT: Massachusetts Institute of Technology)H-Index: 2
#2Nathanael J. Fast (SC: University of Southern California)H-Index: 13
Last. Michael Yeomans (Harvard University)H-Index: 7
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When do people heed the advice from an algorithm as opposed to a human? This session delves into novel research in which people opt for the recommendations from algorithmically-derived sources and ...
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#1Hayley Blunden (Harvard University)H-Index: 2
#2Leigh Plunkett Tost (SC: University of Southern California)H-Index: 16
Last. Ting Zhang (Harvard University)H-Index: 53
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#1Poruz Khambatta (Stanford University)H-Index: 3
#1Hengchen DaiH-Index: 1
Last. David T. Newman (SC: University of Southern California)H-Index: 2
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Recent advances in technology are dramatically changing the face of business. New digital platforms are enabling individuals and organizations to connect and interact virtually rather than in-perso...
1 CitationsSource
#1Hayley BlundenH-Index: 2
Last. Francesca GinoH-Index: 78
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#1Jennifer Marie Logg (Harvard University)H-Index: 8
#2Julia A. Minson (Harvard University)H-Index: 11
Last. Don A. Moore (University of California, Berkeley)H-Index: 53
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Abstract Even though computational algorithms often outperform human judgment, received wisdom suggests that people may be skeptical of relying on them (Dawes, 1979). Counter to this notion, results from six experiments show that lay people adhere more to advice when they think it comes from an algorithm than from a person. People showed this effect, what we call algorithm appreciation, when making numeric estimates about a visual stimulus (Experiment 1A) and forecasts about the popularity of so...
118 CitationsSource
#1Hayley Blunden (Harvard University)H-Index: 2
#2Jennifer Marie Logg (Harvard University)H-Index: 8
Last. Francesca Gino (Harvard University)H-Index: 78
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Abstract Prior advice research has focused on why people rely on (or ignore) advice and its impact on judgment accuracy. We expand the consideration of advice-seeking outcomes by investigating the interpersonal consequences of advice seekers’ decisions. Across nine studies, we show that advisors interpersonally penalize seekers who disregard their advice, and that these reactions are especially strong among expert advisors. This penalty also drives advisor reactions to a widely-recommended advic...
11 CitationsSource
#1Jennifer Marie Logg (Harvard University)H-Index: 8
#2Uriel Haran (BGU: Ben-Gurion University of the Negev)H-Index: 7
Last. Don A. Moore (University of California, Berkeley)H-Index: 53
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: Are overconfident beliefs driven by the motivation to view oneself positively? We test the relationship between motivation and overconfidence using two distinct, but often conflated measures: better-than-average (BTA) beliefs and overplacement. Our results suggest that motivation can indeed affect these faces of overconfidence, but only under limited conditions. Whereas BTA beliefs are inflated by motivation, introducing some specificity and clarity to the standards of assessment (Experiment 1...
14 CitationsSource