Generative modeling of human behavior and social interactions using abductive analysis

Yihui Ren; Vanessa Cedeno-Mieles; Zhihao Hu; Xinwei Deng; Abhijin Adiga; Christopher Barrett; Saliya Ekanayake; Brian J. Goode; Gizem Korkmaz; Chris J. Kuhlman; Dustin Machi; Madhav V. Marathe; Naren Ramakrishnan; S. S. Ravi; Parang Sarat; Nathan Selt; Noshir Contractor; Joshua Epstein; Michael W. Macy

doi:10.1109/ASONAM.2018.8508282

Generative modeling of human behavior and social interactions using abductive analysis

Yihui Ren, Vanessa Cedeno-Mieles, Zhihao Hu, Xinwei Deng, Abhijin Adiga, Christopher Barrett, Saliya Ekanayake, Brian J. Goode, Gizem Korkmaz, Chris J. Kuhlman, Dustin Machi, Madhav V. Marathe, Naren Ramakrishnan, S. S. Ravi, Parang Sarat, Nathan Selt, Noshir Contractor, Joshua Epstein, Michael W. Macy

Global Public Health

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Abduction is an inference approach that uses data and observations to identify plausible (and preferably, best) explanations for phenomena. Applications of abduction (e.g., robotics, genetics, image understanding) have largely been devoid of human behavior. Here, we devise and execute an iterative abductive analysis process that is driven by the social sciences: Behaviors and interactions among groups of human subjects. One goal is to understand intra-group cooperation and its effect on fostering collective identity. We build an online game platform; perform and analyze controlled laboratory experiments; form hypotheses; build, exercise, and evaluate network-based agent-based models; and evaluate the hypotheses in multiple abductive iterations, improving our understanding as the process unfolds. While the experimental results are of interest, the paper's thrust is methodological, and indeed establishes the potential of iterative abductive looping for the (computational) social sciences.

Original language	English (US)
Title of host publication	Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
Editors	Andrea Tagarelli, Chandan Reddy, Ulrik Brandes
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	413-420
Number of pages	8
ISBN (Electronic)	9781538660515
DOIs	https://doi.org/10.1109/ASONAM.2018.8508282
State	Published - Oct 24 2018
Event	10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 - Barcelona, Spain Duration: Aug 28 2018 → Aug 31 2018

Publication series

Name	Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018

Other

Other	10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
Country/Territory	Spain
City	Barcelona
Period	8/28/18 → 8/31/18

ASJC Scopus subject areas

Sociology and Political Science
Communication
Computer Networks and Communications
Information Systems and Management

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10.1109/ASONAM.2018.8508282

Cite this

Ren, Y., Cedeno-Mieles, V., Hu, Z., Deng, X., Adiga, A., Barrett, C., Ekanayake, S., Goode, B. J., Korkmaz, G., Kuhlman, C. J., Machi, D., Marathe, M. V., Ramakrishnan, N., Ravi, S. S., Sarat, P., Selt, N., Contractor, N., Epstein, J., & Macy, M. W. (2018). Generative modeling of human behavior and social interactions using abductive analysis. In A. Tagarelli, C. Reddy, & U. Brandes (Eds.), Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 (pp. 413-420). Article 8508282 (Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2018.8508282

Generative modeling of human behavior and social interactions using abductive analysis. / Ren, Yihui; Cedeno-Mieles, Vanessa; Hu, Zhihao et al.
Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018. ed. / Andrea Tagarelli; Chandan Reddy; Ulrik Brandes. Institute of Electrical and Electronics Engineers Inc., 2018. p. 413-420 8508282 (Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ren, Y, Cedeno-Mieles, V, Hu, Z, Deng, X, Adiga, A, Barrett, C, Ekanayake, S, Goode, BJ, Korkmaz, G, Kuhlman, CJ, Machi, D, Marathe, MV, Ramakrishnan, N, Ravi, SS, Sarat, P, Selt, N, Contractor, N, Epstein, J & Macy, MW 2018, Generative modeling of human behavior and social interactions using abductive analysis. in A Tagarelli, C Reddy & U Brandes (eds), Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018., 8508282, Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 413-420, 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018, Barcelona, Spain, 8/28/18. https://doi.org/10.1109/ASONAM.2018.8508282

Ren Y, Cedeno-Mieles V, Hu Z, Deng X, Adiga A, Barrett C et al. Generative modeling of human behavior and social interactions using abductive analysis. In Tagarelli A, Reddy C, Brandes U, editors, Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 413-420. 8508282. (Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018). doi: 10.1109/ASONAM.2018.8508282

Ren, Yihui ; Cedeno-Mieles, Vanessa ; Hu, Zhihao et al. / Generative modeling of human behavior and social interactions using abductive analysis. Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018. editor / Andrea Tagarelli ; Chandan Reddy ; Ulrik Brandes. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 413-420 (Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018).

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abstract = "Abduction is an inference approach that uses data and observations to identify plausible (and preferably, best) explanations for phenomena. Applications of abduction (e.g., robotics, genetics, image understanding) have largely been devoid of human behavior. Here, we devise and execute an iterative abductive analysis process that is driven by the social sciences: Behaviors and interactions among groups of human subjects. One goal is to understand intra-group cooperation and its effect on fostering collective identity. We build an online game platform; perform and analyze controlled laboratory experiments; form hypotheses; build, exercise, and evaluate network-based agent-based models; and evaluate the hypotheses in multiple abductive iterations, improving our understanding as the process unfolds. While the experimental results are of interest, the paper's thrust is methodological, and indeed establishes the potential of iterative abductive looping for the (computational) social sciences.",

author = "Yihui Ren and Vanessa Cedeno-Mieles and Zhihao Hu and Xinwei Deng and Abhijin Adiga and Christopher Barrett and Saliya Ekanayake and Goode, {Brian J.} and Gizem Korkmaz and Kuhlman, {Chris J.} and Dustin Machi and Marathe, {Madhav V.} and Naren Ramakrishnan and Ravi, {S. S.} and Parang Sarat and Nathan Selt and Noshir Contractor and Joshua Epstein and Macy, {Michael W.}",

note = "Funding Information: 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 is supported in part by DARPA Cooperative Agreement D17AC00003 (NGS2), DTRA CNIMS (Contract HDTRA1-11-D-0016-0001), National Science Foundation via grants DGE-1545362, IIS-1633363, and DIBBS ACI-1443054, and Army Research Laboratory under grant W911NF-17-1-0021. The US Government is authorized to reproduce and distribute reprints of this work 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, Army Research Laboratory, or the U.S. Funding Information: This work was motivated in part by the DARPA Next Generation Social Science (NGS2) program. Goals of the program include devising methods to identify theories that are and are not applicable for explaining societal events. Publisher Copyright: {\textcopyright} 2018 IEEE.; 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 ; Conference date: 28-08-2018 Through 31-08-2018",

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AU - Barrett, Christopher

AU - Ekanayake, Saliya

AU - Goode, Brian J.

AU - Korkmaz, Gizem

AU - Kuhlman, Chris J.

AU - Machi, Dustin

AU - Marathe, Madhav V.

AU - Ramakrishnan, Naren

AU - Ravi, S. S.

AU - Sarat, Parang

AU - Selt, Nathan

AU - Contractor, Noshir

AU - Epstein, Joshua

AU - Macy, Michael W.

N1 - Funding Information: 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 is supported in part by DARPA Cooperative Agreement D17AC00003 (NGS2), DTRA CNIMS (Contract HDTRA1-11-D-0016-0001), National Science Foundation via grants DGE-1545362, IIS-1633363, and DIBBS ACI-1443054, and Army Research Laboratory under grant W911NF-17-1-0021. The US Government is authorized to reproduce and distribute reprints of this work 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, Army Research Laboratory, or the U.S. Funding Information: This work was motivated in part by the DARPA Next Generation Social Science (NGS2) program. Goals of the program include devising methods to identify theories that are and are not applicable for explaining societal events. Publisher Copyright: © 2018 IEEE.

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N2 - Abduction is an inference approach that uses data and observations to identify plausible (and preferably, best) explanations for phenomena. Applications of abduction (e.g., robotics, genetics, image understanding) have largely been devoid of human behavior. Here, we devise and execute an iterative abductive analysis process that is driven by the social sciences: Behaviors and interactions among groups of human subjects. One goal is to understand intra-group cooperation and its effect on fostering collective identity. We build an online game platform; perform and analyze controlled laboratory experiments; form hypotheses; build, exercise, and evaluate network-based agent-based models; and evaluate the hypotheses in multiple abductive iterations, improving our understanding as the process unfolds. While the experimental results are of interest, the paper's thrust is methodological, and indeed establishes the potential of iterative abductive looping for the (computational) social sciences.

AB - Abduction is an inference approach that uses data and observations to identify plausible (and preferably, best) explanations for phenomena. Applications of abduction (e.g., robotics, genetics, image understanding) have largely been devoid of human behavior. Here, we devise and execute an iterative abductive analysis process that is driven by the social sciences: Behaviors and interactions among groups of human subjects. One goal is to understand intra-group cooperation and its effect on fostering collective identity. We build an online game platform; perform and analyze controlled laboratory experiments; form hypotheses; build, exercise, and evaluate network-based agent-based models; and evaluate the hypotheses in multiple abductive iterations, improving our understanding as the process unfolds. While the experimental results are of interest, the paper's thrust is methodological, and indeed establishes the potential of iterative abductive looping for the (computational) social sciences.

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ER -

Generative modeling of human behavior and social interactions using abductive analysis

Abstract

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ASJC Scopus subject areas

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