Searching for explanations: testing social scientific methods in synthetic ground-truthed worlds

Aurora C. Schmidt (), Christopher J. Cameron (), Corey Lowman (), Joshua Brulé (), Amruta J. Deshpande (), Seyyed A. Fatemi (), Vladimir Barash (), Ariel M. Greenberg (), Cash J. Costello (), Eli S. Sherman (), Rohit Bhattacharya (), Liz McQuillan (), Alexander Perrone (), Yanni A. Kouskoulas (), Clay Fink (), June Zhang (), Ilya Shpitser () and Michael W. Macy ()
Additional contact information
Aurora C. Schmidt: Johns Hopkins University Applied Physics Laboratory (JHU/APL)
Christopher J. Cameron: Cornell University
Corey Lowman: Johns Hopkins University Applied Physics Laboratory (JHU/APL)
Joshua Brulé: Johns Hopkins University Applied Physics Laboratory (JHU/APL)
Amruta J. Deshpande: Graphika, Inc.
Seyyed A. Fatemi: University of Hawaii
Vladimir Barash: Graphika, Inc.
Ariel M. Greenberg: Johns Hopkins University Applied Physics Laboratory (JHU/APL)
Cash J. Costello: Johns Hopkins University Applied Physics Laboratory (JHU/APL)
Eli S. Sherman: Johns Hopkins University
Rohit Bhattacharya: Johns Hopkins University
Liz McQuillan: Graphika, Inc.
Alexander Perrone: Johns Hopkins University Applied Physics Laboratory (JHU/APL)
Yanni A. Kouskoulas: Johns Hopkins University Applied Physics Laboratory (JHU/APL)
Clay Fink: Johns Hopkins University Applied Physics Laboratory (JHU/APL)
June Zhang: University of Hawaii
Ilya Shpitser: Johns Hopkins University
Michael W. Macy: Cornell University

Computational and Mathematical Organization Theory, 2023, vol. 29, issue 1, No 6, 156-187

Abstract: Abstract A scientific model’s usefulness relies on its ability to explain phenomena, predict how such phenomena will be impacted by future interventions, and prescribe actions to achieve desired outcomes. We study methods for learning causal models that explain the behaviors of simulated “human” populations. Through the Ground Truth project, we solved a series of Challenges where our explanations, predictions and prescriptions were scored against ground truth information. We describe the processes that emerged for applying causal discovery, network analysis, agent-based modeling and other analytical methods to inform solutions to Challenge tasks. We present our team’s overall performance results on these Challenges and discuss implications for future efforts to validate social scientific research using simulation-based challenges.

Keywords: Causal discovery; Network analysis; Social networks (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10588-021-09353-w

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