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Title: Versatile Uncertainty Quantification of Contrastive Behaviors for Modeling Networked Anagram Games
In a networked anagram game, each team member is given a set of letters and members collectively form as many words as possible. They can share letters through a communication network in assisting their neighbors in forming words. There is variability in behaviors of players, e.g., there can be large differences in numbers of letter requests, of replies to letter requests, and of words formed among players. Therefore, it is of great importance to understand uncertainty and variability in player behaviors. In this work, we propose versatile uncertainty quantification (VUQ) of behaviors for modeling the networked anagram game. Specifically, the proposed methods focus on building contrastive models of game player behaviors that quantify player actions in terms of worst, average, and best performance. Moreover, we construct agent-based models and perform agent-based simulations using these VUQ methods to evaluate the model building methodology and understand the impact of uncertainty. We believe that this approach is applicable to other networked games.  more » « less
Award ID(s):
1916670
NSF-PAR ID:
10310240
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Complex Networks and their Applications
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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