@inproceedings{6f39c3bb613a44d5ade93eaacf2b3837,
title = "Initializing agent-based models with clustering archetypes",
abstract = "Agent-based models are a powerful tool for predicting population level behaviors; however their performance can be sensitive to the initial simulation conditions. This paper introduces a procedure for leveraging large datasets to initialize agent-based simulations in which the population is abstracted into a set of archetypes. We show that these archetypes can be discovered using clustering and evaluate the benefits of selecting clusters based on their stability over time. Our experiments on the GitHub dataset demonstrate that simulation runs performed with the clustering archetypes are more successful at predicting large-scale activity patterns.",
keywords = "Agent-based models, GitHub archetypes, Stable clustering, Unsupervised learning",
author = "Samaneh Saadat and Chathika Gunaratne and Nisha Baral and Gita Sukthankar and Ivan Garibay",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018 ; Conference date: 10-07-2018 Through 13-07-2018",
year = "2018",
doi = "10.1007/978-3-319-93372-6_27",
language = "English",
isbn = "9783319933719",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "233--239",
editor = "Halil Bisgin and Robert Thomson and Ayaz Hyder and Christopher Dancy",
booktitle = "Social, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings",
}