TY - GEN
T1 - Generative modeling of human behavior and social interactions using abductive analysis
AU - Ren, Yihui
AU - Cedeno-Mieles, Vanessa
AU - Hu, Zhihao
AU - Deng, Xinwei
AU - Adiga, Abhijin
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 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/24
Y1 - 2018/10/24
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.
UR - http://www.scopus.com/inward/record.url?scp=85057325049&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057325049&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2018.8508282
DO - 10.1109/ASONAM.2018.8508282
M3 - Conference contribution
AN - SCOPUS:85057325049
T3 - Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
SP - 413
EP - 420
BT - Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
A2 - Tagarelli, Andrea
A2 - Reddy, Chandan
A2 - Brandes, Ulrik
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
Y2 - 28 August 2018 through 31 August 2018
ER -