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Martel C, Allen J, Pennycook G, Rand DG. Crowds Can Effectively Identify Misinformation at Scale. Perspect Psychol Sci 2024; 19:477-488. [PMID: 37594056 DOI: 10.1177/17456916231190388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Identifying successful approaches for reducing the belief and spread of online misinformation is of great importance. Social media companies currently rely largely on professional fact-checking as their primary mechanism for identifying falsehoods. However, professional fact-checking has notable limitations regarding coverage and speed. In this article, we summarize research suggesting that the "wisdom of crowds" can be harnessed successfully to help identify misinformation at scale. Despite potential concerns about the abilities of laypeople to assess information quality, recent evidence demonstrates that aggregating judgments of groups of laypeople, or crowds, can effectively identify low-quality news sources and inaccurate news posts: Crowd ratings are strongly correlated with fact-checker ratings across a variety of studies using different designs, stimulus sets, and subject pools. We connect these experimental findings with recent attempts to deploy crowdsourced fact-checking in the field, and we close with recommendations and future directions for translating crowdsourced ratings into effective interventions.
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Affiliation(s)
- Cameron Martel
- Sloan School of Management, Massachusetts Institute of Technology
| | - Jennifer Allen
- Sloan School of Management, Massachusetts Institute of Technology
| | | | - David G Rand
- Sloan School of Management, Massachusetts Institute of Technology
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology
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2
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Galesic M, Barkoczi D, Berdahl AM, Biro D, Carbone G, Giannoccaro I, Goldstone RL, Gonzalez C, Kandler A, Kao AB, Kendal R, Kline M, Lee E, Massari GF, Mesoudi A, Olsson H, Pescetelli N, Sloman SJ, Smaldino PE, Stein DL. Beyond collective intelligence: Collective adaptation. J R Soc Interface 2023; 20:20220736. [PMID: 36946092 PMCID: PMC10031425 DOI: 10.1098/rsif.2022.0736] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 02/27/2023] [Indexed: 03/23/2023] Open
Abstract
We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.
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Affiliation(s)
- Mirta Galesic
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Complexity Science Hub Vienna, 1080 Vienna, Austria
- Vermont Complex Systems Center, University of Vermont, Burlington, VM 05405, USA
| | | | - Andrew M. Berdahl
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, USA
| | - Dora Biro
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | - Giuseppe Carbone
- Department of Mechanics, Mathematics and Management, Politecnico di Bari, Bari 70125, Italy
| | - Ilaria Giannoccaro
- Department of Mechanics, Mathematics and Management, Politecnico di Bari, Bari 70125, Italy
| | - Robert L. Goldstone
- Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Cleotilde Gonzalez
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Anne Kandler
- Department of Mathematics, Max-Planck-Institute for Evolutionary Anthropology, Leipzig 04103, Germany
| | - Albert B. Kao
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Biology Department, University of Massachusetts Boston, Boston, MA 02125, USA
| | - Rachel Kendal
- Centre for Coevolution of Biology and Culture, Durham University, Anthropology Department, Durham, DH1 3LE, UK
| | - Michelle Kline
- Centre for Culture and Evolution, Division of Psychology, Brunel University London, Uxbridge, UB8 3PH, UK
| | - Eun Lee
- Department of Scientific Computing, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan, 48513, Republic of Korea
| | | | - Alex Mesoudi
- Department of Ecology and Conservation, University of Exeter, Penryn TR10 9FE, UK
| | | | | | - Sabina J. Sloman
- Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Computer Science, University of Manchester, Manchester, M13 9PL, UK
| | - Paul E. Smaldino
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Department of Cognitive and Information Sciences, University of California, Merced, CA 95343, USA
| | - Daniel L. Stein
- Santa Fe Institute, Santa Fe, NM 87501, USA
- Department of Physics and Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA
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Yu L, Wang C, Wu S, Wang DH. Communication speeds up but impairs the consensus decision in a dyadic colour estimation task. R Soc Open Sci 2020; 7:191974. [PMID: 32874604 PMCID: PMC7428237 DOI: 10.1098/rsos.191974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 06/30/2020] [Indexed: 06/11/2023]
Abstract
Communication plays an important role in consensus decision-making which pervades our daily life. However, the exact role of communication in consensus formation is not clear. Here, to study the effects of communication on consensus formation, we designed a dyadic colour estimation task, where a pair of isolated participants repeatedly estimated the colours of discs until they reached a consensus or completed eight estimations, either with or without communication. We show that participants' estimates gradually approach each other, reaching towards a consensus, and these are enhanced with communication. We also show that dyadic consensus estimation is on average better than individual estimation. Surprisingly, consensus estimation without communication generally outperforms that with communication, indicating that communication impairs the improvement of consensus estimation. However, without communication, it takes longer to reach a consensus. Moreover, participants who partially cooperate with each other tend to result in better overall consensus. Taken together, we have identified the effect of communication on the dynamics of consensus formation, and the results may have implications on group decision-making in general.
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Affiliation(s)
- Liutao Yu
- School of Systems Science and State Key Laboratory of Cognitive Science and Learning of China, Beijing Normal University, Beijing 100875, People's Republic of China
- School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, People's Republic of China
| | - Chundi Wang
- School of Systems Science and State Key Laboratory of Cognitive Science and Learning of China, Beijing Normal University, Beijing 100875, People's Republic of China
- Department of Psychology and Research Centre of Aeronautic Psychology and Behavior, Beihang University, Beijing 100191, People's Republic of China
| | - Si Wu
- School of Electronics Engineering and Computer Science, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, People's Republic of China
| | - Da-Hui Wang
- School of Systems Science and State Key Laboratory of Cognitive Science and Learning of China, Beijing Normal University, Beijing 100875, People's Republic of China
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Abstract
In peer instruction, instructors pose a challenging question to students, students answer the question individually, students work with a partner in the class to discuss their answers, and finally students answer the question again. A large body of evidence shows that peer instruction benefits student learning. To determine the mechanism for these benefits, we collected semester-long data from six classes, involving a total of 208 undergraduate students being asked a total of 86 different questions related to their course content. For each question, students chose their answer individually, reported their confidence, discussed their answers with their partner, and then indicated their possibly revised answer and confidence again. Overall, students were more accurate and confident after discussion than before. Initially correct students were more likely to keep their answers than initially incorrect students, and this tendency was partially but not completely attributable to differences in confidence. We discuss the benefits of peer instruction in terms of differences in the coherence of explanations, social learning, and the contextual factors that influence confidence and accuracy.
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Affiliation(s)
- Jonathan G Tullis
- Department of Educational Psychology, University of Arizona, 1430 E. Second St., Tucson, AZ, 85721, USA.
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Abstract
After obtaining a sample of published, peer-reviewed articles from journals with high and low impact factors in social, cognitive, neuro-, developmental, and clinical psychology, we used a priori equations recently derived by Trafimow (Educational and Psychological Measurement, 77, 831-854, 2017; Trafimow & MacDonald in Educational and Psychological Measurement, 77, 204-219, 2017) to compute the articles' median levels of precision. Our findings indicate that developmental research performs best with respect to precision, whereas cognitive research performs the worst; however, none of the psychology subfields excelled. In addition, we found important differences in precision between journals in the upper versus lower echelons with respect to impact factors in cognitive, neuro-, and clinical psychology, whereas the difference was dramatically attenuated for social and developmental psychology. Implications are discussed.
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Bazazi S, von Zimmermann J, Bahrami B, Richardson D. Self-serving incentives impair collective decisions by increasing conformity. PLoS One 2019; 14:e0224725. [PMID: 31725758 PMCID: PMC6855459 DOI: 10.1371/journal.pone.0224725] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Accepted: 10/21/2019] [Indexed: 11/19/2022] Open
Abstract
The average judgment of large numbers of people has been found to be consistently better than the best individual response. But what motivates individuals when they make collective decisions? While it is a popular belief that individual incentives promote out-of-the-box thinking and diverse solutions, the exact role of motivation and reward in collective intelligence remains unclear. Here we examined collective intelligence in an interactive group estimation task where participants were rewarded for their individual or group’s performance. In addition to examining individual versus collective incentive structures, we controlled whether participants could see social information about the others’ responses. We found that knowledge about others’ responses reduced the wisdom of the crowd and, crucially, this effect depended on how people were rewarded. When rewarded for the accuracy of their individual responses, participants converged to the group mean, increasing social conformity, reducing diversity and thereby diminishing their group wisdom. When rewarded for their collective performance, diversity of opinions and the group wisdom increased. We conclude that the intuitive association between individual incentives and individualist opinion needs revising.
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Affiliation(s)
| | - Jorina von Zimmermann
- Department of Experimental Psychology, University College London, London, United Kingdom
| | - Bahador Bahrami
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Daniel Richardson
- Department of Experimental Psychology, University College London, London, United Kingdom
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Abstract
Understanding the dynamics of social networks is the objective of interdisciplinary research ranging from animal collective behaviour to epidemiology, political science and marketing. Social influence is key to comprehending emergent group behaviour, but we know little about how inter-individual relationships emerge in the first place. We conducted an experiment where participants repeatedly performed a cognitive test in a small group. In each round, they were allowed to change their answers upon seeing the current answers of other members and their past performance in selecting correct answers. Rather than following a simple majority rule, participants granularly processed the performance of others in deciding how to change their answers. Toward a network model of the experiment, we associated a directed link of a time-varying network with every change in a participant's answer that mirrored the answer of another group member. The rate of growth of the network was not constant in time, whereby links were found to emerge faster as time progressed. Further, repeated interactions reinforced relationships between individuals' performance and their network centrality. Our results provide empirical evidence that inter-individual relationships spontaneously emerge in an adaptive way, where good performers rise as group leaders over time.
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Affiliation(s)
- Shinnosuke Nakayama
- 1 Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering , 6 MetroTech Center, Brooklyn, 11201 New York, NY , USA
| | - Elizabeth Krasner
- 1 Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering , 6 MetroTech Center, Brooklyn, 11201 New York, NY , USA
| | - Lorenzo Zino
- 1 Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering , 6 MetroTech Center, Brooklyn, 11201 New York, NY , USA.,3 Department of Mathematical Sciences, Politecnico di Torino , Corso Duca degli Abruzzi 24, 10129 Turin , Italy
| | - Maurizio Porfiri
- 1 Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering , 6 MetroTech Center, Brooklyn, 11201 New York, NY , USA.,2 Department of Biomedical Engineering, New York University Tandon School of Engineering , 6 MetroTech Center, Brooklyn, 11201 New York, NY , USA
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Nakayama S, Tolbert TJ, Nov O, Porfiri M. Social Information as a Means to Enhance Engagement in Citizen Science‐Based Telerehabilitation. J Assoc Inf Sci Technol 2018. [DOI: 10.1002/asi.24147] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Shinnosuke Nakayama
- Department of Mechanical and Aerospace Engineering New York University Tandon School of Engineering 6 MetroTech Center, Brooklyn NY 11201
| | - Tyrone J. Tolbert
- Department of Mechanical and Aerospace Engineering New York University Tandon School of Engineering 6 MetroTech Center, Brooklyn NY 11201
| | - Oded Nov
- Department of Technology Management and Innovation New York University Tandon School of Engineering 5 MetroTech Center, Brooklyn NY 11201
| | - Maurizio Porfiri
- Department of Mechanical and Aerospace Engineering and Department of Biomedical Engineering, New York University Tandon School of Engineering 6 MetroTech Center, Brooklyn NY 11201
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Foth T, Efstathiou N, Vanderspank-Wright B, Ufholz LA, Dütthorn N, Zimansky M, Humphrey-Murto S. The use of Delphi and Nominal Group Technique in nursing education: A review. Int J Nurs Stud 2016; 60:112-20. [PMID: 27297373 DOI: 10.1016/j.ijnurstu.2016.04.015] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2015] [Revised: 02/29/2016] [Accepted: 04/25/2016] [Indexed: 11/18/2022]
Abstract
OBJECTIVES Consensus methods are used by healthcare professionals and educators within nursing education because of their presumed capacity to extract the profession's' "collective knowledge" which is often considered tacit knowledge that is difficult to verbalize and to formalize. Since their emergence, consensus methods have been criticized and their rigour has been questioned. Our study focuses on the use of consensus methods in nursing education and seeks to explore how extensively consensus methods are used, the types of consensus methods employed, the purpose of the research and how standardized the application of the methods is. DESIGN AND DATA SOURCES A systematic approach was employed to identify articles reporting the use of consensus methods in nursing education. The search strategy included keyword search in five electronic databases [Medline (Ovid), Embase (Ovid), AMED (Ovid), ERIC (Ovid) and CINAHL (EBSCO)] for the period 2004-2014. We included articles published in English, French, German and Greek discussing the use of consensus methods in nursing education or in the context of identifying competencies. REVIEW METHOD A standardized extraction form was developed using an iterative process with results from the search. General descriptors such as type of journal, nursing speciality, type of educational issue addressed, method used, geographic scope were recorded. Features reflecting methodology such as number, selection and composition of panel participants, number of rounds, response rates, definition of consensus, and feedback were recorded. RESULTS 1230 articles were screened resulting in 101 included studies. The Delphi was used in 88.2% of studies. Most were reported in nursing journals (63.4%). The most common purpose to use these methods was defining competencies, curriculum development and renewal, and assessment. Remarkably, both standardization and reporting of consensus methods was noted to be generally poor. Areas where the methodology appeared weak included: preparation of the initial questionnaire; the selection and description of participants; number of rounds and number of participants remaining after each round; formal feedback of group ratings; definitions of consensus and a priori definition of numbers of rounds; and modifications to the methodology. CONCLUSIONS The findings of this study are concerning if interpreted within the context of the structural critiques because our findings lend support to these critiques. If consensus methods should continue being used to inform best practices in nursing education, they must be rigorous in design.
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Affiliation(s)
- Thomas Foth
- School of Nursing, Faculty of Health Sciences, University of Ottawa, 451 Smyth Road, Ottawa, Ontario, Canada K1H 8M5.
| | - Nikolaos Efstathiou
- School of Nursing, Institute of Clinical Science, College of Medical and Dental Sciences, University of Birmingham, United Kingdom
| | | | | | - Nadin Dütthorn
- Münster School of Health, Department of Health Care Education, University of Applied Science in Münster, Niedersachsen, Germany
| | - Manuel Zimansky
- Department of Nursing Sciences, Faculty of Health Sciences, Osnabrück University, Niedersachsen, Germany
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