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3 results on '"Argyle, Lisa"'

1. 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
Pediatric, Behavioral and Social Science, Generic health relevance, Adolescent, Child, Child, Preschool, Cohort Studies, Family, Female, Humans, Infant, Life, Machine Learning, Male, Predictive Value of Tests, Social Sciences, life course, prediction, machine learning, mass collaboration
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

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
Humans, Cohort Studies, Predictive Value of Tests, Family, Social Sciences, Life, Adolescent, Child, Child, Preschool, Infant, Female, Male, Machine Learning, life course, machine learning, mass collaboration, prediction, 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. The Paradox of Persuasion: Interpersonal Influence in Everyday Conversation

Author

Argyle, Lisa P.

Subjects
Political science, American Politics, Political Behavior, Political Persuasion, Public Opinion
Abstract

Political scientists have been using individuals’ self-reported efforts to try to influence the votes of others as one indicator of political activism for more than a half-century. However, in spite of this widespread use, very little is known about the motivations of interpersonal persuasion. This dissertation examines why some people try to influence the votes of others during the course of their everyday political conversations, while others are content to discuss politics without trying to persuade. Although attempts to persuade are often treated as a form of campaign participation with a goal of influencing the outcome of the election, I find that the motivations for persuasion are more internal and interpersonal than are the motivations for other forms of campaign involvement. I argue that interpersonal persuasion should be treated as a form of discursive participation, with consequences for our understanding of public opinion and deliberation.I use three large-scale survey datasets to examine interpersonal persuasion as a distinct form of political participation: the 2012 American National Election Studies (Ch. 3), the Youth-Parent Political Socialization Panel (Ch. 4), and the 2008 National Annenberg Election Studies Phone Survey (Ch. 5). Each dataset has distinct advantages in design and content that allow for examination of particular features of the motivations of persuasion. Using a variety of statistical methods and data sources, the overall argument is that attempts at persuasion have relatively little to do with campaigns and elections, and much more to do with individual orientations towards politics and social norms about political discussion. In particular, I find that organizational membership and campaign mobilization efforts do not provide the driving force for attempts to persuade that characterize other forms of political activity. Additionally, persuasive behavior does not ebb and flow in expected ways relative to the campaign cycle or the competitiveness of races. Rather, internal (but not external) efficacy, political capital (e.g., political interest, attention to political news), and exposure to disagreement and attempts to persuade by other social contacts are highly predictive of persuasive behavior. Persuasive behavior also declines over the life cycle, unlike many other forms of participation, and it is not a stepping stone to other more costly or time-intensive forms of participation. Therefore, persuasive behavior is equally or better understood within a framework of discursive participation, where it is a mechanism for people to develop their political identities, form political opinions, and process political events within the context of their social networks.

Published
2016

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