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Title: Approaches to Uncertainty Quantification in Building Models of Human Behavior
In a group anagram game, players are provided letters to form as many words as possible. They can also request letters from their neighbors and reply to letter requests. Currently, a single agent-based model is produced from all experimental data, with dependence only on number of neighbors. In this work, we build, exercise, and evaluate enhanced agent behavior models for networked group anagram games under an uncertainty quantification framework. Specifically, we cluster game data for players based on their skill levels (forming words, requesting letters, and replying to requests), perform multinomial logistic regression for transition probabilities, and quantify uncertainty within each cluster. The result of this process is a model where players are assigned different numbers of neighbors and different skill levels in the game. We conduct simulations of ego agents with neighbors to demonstrate the efficacy of our proposed methods.
Authors:
; ;
Award ID(s):
1916670
Publication Date:
NSF-PAR ID:
10310244
Journal Name:
Winter Simulation Conference
Sponsoring Org:
National Science Foundation
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