TY - GEN
T1 - Pipelines and their compositions for modeling and analysis of controlled online networked social science experiments
AU - Cedeno-Mieles, Vanessa
AU - Hu, Zhihao
AU - Deng, Xinwei
AU - Contractor, Noshir
AU - Ren, Yihui
AU - Ekanayake, Saliya
AU - Goode, Brian J.
AU - Kuhlman, Chris J.
AU - Machi, Dustin
AU - Marathe, Madhav V.
AU - Mortveit, Henning H.
AU - Ramakrishnan, Naren
AU - Saraf, Parang
AU - Self, Nathan
AU - Epstein, Joshua M.
AU - Macy, Michael W.
N1 - Funding Information:
We thank the reviewers for their valuable suggestions. We thank the computer systems administrators and managers at the Biocomplexity Institute for their help in this and many other works: Dominik Borkowski, William Miles Gentry, Jeremy Johnson, William Marmagas, Douglas McMaster, Kevin Shinpaugh, and Robert Wills. This work has been partially supported by DARPA Cooperative Agreement D17AC00003 (NGS2), DTRA CNIMS (Contract HDTRA1-11-D-0016- 0001), NSF DIBBS Grant ACI-1443054, NSF BIG DATA Grant IIS-1633028, NSF Grants DGE-1545362 and IIS-1633363, and ARL Grant W911NF-17-1-0021. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA, DTRA, NSF, ARL, or the U.S. Government.
Funding Information:
We thank the reviewers for their valuable suggestions. We thank the computer systems administrators and managers at the Biocomplexity Institute for their help in this and many other works: Dominik Borkowski, William Miles Gentry, Jeremy Johnson, William Marmagas, Douglas McMaster, Kevin Shinpaugh, and Robert Wills. This work has been partially supported by DARPA Cooperative Agreement D17AC00003 (NGS2), DTRA CNIMS (Contract HDTRA1-11-D-0016-0001), NSF DIBBS Grant ACI-1443054, NSF BIG DATA Grant IIS-1633028, NSF Grants DGE-1545362 and IIS-1633363, and ARL Grant W911NF-17-1-0021. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA, DTRA, NSF, ARL, or the U.S. Government.
Publisher Copyright:
© 2018 IEEE
PY - 2019/1/31
Y1 - 2019/1/31
N2 - There has been significant growth in online social science experiments in order to understand behavior at-scale, with finer-grained data collection. Considerable work is required to perform data analytics for custom experiments. We also seek to perform repeated networked experiments and modeling in an iterative loop. In this work, we design and build four composable and extensible automated software pipelines for (1) data analytics; (2) model property inference; (3) model/simulation; and (4) results analysis and comparisons between experimental data and model predictions. To reason about experiments and models, we design a formal data model. Our data model is for scenarios where subjects can repeat actions (from a set) any number of times over the game duration. Because the types of interactions and action sets are flexible, this class of experiments is large. Two case studies, on collective identity and complex contagion, illustrate use of the system.
AB - There has been significant growth in online social science experiments in order to understand behavior at-scale, with finer-grained data collection. Considerable work is required to perform data analytics for custom experiments. We also seek to perform repeated networked experiments and modeling in an iterative loop. In this work, we design and build four composable and extensible automated software pipelines for (1) data analytics; (2) model property inference; (3) model/simulation; and (4) results analysis and comparisons between experimental data and model predictions. To reason about experiments and models, we design a formal data model. Our data model is for scenarios where subjects can repeat actions (from a set) any number of times over the game duration. Because the types of interactions and action sets are flexible, this class of experiments is large. Two case studies, on collective identity and complex contagion, illustrate use of the system.
UR - http://www.scopus.com/inward/record.url?scp=85062631161&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062631161&partnerID=8YFLogxK
U2 - 10.1109/WSC.2018.8632478
DO - 10.1109/WSC.2018.8632478
M3 - Conference contribution
AN - SCOPUS:85062631161
T3 - Proceedings - Winter Simulation Conference
SP - 774
EP - 785
BT - WSC 2018 - 2018 Winter Simulation Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Winter Simulation Conference, WSC 2018
Y2 - 9 December 2018 through 12 December 2018
ER -