Abhijin Adiga, Gizem Korkmaz, Chris Kuhlman, Madhav Marathe, Naren Ramakrishnan, Parang Saraf, Nathan Self
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.
Yihui Ren , Vanessa Cedeno-Mieles , Zhihao Hu, Xinwei Deng, Abhijin Adiga, Christopher L. Barrett, Saliya Ekanayake, Brian J. Goode, Gizem Korkmaz, Chris J. Kuhlman, Dustin Machi, Madhav V. Marathe, Naren Ramakrishnan, S. S. Ravi, Parang Saraf, Nathan Self, Noshir Contractor, Joshua M. Epstein, Michael W. Macy: Generative Modeling of Human Behavior and Social Interactions Using Abductive Analysis. ASONAM2018: 413-420
- Date of publication:
- October 25, 2018
- IEEE/ACM Advances in Social Networks Analysis and Mining (ASONAM)
- Page number(s):