PLOS ONE
| Autor: | Noshir Contractor, Madhav V. Marathe, Yihui Ren, Xinwei Deng, Brian J. Goode, Parang Saraf, Vanessa Cedeno-Mieles, Chris J. Kuhlman, Saliya Ekanayake, Zhihao Hu, Naren Ramakrishnan, Dustin Machi, Joshua M. Epstein, Nathan Self, Michael W. Macy, Gizem Korkmaz |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Social Sciences 02 engineering and technology Systems Science Data modeling Software Pipelines Software Agent-Based Modeling DESIGN 0202 electrical engineering electronic engineering information engineering Psychology FAILURE Social science Multidisciplinary Simulation and Modeling Software Engineering SUCCESS Dynamical Systems Data model Physical Sciences Data analysis Engineering and Technology Medicine 020201 artificial intelligence & image processing Games Algorithms Research Article Computer and Information Sciences Science VALENCE ONLINE Research and Analysis Methods COLLECTIVE IDENTITY Computer Software Text mining INITIAL CONFIDENCE 020204 information systems Computational Techniques WORKFLOW MANAGEMENT Humans Software system Social Behavior Behavior Electronic Data Processing business.industry RESPONSIBILITY Computational Pipelines ATTRIBUTION Biology and Life Sciences Experimental data Models Theoretical Pipeline (software) Workflow Recreation business Mathematics |
| Zdroj: | PLoS ONE, Vol 15, Iss 11, p e0242453 (2020) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments. Published version |
| Databáze: | OpenAIRE |
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