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Oral Defense of Doctoral Dissertation: Carmen Arleth Iasiello
Apr 20, 2021, 9:30 - 11:30 AM
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Meeting ID: 994 3379 8372
Passcode: 050180
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Social Science
Department of Computational and Data Sciences
College of Science
George Mason University
Carmen Arleth Iasiello
Bachelor of Arts, American University, 2001
Master of Arts, Columbia University, 2003
An Agent-Based Modeling Approach for Human Resource Management
Tuesday, April 20, 2021
9:30 to 11:30 a.m.
Committee
Andrew Crooks, Chair
Robert Axtell
William Kennedy
Sarah Wittman
Abstract: Computational social science methods and specifically agent-based modelling have increasingly been used within applied social science fields. A significant contributor to this trend has been the availability of fine-grained data about individual and social behavior. While data availability may aid this process, the true power of computational social science arises when data and theory are combined. Theories derived from different social science traditions vary in their development, testing methods, and interpretation of data. The applications of computational methods have largely excluded explicit consideration of what is gained or lost in the translation of theory derived from epistemic traditions that differ from that in computational social science. This dissertation addresses this gap in three ways. First, it defines a framework that may be used in the process of applying inductively-derived, qualitatively-developed theories. Second, it applies this framework by exploring management science theory. Third, it presents an agent-based model informed by a management science theory and validates it based on human resource management data. This application is presented as a replicable example of both epistemological translation and the computational methods applied.