Search

Your search keyword '"Argyle, Lisa"' showing total 4 results
Start Over Author "Argyle, Lisa" Topic social sciences
4 results on '"Argyle, Lisa"'

1. Measuring the predictability of life outcomes with a scientific mass collaboration.

Author

Salganik MJ, Lundberg I, Kindel AT, Ahearn CE, Al-Ghoneim K, Almaatouq A, Altschul DM, Brand JE, Carnegie NB, Compton RJ, Datta D, Davidson T, Filippova A, Gilroy C, Goode BJ, Jahani E, Kashyap R, Kirchner A, McKay S, Morgan AC, Pentland A, Polimis K, Raes L, Rigobon DE, Roberts CV, Stanescu DM, Suhara Y, Usmani A, Wang EH, Adem M, Alhajri A, AlShebli B, Amin R, Amos RB, Argyle LP, Baer-Bositis L, Büchi M, Chung BR, Eggert W, Faletto G, Fan Z, Freese J, Gadgil T, Gagné J, Gao Y, Halpern-Manners A, Hashim SP, Hausen S, He G, Higuera K, Hogan B, Horwitz IM, Hummel LM, Jain N, Jin K, Jurgens D, Kaminski P, Karapetyan A, Kim EH, Leizman B, Liu N, Möser M, Mack AE, Mahajan M, Mandell N, Marahrens H, Mercado-Garcia D, Mocz V, Mueller-Gastell K, Musse A, Niu Q, Nowak W, Omidvar H, Or A, Ouyang K, Pinto KM, Porter E, Porter KE, Qian C, Rauf T, Sargsyan A, Schaffner T, Schnabel L, Schonfeld B, Sender B, Tang JD, Tsurkov E, van Loon A, Varol O, Wang X, Wang Z, Wang J, Wang F, Weissman S, Whitaker K, Wolters MK, Woon WL, Wu J, Wu C, Yang K, Yin J, Zhao B, Zhu C, Brooks-Gunn J, Engelhardt BE, Hardt M, Knox D, Levy K, Narayanan A, Stewart BM, Watts DJ, and McLanahan S

Subjects
Adolescent, Child, Child, Preschool, Cohort Studies, Family, Female, Humans, Infant, Life, Machine Learning, Male, Predictive Value of Tests, Social Sciences methods, Social Sciences statistics & numerical data, Social Sciences standards
Abstract

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences., Competing Interests: Competing interest statement: B.E.E. is on the scientific advisory boards of Celsius Therapeutics and Freenome, is currently employed by Genomics plc and Freenome, and is on a year leave-of-absence from Princeton University., (Copyright © 2020 the Author(s). Published by PNAS.)

Published
2020
Full Text
View/download PDF

2. Measuring the predictability of life outcomes with a scientific mass collaboration

Author

Salganik, Matthew J, Lundberg, Ian, Kindel, Alexander T, Ahearn, Caitlin E, Al-Ghoneim, Khaled, Almaatouq, Abdullah, Altschul, Drew M, Brand, Jennie E, Carnegie, Nicole Bohme, Compton, Ryan James, Datta, Debanjan, Davidson, Thomas, Filippova, Anna, Gilroy, Connor, Goode, Brian J, Jahani, Eaman, Kashyap, Ridhi, Kirchner, Antje, McKay, Stephen, Morgan, Allison C, Pentland, Alex, Polimis, Kivan, Raes, Louis, Rigobon, Daniel E, Roberts, Claudia V, Stanescu, Diana M, Suhara, Yoshihiko, Usmani, Adaner, Wang, Erik H, Adem, Muna, Alhajri, Abdulla, AlShebli, Bedoor, Amin, Redwane, Amos, Ryan B, Argyle, Lisa P, Baer-Bositis, Livia, Büchi, Moritz, Chung, Bo-Ryehn, Eggert, William, Faletto, Gregory, Fan, Zhilin, Freese, Jeremy, Gadgil, Tejomay, Gagné, Josh, Gao, Yue, Halpern-Manners, Andrew, Hashim, Sonia P, Hausen, Sonia, He, Guanhua, Higuera, Kimberly, Hogan, Bernie, Horwitz, Ilana M, Hummel, Lisa M, Jain, Naman, Jin, Kun, Jurgens, David, Kaminski, Patrick, Karapetyan, Areg, Kim, EH, Leizman, Ben, Liu, Naijia, Möser, Malte, Mack, Andrew E, Mahajan, Mayank, Mandell, Noah, Marahrens, Helge, Mercado-Garcia, Diana, Mocz, Viola, Mueller-Gastell, Katariina, Musse, Ahmed, Niu, Qiankun, Nowak, William, Omidvar, Hamidreza, Or, Andrew, Ouyang, Karen, Pinto, Katy M, Porter, Ethan, Porter, Kristin E, Qian, Crystal, Rauf, Tamkinat, Sargsyan, Anahit, Schaffner, Thomas, Schnabel, Landon, Schonfeld, Bryan, Sender, Ben, Tang, Jonathan D, Tsurkov, Emma, van Loon, Austin, Varol, Onur, Wang, Xiafei, Wang, Zhi, Wang, Julia, Wang, Flora, Weissman, Samantha, Whitaker, Kirstie, Wolters, Maria K, Woon, Wei Lee, Wu, James, Wu, Catherine, and Yang, Kengran

Subjects
Male, life course, Adolescent, Social Sciences, Infant, prediction, Cohort Studies, Machine Learning, Life, Predictive Value of Tests, Humans, Family, Female, mass collaboration, Child, Preschool
Abstract

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

Published
2020

3. Exposure to opposing views on social media can increase political polarization.

Author

Bail, Christopher A., Brown, Taylor W., Bumpus, John P., Jaemin Lee, Mann, Marcus, Merhout, Friedolin, Argyle, Lisa P., Haohan Chen, Hunzaker, M. B. Fallin, and Volfovsky, Alexander

Subjects
POLARIZATION (Social sciences), SOCIAL media, SOCIAL network analysis, SOCIOLOGY, SOCIAL sciences
Abstract

There is mounting concern that social media sites contribute to political polarization by creating "echo chambers" that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for 1 month that exposed them to messages from those with opposing political ideologies (e.g., elected officials, opinion leaders, media organizations, and nonprofit groups). Respondents were resurveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative posttreatment. Democrats exhibited slight increases in liberal attitudes after following a conservative Twitter bot, although these effects are not statistically significant. Notwithstanding important limitations of our study, these findings have significant implications for the interdisciplinary literature on political polarization and the emerging field of computational social science. [ABSTRACT FROM AUTHOR]

Published
2018
Full Text
View/download PDF

4. Measuring the predictability of life outcomes with a scientific mass collaboration

Author

Kristin E. Porter, Malte Möser, Flora Wang, Bingyu Zhao, Wei Lee Woon, Yoshihiko Suhara, Adaner Usmani, Erik H. Wang, Kun Jin, Samantha Weissman, William Eggert, Hamidreza Omidvar, Andrew Or, Lisa M Hummel, Gregory Faletto, Ben Sender, Qiankun Niu, Viola Mocz, Antje Kirchner, Catherine Wu, Karen Ouyang, Ian Lundberg, Allison C. Morgan, Abdulla Alhajri, Arvind Narayanan, Khaled AlGhoneim, Louis Raes, Ilana M. Horwitz, Barbara E. Engelhardt, Ben Leizman, Crystal Qian, Drew Altschul, Guanhua He, Jeanne Brooks-Gunn, Ridhi Kashyap, Eaman Jahani, Ryan James Compton, Anna Filippova, Sara McLanahan, Tejomay Gadgil, Claudia V. Roberts, Muna Adem, Julia Wang, Jeremy Freese, Alexander T. Kindel, Daniel E Rigobon, Naijia Liu, Lisa P. Argyle, Mayank Mahajan, Jonathan D Tang, Moritz Hardt, Ethan Porter, Diana Mercado-Garcia, Andrew Halpern-Manners, Anahit Sargsyan, Duncan J. Watts, Alex Pentland, Sonia P Hashim, Dean Knox, Onur Varol, Ryan Amos, James M. Wu, Thomas Davidson, Emma Tsurkov, Bernie Hogan, Areg Karapetyan, William Nowak, Jingwen Yin, Livia Baer-Bositis, Landon Schnabel, Chenyun Zhu, Noah Mandell, Ahmed Musse, Yue Gao, Josh Gagné, Stephen McKay, Jennie E. Brand, Abdullah Almaatouq, Katy M. Pinto, Andrew E Mack, Austin van Loon, Bedoor K. AlShebli, Helge Marahrens, Xiafei Wang, Bryan Schonfeld, Sonia Hausen, Kengran Yang, Maria Wolters, Brandon M. Stewart, Naman Jain, Moritz Büchi, Nicole Bohme Carnegie, Redwane Amin, Caitlin Ahearn, Kirstie Whitaker, Bo-Ryehn Chung, Diana Stanescu, Thomas Schaffner, Patrick Kaminski, David Jurgens, Kivan Polimis, Kimberly Higuera, Zhilin Fan, Matthew J. Salganik, Debanjan Datta, Connor Gilroy, E H Kim, Katariina Mueller-Gastell, Karen Levy, Brian J. Goode, Zhi Wang, Tamkinat Rauf, Lundberg, Ian [0000-0002-1909-2270], Almaatouq, Abdullah [0000-0002-8467-9123], Altschul, Drew M [0000-0001-7053-4209], Carnegie, Nicole Bohme [0000-0001-7664-6682], Kashyap, Ridhi [0000-0003-0615-2868], McKay, Stephen [0000-0002-5080-8417], Morgan, Allison C [0000-0003-2926-2162], Raes, Louis [0000-0003-2640-7493], Argyle, Lisa P [0000-0003-3109-2537], Büchi, Moritz [0000-0002-9202-889X], Jin, Kun [0000-0002-0118-1021], Varol, Onur [0000-0002-3994-6106], Watts, Duncan J [0000-0001-5005-4961], Apollo - University of Cambridge Repository, Department of Economics, and Research Group: Economics

Subjects
Male, Adolescent, Computer science, Social Sciences, 02 engineering and technology, Cohort Studies, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Life, Multidisciplinary approach, Predictive Value of Tests, Benchmark (surveying), 0202 electrical engineering, electronic engineering, information engineering, Econometrics, Humans, Family, Predictability, mass collaboration, Child, life course, Multidisciplinary, Fragile Families, Correction, Infant, Fragile Families and Child Wellbeing Study, prediction, Outcome (probability), Mass collaboration, machine learning, L310 Applied Sociology, Child, Preschool, Computational Social Science, Life course approach, 020201 artificial intelligence & image processing, Computational sociology, Female, 030217 neurology & neurosurgery
Abstract

© This open access article is distributed under Creative Commons Attribution-NonCommercialNoDerivatives License 4.0 (CC BY-NC-ND). How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

Published
2020

Catalog

Books, media, physical & digital resources
See catalog results