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Lundberg I, Brown-Weinstock R, Clampet-Lundquist S, Pachman S, Nelson TJ, Yang V, Edin K, Salganik MJ. The origins of unpredictability in life outcome prediction tasks. Proc Natl Acad Sci U S A 2024; 121:e2322973121. [PMID: 38833466 DOI: 10.1073/pnas.2322973121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 04/12/2024] [Indexed: 06/06/2024] Open
Abstract
Why are some life outcomes difficult to predict? We investigated this question through in-depth qualitative interviews with 40 families sampled from a multidecade longitudinal study. Our sampling and interviewing process was informed by the earlier efforts of hundreds of researchers to predict life outcomes for participants in this study. The qualitative evidence we uncovered in these interviews combined with a mathematical decomposition of prediction error led us to create a conceptual framework. Our specific evidence and our more general framework suggest that unpredictability should be expected in many life outcome prediction tasks, even in the presence of complex algorithms and large datasets. Our work provides a foundation for future empirical and theoretical work on unpredictability in human lives.
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Affiliation(s)
- Ian Lundberg
- Department of Information Science, Cornell University, Ithaca, NY 14853
| | | | | | - Sarah Pachman
- Office of Population Research, Princeton University, Princeton, NJ 08544
| | - Timothy J Nelson
- Department of Sociology, Princeton University, Princeton, NJ 08544
| | - Vicki Yang
- Department of Sociology, Princeton University, Princeton, NJ 08544
| | - Kathryn Edin
- Department of Sociology, Princeton University, Princeton, NJ 08544
- Office of Population Research, Princeton University, Princeton, NJ 08544
| | - Matthew J Salganik
- Department of Sociology, Princeton University, Princeton, NJ 08544
- Office of Population Research, Princeton University, Princeton, NJ 08544
- Center for Information Technology Policy, Princeton University, Princeton, NJ 08544
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2
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Domingue BW, Kanopka K, Kapoor R, Pohl S, Chalmers RP, Rahal C, Rhemtulla M. The InterModel Vigorish as a Lens for Understanding (and Quantifying) the Value of Item Response Models for Dichotomously Coded Items. PSYCHOMETRIKA 2024:10.1007/s11336-024-09977-2. [PMID: 38829495 DOI: 10.1007/s11336-024-09977-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 05/02/2024] [Indexed: 06/05/2024]
Abstract
The deployment of statistical models-such as those used in item response theory-necessitates the use of indices that are informative about the degree to which a given model is appropriate for a specific data context. We introduce the InterModel Vigorish (IMV) as an index that can be used to quantify accuracy for models of dichotomous item responses based on the improvement across two sets of predictions (i.e., predictions from two item response models or predictions from a single such model relative to prediction based on the mean). This index has a range of desirable features: It can be used for the comparison of non-nested models and its values are highly portable and generalizable. We use this fact to compare predictive performance across a variety of simulated data contexts and also demonstrate qualitative differences in behavior between the IMV and other common indices (e.g., the AIC and RMSEA). We also illustrate the utility of the IMV in empirical applications with data from 89 dichotomous item response datasets. These empirical applications help illustrate how the IMV can be used in practice and substantiate our claims regarding various aspects of model performance. These findings indicate that the IMV may be a useful indicator in psychometrics, especially as it allows for easy comparison of predictions across a variety of contexts.
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Affiliation(s)
| | - Klint Kanopka
- Graduate School of Education, Stanford University, Santa Clara, USA
| | - Radhika Kapoor
- Graduate School of Education, Stanford University, Santa Clara, USA
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3
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Daoud A, Johansson FD. The impact of austerity on children: Uncovering effect heterogeneity by political, economic, and family factors in low- and middle-income countries. SOCIAL SCIENCE RESEARCH 2024; 118:102973. [PMID: 38336420 DOI: 10.1016/j.ssresearch.2023.102973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 07/26/2023] [Accepted: 12/07/2023] [Indexed: 02/12/2024]
Abstract
Which children are most vulnerable when their government imposes austerity? Research tends to focus on either the political-economic level or the family level. Using a sample of nearly two million children in 67 countries, this study synthesizes theories from family sociology and political science to examine the heterogeneous effects on child poverty of economic shocks following the implementation of an International Monetary Fund (IMF) program. To discover effect heterogeneity, we apply machine learning to policy evaluation. We find that children's average probability of falling into poverty increases by 14 percentage points. We find substantial effect heterogeneity, with family wealth and governments' education spending as the two most important moderators. In contrast to studies that emphasize the vulnerability of low-income families, we find that middle-class children face an equally high risk of poverty. Our results show that synthesizing family and political factors yield deeper knowledge of how economic shocks affect children.
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Affiliation(s)
- Adel Daoud
- Center for Population and Development Studies, Harvard T.H. Chan School of Public Health, Harvard University, Boston MA, USA; Institute for Analytical Sociology, Linköping University, Sweden; The Division of Data Science and Artificial Intelligence, The Department of Computer Science and Engineering, Chalmers University of Technology, Sweden.
| | - Fredrik D Johansson
- The Division of Data Science and Artificial Intelligence, The Department of Computer Science and Engineering, Chalmers University of Technology, Sweden
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4
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Whiting ME, Watts DJ. A framework for quantifying individual and collective common sense. Proc Natl Acad Sci U S A 2024; 121:e2309535121. [PMID: 38227650 PMCID: PMC10823256 DOI: 10.1073/pnas.2309535121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 11/02/2023] [Indexed: 01/18/2024] Open
Abstract
The notion of common sense is invoked so frequently in contexts as diverse as everyday conversation, political debates, and evaluations of artificial intelligence that its meaning might be surmised to be unproblematic. Surprisingly, however, neither the intrinsic properties of common sense knowledge (what makes a claim commonsensical) nor the degree to which it is shared by people (its "commonness") have been characterized empirically. In this paper, we introduce an analytical framework for quantifying both these elements of common sense. First, we define the commonsensicality of individual claims and people in terms of the latter's propensity to agree on the former and their awareness of one another's agreement. Second, we formalize the commonness of common sense as a clique detection problem on a bipartite belief graph of people and claims, defining [Formula: see text] common sense as the fraction [Formula: see text] of claims shared by a fraction [Formula: see text] of people. Evaluating our framework on a dataset of [Formula: see text] raters evaluating [Formula: see text] diverse claims, we find that commonsensicality aligns most closely with plainly worded, fact-like statements about everyday physical reality. Psychometric attributes such as social perceptiveness influence individual common sense, but surprisingly demographic factors such as age or gender do not. Finally, we find that collective common sense is rare: At most, a small fraction [Formula: see text] of people agree on more than a small fraction [Formula: see text] of claims. Together, these results undercut universalistic beliefs about common sense and raise questions about its variability that are relevant both to human and artificial intelligence.
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Affiliation(s)
- Mark E. Whiting
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA19104
- Operations, Information and Decisions Department, Wharton School, University of Pennsylvania, Philadelphia, PA19104
| | - Duncan J. Watts
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA19104
- Operations, Information and Decisions Department, Wharton School, University of Pennsylvania, Philadelphia, PA19104
- Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA19104
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5
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Stulp G, Top L, Xu X, Sivak E. A data-driven approach shows that individuals' characteristics are more important than their networks in predicting fertility preferences. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230988. [PMID: 38126069 PMCID: PMC10731326 DOI: 10.1098/rsos.230988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 11/23/2023] [Indexed: 12/23/2023]
Abstract
People's networks are considered key in explaining fertility outcomes-whether people want and have children. Existing research on social influences on fertility is limited because data often come from small networks or highly selective samples, only few network variables are considered, and the strength of network effects is not properly assessed. We use data from a representative sample of Dutch women reporting on over 18 000 relationships. A data-driven approach including many network characteristics accounted for 0 to 40% of the out-of-sample variation in different outcomes related to fertility preferences. Individual characteristics were more important for all outcomes than network variables. Network composition was also important, particularly those people in the network desiring children or those choosing to be childfree. Structural network characteristics, which feature prominently in social influence theories and are based on the relations between people in the networks, hardly mattered. We discuss to what extent our results provide support for different mechanisms of social influence, and the advantages and disadvantages of our data-driven approach in comparison to traditional approaches.
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Affiliation(s)
- Gert Stulp
- Department of Sociology, University of Groningen, Grote Rozenstraat 31, 9712 TS Groningen, The Netherlands
- Inter-University Center for Social Science Theory and Methodology, University of Groningen, Grote Rozenstraat 31, 9712 TS Groningen, The Netherlands
| | - Lars Top
- Department of Sociology, University of Groningen, Grote Rozenstraat 31, 9712 TS Groningen, The Netherlands
| | - Xiao Xu
- Department of Sociology, University of Groningen, Grote Rozenstraat 31, 9712 TS Groningen, The Netherlands
- Netherlands Interdisciplinary Demographic Institute (NIDI-KNAW), Lange Houtstraat 19, 2511 CV Den Haag, The Hague, The Netherlands
| | - Elizaveta Sivak
- Department of Sociology, University of Groningen, Grote Rozenstraat 31, 9712 TS Groningen, The Netherlands
- Inter-University Center for Social Science Theory and Methodology, University of Groningen, Grote Rozenstraat 31, 9712 TS Groningen, The Netherlands
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6
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Leitgöb H, Prandner D, Wolbring T. Editorial: Big data and machine learning in sociology. FRONTIERS IN SOCIOLOGY 2023; 8:1173155. [PMID: 37229284 PMCID: PMC10203698 DOI: 10.3389/fsoc.2023.1173155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/13/2023] [Indexed: 05/27/2023]
Affiliation(s)
- Heinz Leitgöb
- Institute of Sociology, Leipzig University, Leipzig, Germany
- Institute of Sociology, University of Frankfurt, Frankfurt, Germany
| | | | - Tobias Wolbring
- Institute of Labour Market and Socioeconomics, University of Erlangen-Nuremberg, Nuremberg, Germany
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7
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Uitermark J, van Meeteren M. Geographical Network Analysis. TIJDSCHRIFT VOOR ECONOMISCHE EN SOCIALE GEOGRAFIE = JOURNAL OF ECONOMIC AND SOCIAL GEOGRAPHY = REVUE DE GEOGRAPHIE ECONOMIQUE ET HUMAINE = ZEITSCHRIFT FUR OKONOMISCHE UND SOZIALE GEOGRAPHIE = REVISTA DE GEOGRAFIA ECONOMICA Y SOCIAL 2021; 112:337-350. [PMID: 34594058 PMCID: PMC8459335 DOI: 10.1111/tesg.12480] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 03/31/2021] [Accepted: 04/09/2021] [Indexed: 06/13/2023]
Abstract
As the volume of digital data is growing exponentially and computational methods are advancing rapidly, network analysis is an increasingly important analytical tool to understand social life. This paper revisits the rich history of network analysis in geography and uses insights from that history to review contemporary computational social science. Based on that analysis, we synthesize the distinctive qualities of what we term geographical network analysis. Geographical network analysis presumes that networks are situated, construed through meaning, and reflect power relations. Instead of pursuing parsimonious explanations or universal theories, geographical network analysis strives to understand how uneven networks develop across space and within place through a constant back and forth between abstraction and contextualization. Drawing on the articles in this special issue, this paper illustrates how geographical network analysis can be put to work.
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Affiliation(s)
- Justus Uitermark
- Department of Geography, Planning, and International Development StudiesUniversity of AmsterdamAmsterdamthe Netherlands
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8
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Rocca R, Yarkoni T. Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction. ADVANCES IN METHODS AND PRACTICES IN PSYCHOLOGICAL SCIENCE 2021; 4:10.1177/25152459211026864. [PMID: 38737598 PMCID: PMC11087019 DOI: 10.1177/25152459211026864] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Abstract
Consensus on standards for evaluating models and theories is an integral part of every science. Nonetheless, in psychology, relatively little focus has been placed on defining reliable communal metrics to assess model performance. Evaluation practices are often idiosyncratic and are affected by a number of shortcomings (e.g., failure to assess models' ability to generalize to unseen data) that make it difficult to discriminate between good and bad models. Drawing inspiration from fields such as machine learning and statistical genetics, we argue in favor of introducing common benchmarks as a means of overcoming the lack of reliable model evaluation criteria currently observed in psychology. We discuss a number of principles benchmarks should satisfy to achieve maximal utility, identify concrete steps the community could take to promote the development of such benchmarks, and address a number of potential pitfalls and concerns that may arise in the course of implementation. We argue that reaching consensus on common evaluation benchmarks will foster cumulative progress in psychology and encourage researchers to place heavier emphasis on the practical utility of scientific models.
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Affiliation(s)
- Roberta Rocca
- Department of Psychology, University of Texas at Austin, Austin, Texas, USA
- Interacting Minds Centre, Aarhus University, Aarhus, Denmark
| | - Tal Yarkoni
- Department of Psychology, University of Texas at Austin, Austin, Texas, USA
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9
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Hofman JM, Watts DJ, Athey S, Garip F, Griffiths TL, Kleinberg J, Margetts H, Mullainathan S, Salganik MJ, Vazire S, Vespignani A, Yarkoni T. Integrating explanation and prediction in computational social science. Nature 2021; 595:181-188. [PMID: 34194044 DOI: 10.1038/s41586-021-03659-0] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/20/2021] [Indexed: 12/30/2022]
Abstract
Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions-the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes-and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.
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Affiliation(s)
| | - Duncan J Watts
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA. .,The Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA, USA. .,Operations, Information, and Decisions Department, University of Pennsylvania, Philadelphia, PA, USA.
| | - Susan Athey
- Graduate School of Business, Stanford University, Stanford, CA, USA
| | - Filiz Garip
- Department of Sociology, Princeton University, Princeton, NJ, USA
| | - Thomas L Griffiths
- Department of Psychology, Princeton University, Princeton, NJ, USA.,Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Jon Kleinberg
- Department of Computer Science, Cornell University, Ithaca, NY, USA.,Department of Information Science, Cornell University, Ithaca, NY, USA
| | - Helen Margetts
- Oxford Internet Institute, University of Oxford, Oxford, UK.,Public Policy Programme, The Alan Turing Institute, London, UK
| | | | | | - Simine Vazire
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
| | - Tal Yarkoni
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
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10
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Horne M, Youell J, Brown LJE, Simpson P, Dickinson T, Brown-Wilson C. A scoping review of education and training resources supporting care home staff in facilitating residents' sexuality, intimacy and relational needs. Age Ageing 2021; 50:758-771. [PMID: 33681969 PMCID: PMC8123381 DOI: 10.1093/ageing/afab022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Indexed: 11/22/2022] Open
Abstract
Background Having positive intimate, sexual and relational experiences is an important issue for older adults in care settings, yet little is known on the extent to which nursing staff and care workers have received education or training in addressing and meeting these needs among older residents. This scoping review aimed to identify and examine what education and training resources exist to assist nursing staff and care workers to meet their residents’ needs in this area. Methods and analysis Using the Arksey and O’Malley framework, we systematically searched papers and grey literature to identify education interventions and resources that aimed to facilitate care home staff to meet their residents’ sexuality, intimacy and relational needs. Results Eleven studies (one dissertation) and three education resources met the inclusion criteria; most were conducted in the USA and Australia. Across the studies and resources identified, the education content was mixed and the methodology, presentation, design and duration varied widely. The focus of the education interventions and resources was to increase knowledge and improve and/or change attitudes towards the: (i) sexual expression of older people living in residential aged care, (ii) sexuality and ageing and (iii) expression of sexuality in people with dementia. Conclusion Few education interventions and training resources were identified. The findings suggest that education interventions can improve knowledge and/or change care staff attitudes, in the short-term, towards older people’s sexuality, intimacy and relational needs in care home settings, which can lead to facilitating staff to enhance person-centred care in this area of need.
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Affiliation(s)
- Maria Horne
- Faculty of Medicine and Health, School of Healthcare, University of Leeds, Leeds, UK
| | - Jane Youell
- Faculty of Medicine and Health, School of Healthcare, University of Leeds, Leeds, UK
| | - Laura J E Brown
- Division of Psychology and Mental Health, School of Health Sciences, University of Manchester, Manchester, UK
| | - Paul Simpson
- Department of Applied Health and Social Care, Edge Hill University, Ormskirk, UK
| | - Tommy Dickinson
- Department of Mental Health Nursing, Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King’s College London, London, UK
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11
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Emmert-Streib F, Dehmer M. Data-Driven Computational Social Network Science: Predictive and Inferential Models for Web-Enabled Scientific Discoveries. Front Big Data 2021; 4:591749. [PMID: 33969290 PMCID: PMC8100320 DOI: 10.3389/fdata.2021.591749] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 02/18/2021] [Indexed: 12/13/2022] Open
Abstract
The ultimate goal of the social sciences is to find a general social theory encompassing all aspects of social and collective phenomena. The traditional approach to this is very stringent by trying to find causal explanations and models. However, this approach has been recently criticized for preventing progress due to neglecting prediction abilities of models that support more problem-oriented approaches. The latter models would be enabled by the surge of big Web-data currently available. Interestingly, this problem cannot be overcome with methods from computational social science (CSS) alone because this field is dominated by simulation-based approaches and descriptive models. In this article, we address this issue and argue that the combination of big social data with social networks is needed for creating prediction models. We will argue that this alliance has the potential for gradually establishing a causal social theory. In order to emphasize the importance of integrating big social data with social networks, we call this approach data-driven computational social network science (DD-CSNS).
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Affiliation(s)
- Frank Emmert-Streib
- Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.,Institute of Biosciences and Medical Technology, Tampere, Finland
| | - Matthias Dehmer
- Department of Computer Science, Swiss Distance University of Applied Sciences, Brig, Switzerland.,School of Science, Xian Technological University, Xian, China.,College of Artificial Intelligence, Nankai University, Tianjin, China.,Department of Biomedical Computer Science and Mechatronics, The Health and Life Science University, UMIT, Hall in Tyrol, Austria
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12
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Styhre A. Theoretical explanation, understanding and prediction in management studies. INTERNATIONAL JOURNAL OF ORGANIZATIONAL ANALYSIS 2021. [DOI: 10.1108/ijoa-11-2019-1935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
The purpose of this paper is to question whether the management studies is rigorous and/or relevant is a recurrent debate in the discipline. There is reason to believe that this dichotomy is simplistic as relevance is a consequence of rigor (defined in a variety of ways in the literature), whereas the epistemic value of rigor must per se be examined in more detail.
Design/methodology/approach
Drawing on the study of the analytical philosophy of Donald Davidson, rigor is here conceptualized as the capacity to examine how intersubjective meaning is constituted on the basis of the semantics of the everyday language.
Findings
In Davidson’s pragmatist view, individual beliefs are established through communication, and beliefs generate preferences and “pro-attitudes” that result in social action. Using Ian Hacking’s term undoing as a critique of a proposition or idea as being no contender for truth (or some other quality) at all, the paper questions the proposition that scientific rigor can be operationalized as the use of data collection and analysis methods developed in other and more authoritative disciplines, e.g. economics. On the contrary, to make accurate descriptions of beliefs, preferences and actions on the basis of the use of everyday language is the mark of scientific rigor in management studies.
Originality/value
The paper addresses the question of how rigorous research in management studies is essentially a matter of explanation, and that explanation, in turn, demands a more elaborate theory of action. The paper also introduces the work of Donald Davidson as an important figure when theorizing action.
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13
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Saha K, Torous J, Caine ED, De Choudhury M. Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media. J Med Internet Res 2020; 22:e22600. [PMID: 33156805 PMCID: PMC7690250 DOI: 10.2196/22600] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 08/19/2020] [Accepted: 10/26/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multifaceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a "mental health tsunami", the psychological effects of the COVID-19 crisis remain unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances. OBJECTIVE Our study aims to provide insights regarding people's psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support expressions in the pandemic context. METHODS We obtained about 60 million Twitter streaming posts originating from the United States from March 24 to May 24, 2020, and compared these with about 40 million posts from a comparable period in 2019 to attribute the effect of COVID-19 on people's social media self-disclosure. Using these data sets, we studied people's self-disclosure on social media in terms of symptomatic mental health concerns and expressions of support. We employed transfer learning classifiers that identified the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examined the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 data sets. RESULTS We found that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis-mental health symptomatic expressions have increased by about 14%, and support expressions have increased by about 5%, both thematically related to COVID-19. We also observed a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlighted that people express concerns that are specific to and contextually related to the COVID-19 crisis. CONCLUSIONS We studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people's mental health symptomatic and support expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their "new normal." Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, health care and precautionary measures, and pandemic-related awareness. This study shows the potential to provide insights to mental health care and stakeholders and policy makers in planning and implementing measures to mitigate mental health risks amid the health crisis.
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Affiliation(s)
- Koustuv Saha
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Eric D Caine
- Department of Psychiatry, University of Rochester, Rochester, NY, United States
| | - Munmun De Choudhury
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States
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14
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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, McLanahan S. Measuring the predictability of life outcomes with a scientific mass collaboration. Proc Natl Acad Sci U S A 2020; 117:8398-8403. [PMID: 32229555 PMCID: PMC7165437 DOI: 10.1073/pnas.1915006117] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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.
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Affiliation(s)
| | - Ian Lundberg
- Department of Sociology, Princeton University, Princeton, NJ 08544
| | | | - Caitlin E Ahearn
- Department of Sociology, University of California, Los Angeles, CA 90095
| | | | - Abdullah Almaatouq
- Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Drew M Altschul
- Mental Health Data Science Scotland, Department of Psychology, The University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
| | - Jennie E Brand
- Department of Sociology, University of California, Los Angeles, CA 90095
- Department of Statistics, University of California, Los Angeles, CA 90095
| | | | - Ryan James Compton
- Human Computer Interaction Lab, University of California, Santa Cruz, CA 95064
| | - Debanjan Datta
- Discovery Analytics Center, Virginia Polytechnic Institute and State University, Arlington, VA 22203
| | - Thomas Davidson
- Department of Sociology, Cornell University, Ithaca, NY 14853
| | | | - Connor Gilroy
- Department of Sociology, University of Washington, Seattle, WA 98105
| | - Brian J Goode
- Social and Decision Analytics Laboratory, Fralin Life Sciences Institute, Virginia Polytechnic Institute and State University, Arlington, VA 22203
| | - Eaman Jahani
- Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Ridhi Kashyap
- Department of Sociology, University of Oxford, Oxford OX1 1JD, United Kingdom
- Nuffield College, University of Oxford, Oxford OX1 1NF, United Kingdom
- School of Anthropology and Museum Ethnography, University of Oxford, Oxford OX2 6PE, United Kingdom
| | - Antje Kirchner
- Program for Research in Survey Methodology, Survey Research Division, RTI International, Research Triangle Park, NC 27709
| | - Stephen McKay
- School of Social and Political Sciences, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, United Kingdom
| | - Allison C Morgan
- Department of Computer Science, University of Colorado, Boulder, CO 80309
| | - Alex Pentland
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Kivan Polimis
- Center for the Study of Demography and Ecology, University of Washington, Seattle, WA 98105
| | - Louis Raes
- Department of Economics, Tilburg School of Economics and Management, Tilburg University, 5037 AB Tilburg, The Netherlands
| | - Daniel E Rigobon
- Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544
| | - Claudia V Roberts
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Diana M Stanescu
- Department of Politics, Princeton University,Princeton, NJ, 08544
| | - Yoshihiko Suhara
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Adaner Usmani
- Department of Sociology, Harvard University, Cambridge, MA 02138
| | - Erik H Wang
- Department of Politics, Princeton University,Princeton, NJ, 08544
| | - Muna Adem
- Department of Sociology, Indiana University, Bloomington, IN 47405
| | - Abdulla Alhajri
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Bedoor AlShebli
- Computational Social Science Lab, Social Science Division, New York University Abu Dhabi, 129188 Abu Dhabi, United Arab Emirates
| | - Redwane Amin
- Bendheim Center for Finance, Princeton University, Princeton, NJ 08544
| | - Ryan B Amos
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Lisa P Argyle
- Department of Political Science, Brigham Young University, Provo, UT 84602
| | | | - Moritz Büchi
- Department of Communication and Media Research, University of Zurich, Zurich, Switzerland, ZH-8050
| | - Bo-Ryehn Chung
- Center for Statistics & Machine Learning, Princeton University, Princeton, NJ 08544
| | - William Eggert
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544
| | - Gregory Faletto
- Statistics Group, Department of Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, CA 90089
| | - Zhilin Fan
- Department of Statistics, Columbia University, New York, NY 10027
| | - Jeremy Freese
- Department of Sociology, Stanford University, Stanford, CA 94305
| | - Tejomay Gadgil
- Center for Data Science, New York University, New York, NY 10011
| | - Josh Gagné
- Department of Sociology, Stanford University, Stanford, CA 94305
| | - Yue Gao
- Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027
| | | | - Sonia P Hashim
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Sonia Hausen
- Department of Sociology, Stanford University, Stanford, CA 94305
| | - Guanhua He
- Department of Molecular Biology, Princeton University, Princeton, NJ 08544
| | - Kimberly Higuera
- Department of Sociology, Stanford University, Stanford, CA 94305
| | - Bernie Hogan
- Oxford Internet Institute, University of Oxford, Oxford OX1 3JS, United Kingdom
| | - Ilana M Horwitz
- Graduate School of Education, Stanford University, Stanford, CA, 94305
| | - Lisa M Hummel
- Department of Sociology, Stanford University, Stanford, CA 94305
| | - Naman Jain
- Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544
| | - Kun Jin
- Department of Computer Science, Ohio State University, Columbus, OH 43210
| | - David Jurgens
- School of Information, University of Michigan, Ann Arbor, MI 48104
| | - Patrick Kaminski
- Department of Sociology, Indiana University, Bloomington, IN 47405
- Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN 47405
| | - Areg Karapetyan
- Department of Computer Science, Masdar Institute, Khalifa University, 127788 Abu Dhabi, United Arab Emirates
- Research Institute for Mathematical Sciences, Kyoto University, Kyoto 606-8502, Japan
| | - E H Kim
- Department of Sociology, Stanford University, Stanford, CA 94305
| | - Ben Leizman
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Naijia Liu
- Department of Politics, Princeton University,Princeton, NJ, 08544
| | - Malte Möser
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Andrew E Mack
- Department of Politics, Princeton University,Princeton, NJ, 08544
| | - Mayank Mahajan
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Noah Mandell
- Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544
| | - Helge Marahrens
- Department of Sociology, Indiana University, Bloomington, IN 47405
| | | | - Viola Mocz
- Department of Neuroscience, Princeton University, Princeton, NJ 08544
| | | | - Ahmed Musse
- Department of Electrical Engineering, Princeton University, Princeton, NJ, 08544
| | - Qiankun Niu
- Bendheim Center for Finance, Princeton University, Princeton, NJ 08544
| | | | - Hamidreza Omidvar
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544
| | - Andrew Or
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Karen Ouyang
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Katy M Pinto
- Department of Sociology, California State University, Dominguez Hills, Carson, CA 90747
| | - Ethan Porter
- School of Media and Public Affairs, George Washington University, Washington, DC 20052
| | | | - Crystal Qian
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Tamkinat Rauf
- Department of Sociology, Stanford University, Stanford, CA 94305
| | - Anahit Sargsyan
- Social Science Division, New York University Abu Dhabi, 129188 Abu Dhabi, United Arab Emirates
| | - Thomas Schaffner
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Landon Schnabel
- Department of Sociology, Stanford University, Stanford, CA 94305
| | - Bryan Schonfeld
- Department of Politics, Princeton University,Princeton, NJ, 08544
| | - Ben Sender
- Department of Economics, Princeton University, Princeton, NJ 08544
| | - Jonathan D Tang
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Emma Tsurkov
- Department of Sociology, Stanford University, Stanford, CA 94305
| | - Austin van Loon
- Department of Sociology, Stanford University, Stanford, CA 94305
| | - Onur Varol
- Center for Complex Network Research, Northeastern University Networks Science Institute, Boston, MA 02115
- Luddy School of Informatics, Computing, & Engineering, Indiana University, Bloomington, IN 47408
| | - Xiafei Wang
- School of Social Work, David B. Falk College of Sport and Human Dynamics, Syracuse University, NY 13244
| | - Zhi Wang
- Luddy School of Informatics, Computing, & Engineering, Indiana University, Bloomington, IN 47408
- School of Public Health, Indiana University, Bloomington, IN 47408
| | - Julia Wang
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Flora Wang
- Department of Economics, Princeton University, Princeton, NJ 08544
| | - Samantha Weissman
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Kirstie Whitaker
- The Alan Turing Institute, London NW1 2DB, United Kingdom
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, United Kingdom
| | - Maria K Wolters
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom
| | - Wei Lee Woon
- Department of Marketplaces & Yield Data Science, Expedia Group, Seattle, WA 98119
| | - James Wu
- Department of the Applied Statistics, Social Science, and Humanities, New York University, New York, NY 10003
| | - Catherine Wu
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | - Kengran Yang
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544
| | - Jingwen Yin
- Department of Statistics, Columbia University, New York, NY 10027
| | - Bingyu Zhao
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
| | - Chenyun Zhu
- Department of Statistics, Columbia University, New York, NY 10027
| | - Jeanne Brooks-Gunn
- Department of Human Development, Teachers College, Columbia University, New York, NY 10027
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032
| | - Barbara E Engelhardt
- Department of Computer Science, Princeton University, Princeton, NJ 08544
- Center for Statistics & Machine Learning, Princeton University, Princeton, NJ 08544
| | - Moritz Hardt
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720
| | - Dean Knox
- Department of Politics, Princeton University,Princeton, NJ, 08544
| | - Karen Levy
- Department of Information Science, Cornell University, Ithaca, NY 14853
| | - Arvind Narayanan
- Department of Computer Science, Princeton University, Princeton, NJ 08544
| | | | - Duncan J Watts
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104
- Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA 19104
- Operations, Information and Decisions Department, University of Pennsylvania, Philadelphia, PA 19104
| | - Sara McLanahan
- Department of Sociology, Princeton University, Princeton, NJ 08544;
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Abstract
Can events be accurately described as historic at the time they are happening? Claims of this sort are in effect predictions about the evaluations of future historians; that is, that they will regard the events in question as significant. Here we provide empirical evidence in support of earlier philosophical arguments1 that such claims are likely to be spurious and that, conversely, many events that will one day be viewed as historic attract little attention at the time. We introduce a conceptual and methodological framework for applying machine learning prediction models to large corpora of digitized historical archives. We find that although such models can correctly identify some historically important documents, they tend to overpredict historical significance while also failing to identify many documents that will later be deemed important, where both types of error increase monotonically with the number of documents under consideration. On balance, we conclude that historical significance is extremely difficult to predict, consistent with other recent work on intrinsic limits to predictability in complex social systems2,3. However, the results also indicate the feasibility of developing 'artificial archivists' to identify potentially historic documents in very large digital corpora.
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Kindel AT, Bansal V, Catena KD, Hartshorne TH, Jaeger K, Koffman D, McLanahan S, Phillips M, Rouhani S, Vinh R, Salganik MJ. Improving Metadata Infrastructure for Complex Surveys: Insights from the Fragile Families Challenge. SOCIUS : SOCIOLOGICAL RESEARCH FOR A DYNAMIC WORLD 2019; 5:10.1177/2378023118817378. [PMID: 37214352 PMCID: PMC10198672 DOI: 10.1177/2378023118817378] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Researchers rely on metadata systems to prepare data for analysis. As the complexity of data sets increases and the breadth of data analysis practices grow, existing metadata systems can limit the efficiency and quality of data preparation. This article describes the redesign of a metadata system supporting the Fragile Families and Child Wellbeing Study on the basis of the experiences of participants in the Fragile Families Challenge. The authors demonstrate how treating metadata as data (i.e., releasing comprehensive information about variables in a format amenable to both automated and manual processing) can make the task of data preparation less arduous and less error prone for all types of data analysis. The authors hope that their work will facilitate new applications of machine-learning methods to longitudinal surveys and inspire research on data preparation in the social sciences. The authors have open-sourced the tools they created so that others can use and improve them.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Ryan Vinh
- Princeton University, Princeton, NJ, USA
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18
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Lundberg I, Narayanan A, Levy K, Salganik MJ. Privacy, Ethics, and Data Access: A Case Study of the Fragile Families Challenge. SOCIUS : SOCIOLOGICAL RESEARCH FOR A DYNAMIC WORLD 2019; 5:237802311881302. [PMID: 37347012 PMCID: PMC10284584 DOI: 10.1177/2378023118813023] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/23/2023]
Abstract
Stewards of social data face a fundamental tension. On one hand, they want to make their data accessible to as many researchers as possible to facilitate new discoveries. At the same time, they want to restrict access to their data as much as possible to protect the people represented in the data. In this article, we provide a case study addressing this common tension in an uncommon setting: the Fragile Families Challenge, a scientific mass collaboration designed to yield insights that could improve the lives of disadvantaged children in the United States. We describe our process of threat modeling, threat mitigation, and third-party guidance. We also describe the ethical principles that formed the basis of our process. We are open about our process and the trade-offs we made in the hope that others can improve on what we have done.
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Puranam P. The Organizational Foundations of Behavioral Strategy. ADVANCES IN STRATEGIC MANAGEMENT-A RESEARCH ANNUAL 2018. [DOI: 10.1108/s0742-332220180000039006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Keuschnigg M, Lovsjö N, Hedström P. Analytical sociology and computational social science. JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE 2018; 1:3-14. [PMID: 31930176 PMCID: PMC6936355 DOI: 10.1007/s42001-017-0006-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2017] [Accepted: 11/09/2017] [Indexed: 05/18/2023]
Abstract
Analytical sociology focuses on social interactions among individuals and the hard-to-predict aggregate outcomes they bring about. It seeks to identify generalizable mechanisms giving rise to emergent properties of social systems which, in turn, feed back on individual decision-making. This research program benefits from computational tools such as agent-based simulations, machine learning, and large-scale web experiments, and has considerable overlap with the nascent field of computational social science. By providing relevant analytical tools to rigorously address sociology's core questions, computational social science has the potential to advance sociology in a similar way that the introduction of econometrics advanced economics during the last half century. Computational social scientists from computer science and physics often see as their main task to establish empirical regularities which they view as "social laws." From the perspective of the social sciences, references to social laws appear unfounded and misplaced, however, and in this article we outline how analytical sociology, with its theory-grounded approach to computational social science, can help to move the field forward from mere descriptions and predictions to the explanation of social phenomena.
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Affiliation(s)
- Marc Keuschnigg
- The Institute for Analytical Sociology, Linköping University, Norra Grytsgatan 10, 601 74 Norrköping, Sweden
| | - Niclas Lovsjö
- The Institute for Analytical Sociology, Linköping University, Norra Grytsgatan 10, 601 74 Norrköping, Sweden
| | - Peter Hedström
- The Institute for Analytical Sociology, Linköping University, Norra Grytsgatan 10, 601 74 Norrköping, Sweden
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21
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Filbee‐Dexter K, Pittman J, Haig HA, Alexander SM, Symons CC, Burke MJ. Ecological surprise: concept, synthesis, and social dimensions. Ecosphere 2017. [DOI: 10.1002/ecs2.2005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Affiliation(s)
- Karen Filbee‐Dexter
- Marine Section Norwegian Institute for Water Research Gaustadalléen 21 Oslo0349 Norway
- Department of Biology Dalhousie University 1355 Oxford Street Halifax Nova Scotia B3H 4R2 Canada
| | - Jeremy Pittman
- School of Planning University of Waterloo 200 University Avenue West Waterloo Ontario N2L 3G1 Canada
| | - Heather A. Haig
- Department of Biology Limnology Laboratory University of Regina 3737 Wascana Parkway Regina Saskatchewan S4S 0A2 Canada
| | - Steven M. Alexander
- National Socio‐Environmental Synthesis Center University of Maryland 1 Park Place Annapolis Maryland 21401 USA
- Stockholm Resilience Centre Stockholm University Kräftriket 2B Stockholm 10691 Sweden
| | - Celia C. Symons
- Department of Ecology and Evolutionary Biology University of California, Santa Cruz 1156 High St. Santa Cruz California 95064 USA
| | - Matthew J. Burke
- Department of Natural Resource Sciences and McGill School of Environment McGill University 3534 University St. Montréal Quebec H3A 2A7 Canada
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22
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Abstract
Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current practices for evaluating predictions must be better standardized. Second, theoretical limits to predictive accuracy in complex social systems must be better characterized, thereby setting expectations for what can be predicted or explained. Third, predictive accuracy and interpretability must be recognized as complements, not substitutes, when evaluating explanations. Resolving these three issues will lead to better, more replicable, and more useful social science.
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Affiliation(s)
- Jake M Hofman
- Microsoft Research, 641 Avenue of the Americas, 7th Floor, New York, NY 10003, USA.
| | - Amit Sharma
- Microsoft Research, 641 Avenue of the Americas, 7th Floor, New York, NY 10003, USA.
| | - Duncan J Watts
- Microsoft Research, 641 Avenue of the Americas, 7th Floor, New York, NY 10003, USA.
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23
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Heiberger RH, Riebling JR. Installing computational social science: Facing the challenges of new information and communication technologies in social science. METHODOLOGICAL INNOVATIONS 2016. [DOI: 10.1177/2059799115622763] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Today’s world allows people to connect over larger distances and in shorter intervals than ever before, widely monitored by massive online data sources. Ongoing worldwide computerization has led to completely new opportunities for social scientists to conceive human interactions and relations in unknown precision and quantities. However, the large data sets require techniques that are more likely to be found in computer and natural sciences than in the established fields of social relations. In order to facilitate the participation of social scientists in an emerging interdisciplinary research branch of “computational social science,” we propose in this article the usage of the Python programming language. First, we carve out its capacity to handle “Big Data” in suitable formats. Second, we introduce programming libraries to analyze large networks and big text corpora, conduct simulations, and compare their performance to their counterparts in the R environment. Furthermore, we highlight practical tools implemented in Python for operational tasks like preparing presentations. Finally, we discuss how the process of writing code may help to exemplify theoretical concepts and could lead to empirical applications that gain a better understanding of the social processes initiated by the truly global connections of the Internet era.
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