Vanessa Cedeno-Mieles
Papers
1
Total Citations
19
H-Index
1
About
Vanessa Cedeno-Mieles is a computational social scientist whose research lies at the intersection of generative modeling, human behavior, and social interaction dynamics. Her most-cited work, "Generative Modeling of Human Behavior and Social Interactions Using Abductive Analysis" (2018, 19 citations), introduces a novel methodological framework that applies abductive inference—traditionally used in fields like robotics and genetics—to the study of human behavior. By iteratively refining plausible explanations for observed social phenomena, she bridges the gap between data-driven models and theoretical understanding, enabling more realistic simulations of collective human actions. This contribution is foundational for researchers modeling complex social systems, from crowd dynamics to online networks. Cedeno-Mieles’ work demonstrates how computational techniques can uncover the underlying mechanisms driving social behavior, offering powerful tools for predicting and interpreting human interactions. Her interdisciplinary approach, combining computer science, sociology, and statistics, positions her as a key innovator in the emerging field of computational social science, with implications for public health, urban planning, and beyond.
Research Focus
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Top Papers
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