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The Necessity and Challenges of Automatic Causal Map Processing: A Network Science Perspective

Freund, Alexander J
http://orcid.org/0000-0002-9791-9245
http://rave.ohiolink.edu/etdc/view?acc_num=miami1619545359648916

Abstract Details

2021, Master of Computer Science, Miami University, Computer Science and Software Engineering.
Causal maps use directed network structures to represent causality between concepts in a system, and are vital for conceptual modeling- a core activity in the field of Modeling & Simulation (M&S). Simulation models are generated from collections of maps, introducing scalability challenges as modelers are unable to effectively process large collections manually, or when maps contain many concepts. Despite this, there is a paucity of research on reducing human interventions across the various steps in causal mapping. In this thesis, we develop Network Science tools to overcome these challenges and present a framework for processing maps automatically. First, we demonstrate how the accepted practice of manually transforming evidence into maps introduces significant bias and that indirect elicitation must be fully documented. To further reduce the risk of bias from modelers, we present and evaluate a method to combine maps using semantic and causal information. We then develop a systematic, data-driven approach to extract a useful model from combined maps, in part by characterizing whether recently proposed metrics on identifying central concepts are feasible in large maps. Our approach is validated through studies on suicide modeling and can subsequently be used to process causal maps in many other research areas.
Philippe Giabbanelli, PhD (Advisor)
Karen Davis, PhD (Committee Member)
Vaskar Raychoudhury, PhD (Committee Member)
103 p.
Computer Science
Causal Maps; Network Science; Conceptual Modeling; Clustering; Graph Algorithms; Node Centrality

Recommended Citations

Citations

  • Freund, A. J. (2021). The Necessity and Challenges of Automatic Causal Map Processing: A Network Science Perspective [Master's thesis, Miami University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=miami1619545359648916

    APA Style (7th edition)

  • Freund, Alexander. The Necessity and Challenges of Automatic Causal Map Processing: A Network Science Perspective. 2021. Miami University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=miami1619545359648916.

    MLA Style (8th edition)

  • Freund, Alexander. "The Necessity and Challenges of Automatic Causal Map Processing: A Network Science Perspective." Master's thesis, Miami University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=miami1619545359648916

    Chicago Manual of Style (17th edition)

miami1619545359648916
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© 2021, some rights reserved.Creative Commons License The Necessity and Challenges of Automatic Causal Map Processing: A Network Science Perspective by Alexander J Freund is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Miami University and OhioLINK.