Abstract
Information, as well as its qualifiers, or meta-information, forms the basis of human decision-making. Human behavior models (HBMs) therefore require the development of representations of both information and meta-information. However, while existing models and modeling approaches may include computational technologies that support meta-information analysis, they generally neglect its role in human reasoning. Herein, we describe the application of Bayesian belief networks to model how humans calculate, aggregate, and reason about meta-information when making decisions.
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