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Title: A Calibration Model for Bot-Like Behaviors in Agent-Based Anagram Game Simulation
Experiments that are games played among a network of players are widely used to study human behavior. Furthermore, bots or intelligent systems can be used in these games to produce contexts that elicit particular types of human responses. Bot behaviors could be specified solely based on experimental data. In this work, we take a different perspective, called the Probability Calibration (PC) approach, to simulate networked group anagram games with certain players having bot-like behaviors. The proposed method starts with data-driven models and calibrates in principled ways the parameters that alter player behaviors. It can alter the performance of each type of agent (e.g., bot) in group anagram games. Further, statistical methods are used to test whether the PC models produce results that are statistically different from those of the original models. Case studies demonstrate the merits of the proposed method.  more » « less
Award ID(s):
1916670
NSF-PAR ID:
10478903
Author(s) / Creator(s):
; ; ;
Editor(s):
Corlu, C. G.; Hunter, S. R.; Lam, H.; Onggo, B. S.; Shortle, J.; Biller, B.
Publisher / Repository:
IEEE
Date Published:
Journal Name:
Proceedings Winter Simulation Conference
ISSN:
0891-7736
Format(s):
Medium: X
Location:
San Antonio, Texas
Sponsoring Org:
National Science Foundation
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