A Framework of Digital Twins for Modeling Human-Subject Word Formation Experiments

Agent-based models (ABMs) are used to simulate human-subject experiments. A comprehensive understanding of these human systems often requires executing large numbers of simulations, but these requirements are constrained by computational and other resources. In this work, we build a framework of dig...

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Bibliographic Details
Published inProceedings - Winter Simulation Conference pp. 218 - 229
Main Authors He, Hao, Liu, Xueying, Kuhlman, Chris J., Deng, Xinwei
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.12.2024
Subjects
Analysis of variance
Behavioral sciences
Computational modeling
Data models
Digital twins
Games
Process control
Uncertainty
Virtual assistants
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Summary:Agent-based models (ABMs) are used to simulate human-subject experiments. A comprehensive understanding of these human systems often requires executing large numbers of simulations, but these requirements are constrained by computational and other resources. In this work, we build a framework of digital twins for modeling human-subject experiments. The framework has three modules: ABMs of player behaviors built from game data; extensions of these models to represent virtual assistants (agents that are exogenously manipulated to create controlled environments for human agents); and an uncertainty quantification module composed of functional ANOVA and a Gaussian process-based emulator. The emulator is built from the extended ABM; we focus on emulator validation. By incorporating experimental data and agent-based simulation data, our proposed framework enhances the virtual representation of the dynamics in human-subject word formation experiments, which we consider a digital twin. Networked anagram experiments are used as an exemplar to demonstrate the methods.
ISSN:1558-4305
DOI:10.1109/WSC63780.2024.10838941